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Appears in Networks 47

TBI_D1_CVBio_SCAI_8-3-2018 - QC v2.3.11

TBI BEL model is a disease model based on Biological Expression Language (BEL) containing the core mechanisms of Traumatic Brain Injury (TBI). This version contains knowledge extracted from 306 pubmed articles. This work is from the collaboration between CohenVeterans Bioscience and Fraunhofer SCAI.

TBI_D1_CVBio_SCAI_8-3-2018 - QC v2.3.14

TBI BEL model is a disease model based on Biological Expression Language (BEL) containing the core mechanisms of Traumatic Brain Injury (TBI). This version contains knowledge extracted from 306 pubmed articles. This work is from the collaboration between CohenVeterans Bioscience and Fraunhofer SCAI.

TBI_D1_CVBio_SCAI_8-3-2018 - QC v2.3.15

TBI BEL model is a disease model based on Biological Expression Language (BEL) containing the core mechanisms of Traumatic Brain Injury (TBI). This version contains knowledge extracted from 306 pubmed articles. This work is from the collaboration between CohenVeterans Bioscience and Fraunhofer SCAI.

TBI_D1_CVBio_SCAI_8-3-2018 - QC v2.3.16

TBI BEL model is a disease model based on Biological Expression Language (BEL) containing the core mechanisms of Traumatic Brain Injury (TBI). This version contains knowledge extracted from 306 pubmed articles. This work is from the collaboration between CohenVeterans Bioscience and Fraunhofer SCAI.

TBI_D1_CVBio_SCAI_8-3-2018 - QC v2.3.18

TBI BEL model is a disease model based on Biological Expression Language (BEL) containing the core mechanisms of Traumatic Brain Injury (TBI). This version contains knowledge extracted from 306 pubmed articles. This work is from the collaboration between CohenVeterans Bioscience and Fraunhofer SCAI.

TBI_D1_CVBio_SCAI_8-3-2018 - QC v2.3.19

TBI BEL model is a disease model based on Biological Expression Language (BEL) containing the core mechanisms of Traumatic Brain Injury (TBI). This version contains knowledge extracted from 306 pubmed articles. This work is from the collaboration between CohenVeterans Bioscience and Fraunhofer SCAI.

TBI_D1_CVBio_SCAI_8-3-2018 - QC v2.3.20

TBI BEL model is a disease model based on Biological Expression Language (BEL) containing the core mechanisms of Traumatic Brain Injury (TBI). This version contains knowledge extracted from 306 pubmed articles. This work is from the collaboration between CohenVeterans Bioscience and Fraunhofer SCAI.

TBI_D1_CVBio_SCAI_8-3-2018 - QC v2.3.21

TBI BEL model is a disease model based on Biological Expression Language (BEL) containing the core mechanisms of Traumatic Brain Injury (TBI). This version contains knowledge extracted from 306 pubmed articles. This work is from the collaboration between CohenVeterans Bioscience and Fraunhofer SCAI.

TBI_D1_CVBio_SCAI_8-3-2018 - QC v2.3.22

TBI BEL model is a disease model based on Biological Expression Language (BEL) containing the core mechanisms of Traumatic Brain Injury (TBI). This version contains knowledge extracted from 306 pubmed articles. This work is from the collaboration between CohenVeterans Bioscience and Fraunhofer SCAI.

TBI_D1_CVBio_SCAI_8-3-2018 - QC v2.3.23

TBI BEL model is a disease model based on Biological Expression Language (BEL) containing the core mechanisms of Traumatic Brain Injury (TBI). This version contains knowledge extracted from 306 pubmed articles. This work is from the collaboration between CohenVeterans Bioscience and Fraunhofer SCAI.

TBI_D1_CVBio_SCAI_8-3-2018 - QC v2.3.26

TBI BEL model is a disease model based on Biological Expression Language (BEL) containing the core mechanisms of Traumatic Brain Injury (TBI). This version contains knowledge extracted from 306 pubmed articles. This work is from the collaboration between CohenVeterans Bioscience and Fraunhofer SCAI.

TBI_D1_CVBio_SCAI_8-3-2018 - QC v2.3.27

TBI BEL model is a disease model based on Biological Expression Language (BEL) containing the core mechanisms of Traumatic Brain Injury (TBI). This version contains knowledge extracted from 306 pubmed articles. This work is from the collaboration between CohenVeterans Bioscience and Fraunhofer SCAI.

TBI_D1_CVBio_SCAI_8-3-2018 - QC v2.3.28

TBI BEL model is a disease model based on Biological Expression Language (BEL) containing the core mechanisms of Traumatic Brain Injury (TBI). This version contains knowledge extracted from 306 pubmed articles. This work is from the collaboration between CohenVeterans Bioscience and Fraunhofer SCAI.

TBI_D1_CVBio_SCAI_13-04-2018 v2.3.28

TBI BEL model is a disease model based on Biological Expression Language (BEL) containing the core mechanisms of Traumatic Brain Injury (TBI). This version contains knowledge extracted from 306 pubmed articles. This work is from the collaboration between CohenVeterans Bioscience and Fraunhofer SCAI.

TBI_D1_CVBio_SCAI_19-04-2018 v2.3.30

TBI BEL model is a disease model based on Biological Expression Language (BEL) containing the core mechanisms of Traumatic Brain Injury (TBI). This version contains knowledge extracted from 306 pubmed articles. This work is from the collaboration between CohenVeterans Bioscience and Fraunhofer SCAI.

TBI_D1_CVBio_SCAI_22-04-2018 v2.3.31

TBI BEL model is a disease model based on Biological Expression Language (BEL) containing the core mechanisms of Traumatic Brain Injury (TBI). This version contains knowledge extracted from 306 pubmed articles. This work is from the collaboration between CohenVeterans Bioscience and Fraunhofer SCAI.

TBI_D1_CVBio_SCAI_22-04-2018 v2.3.32

TBI BEL model is a disease model based on Biological Expression Language (BEL) containing the core mechanisms of Traumatic Brain Injury (TBI). This version contains knowledge extracted from 306 pubmed articles. This work is from the collaboration between CohenVeterans Bioscience and Fraunhofer SCAI.

TBI_D1_CVBio_SCAI_25-04-2018 v2.3.33

TBI BEL model is a disease model based on Biological Expression Language (BEL) containing the core mechanisms of Traumatic Brain Injury (TBI). This version contains knowledge extracted from 306 pubmed articles. This work is from the collaboration between CohenVeterans Bioscience and Fraunhofer SCAI.

PTSD BEL Model Versions 1 and 2 - Re-Reviewed v1.0.0

PTSD BEL model is a disease model based on Biological Expression Language (BEL) containing the core mechanisms of Post Traumatic Stress Disorder (PTSD). This version contains knowledge extracted from 348 pubmed articles and 2 articles from other sources like Book. This work is from the collaboration between CohenVeterans Bioscience and Fraunhofer SCAI

PTSD and TBI BEL Model v1.0.1

PTSD BEL model is a disease model based on Biological Expression Language (BEL) containing the core mechanisms of Post Traumatic Stress Disorder (PTSD). This version contains knowledge extracted from 348 pubmed articles and 2 articles from other sources like Book. This work is from the collaboration between CohenVeterans Bioscience and Fraunhofer SCAI

PTSD and TBI BEL Model v1.0.8

PTSD BEL model is a disease model based on Biological Expression Language (BEL) containing the core mechanisms of Post Traumatic Stress Disorder (PTSD). This version contains knowledge extracted from 348 pubmed articles and 2 articles from other sources like Book. This work is from the collaboration between CohenVeterans Bioscience and Fraunhofer SCAI

PTSD and TBI BEL Model v1.0.9

PTSD BEL model is a disease model based on Biological Expression Language (BEL) containing the core mechanisms of Post Traumatic Stress Disorder (PTSD). This version contains knowledge extracted from 348 pubmed articles and 2 articles from other sources like Book. This work is from the collaboration between CohenVeterans Bioscience and Fraunhofer SCAI

PTSD and TBI BEL Model v1.0.10

PTSD BEL model is a disease model based on Biological Expression Language (BEL) containing the core mechanisms of Post Traumatic Stress Disorder (PTSD). This version contains knowledge extracted from 348 pubmed articles and 2 articles from other sources like Book. This work is from the collaboration between CohenVeterans Bioscience and Fraunhofer SCAI

PTSD and TBI BEL Model v1.0.11

PTSD BEL model is a disease model based on Biological Expression Language (BEL) containing the core mechanisms of Post Traumatic Stress Disorder (PTSD). This version contains knowledge extracted from 348 pubmed articles and 2 articles from other sources like Book. This work is from the collaboration between CohenVeterans Bioscience and Fraunhofer SCAI

PTSD and TBI BEL Model v1.0.12

PTSD BEL model is a disease model based on Biological Expression Language (BEL) containing the core mechanisms of Post Traumatic Stress Disorder (PTSD). This version contains knowledge extracted from 348 pubmed articles and 2 articles from other sources like Book. This work is from the collaboration between CohenVeterans Bioscience and Fraunhofer SCAI

PTSD and TBI BEL Model v1.0.15

PTSD BEL model is a disease model based on Biological Expression Language (BEL) containing the core mechanisms of Post Traumatic Stress Disorder (PTSD). This version contains knowledge extracted from 348 pubmed articles and 2 articles from other sources like Book. This work is from the collaboration between CohenVeterans Bioscience and Fraunhofer SCAI

PTSD and TBI BEL Model v1.0.16

PTSD BEL model is a disease model based on Biological Expression Language (BEL) containing the core mechanisms of Post Traumatic Stress Disorder (PTSD). This version contains knowledge extracted from 348 pubmed articles and 2 articles from other sources like Book. This work is from the collaboration between CohenVeterans Bioscience and Fraunhofer SCAI

PTSD and TBI BEL Model v1.0.17

PTSD BEL model is a disease model based on Biological Expression Language (BEL) containing the core mechanisms of Post Traumatic Stress Disorder (PTSD). This version contains knowledge extracted from 348 pubmed articles and 2 articles from other sources like Book. This work is from the collaboration between CohenVeterans Bioscience and Fraunhofer SCAI

PTSD and TBI BEL Model v1.0.20

PTSD BEL model is a disease model based on Biological Expression Language (BEL) containing the core mechanisms of Post Traumatic Stress Disorder (PTSD). This version contains knowledge extracted from 348 pubmed articles and 2 articles from other sources like Book. This work is from the collaboration between CohenVeterans Bioscience and Fraunhofer SCAI

Colorectal Cancer Knowledge Assembly - Drugs Pathways and General Biology v1.1.0

Colorectal Cancer Knowledge Assembly with drug associations and Pathways + general CRC biology

PTSD and TBI BEL Model v1.0.22

PTSD BEL model is a disease model based on Biological Expression Language (BEL) containing the core mechanisms of Post Traumatic Stress Disorder (PTSD). This version contains knowledge extracted from 348 pubmed articles and 2 articles from other sources like Book. This work is from the collaboration between CohenVeterans Bioscience and Fraunhofer SCAI

PTSD and TBI BEL Model v1.0.23

PTSD BEL model is a disease model based on Biological Expression Language (BEL) containing the core mechanisms of Post Traumatic Stress Disorder (PTSD). This version contains knowledge extracted from 348 pubmed articles and 2 articles from other sources like Book. This work is from the collaboration between CohenVeterans Bioscience and Fraunhofer SCAI

PTSD and TBI BEL Model v1.0.24

PTSD BEL model is a disease model based on Biological Expression Language (BEL) containing the core mechanisms of Post Traumatic Stress Disorder (PTSD). This version contains knowledge extracted from 348 pubmed articles and 2 articles from other sources like Book. This work is from the collaboration between CohenVeterans Bioscience and Fraunhofer SCAI

PTSD and TBI BEL Model v1.0.25

PTSD BEL model is a disease model based on Biological Expression Language (BEL) containing the core mechanisms of Post Traumatic Stress Disorder (PTSD). This version contains knowledge extracted from 348 pubmed articles and 2 articles from other sources like Book. This work is from the collaboration between CohenVeterans Bioscience and Fraunhofer SCAI

TBI BEL Model v1.0.27

TBI BEL model is a disease model based on Biological Expression Language (BEL) containing the core mechanisms of Traumatic Brain Injury. This version contains knowledge extracted from 523 pubmed articles. This work is from the collaboration between CohenVeterans Bioscience and Fraunhofer SCAI

TBRD - PTSD and TBI BEL Model v1.0.30

TBRD BEL model is a disease model based on Biological Expression Language (BEL) containing the core mechanisms of Post Traumatic Stress Disorder (PTSD) and Traumatic Brain Injury (TBI). This version contains knowledge extracted from 847 pubmed articles and 2 articles from other sources like Book. Out of these,523 articles belongs 323 PTSD articles. This work is from the collaboration between CohenVeterans Bioscience and Fraunhofer SCAI

Colorectal Cancer Knowledge Assembly - Drugs Pathways v1.0.0

Colorectal Cancer Knowledge Assembly with drug associations and Pathways.

colorectal cancer Knowledge Assembly - Drugs v1.0.1

colorectal cancer Knowledge Assembly with drug associations.

colorectal cancer Drugs resistance v1.0.1.0

colorectal cancer Knowledge Assembly with drug associations.

CRC_combined v1.1

BEL Document

Colorectal Cancer Model v2.0.0

Colorectal Cancer Model

Colorectal Cancer Model v0.0.0

Colorectal Cancer Model

Colorectal Cancer Model v2.0.3

Colorectal Cancer Model

Colorectal Cancer Model v2.0.4

Colorectal Cancer Model, uploaded by reagon

Colorectal Cancer Model v2.0.5

Colorectal Cancer Model, uploaded by reagon

Colorectal Cancer Model v2.0.6

Colorectal Cancer Model, uploaded by reagon

In-Edges 37

bp(PTS:"Cerebrospinal fluid _CSF_ pathway") association bp(GOBP:transport)

The extent to which these findings depend on release of NSE from injured neurons or lysed erythrocytes is currently unknown, so studies in which NSE concentrations are compared with free haemoglobin levels are needed. PubMed:27632903

Annotations
Confidence
Very High
NCBI Taxonomy Names
Homo sapiens

bp(PTS:"Cerebrospinal fluid _CSF_ pathway") association bp(GOBP:transport)

bp(PTS:"Cerebrospinal fluid _CSF_ pathway") association bp(GOBP:transport)

The extent to which these findings depend on release of NSE from injured neurons or lysed erythrocytes is currently unknown, so studies in which NSE concentrations are compared with free haemoglobin levels are needed. PubMed:27632903

g(HGNC:ABCB11) association bp(GOBP:transport)

Model selection resulted in the inclusion of 14 SNPs from eight genes (six transporter genes, ABCA9, ABCB11, ABCC10, ATP1A1, ATP1B2, ATP8B3, and two metabolism genes GSTM5, GRHPR), which significantly improved model fit. Using bootstrap analysis we show an improvement of the prediction error of 3.7% in patients treated with oxaliplatin. Several variants in genes involved in metabolism and transport could thus be potential predictive markers for oxaliplatin treatment in CRC patients. PubMed:26835885

g(HGNC:ATP8B3) association bp(GOBP:transport)

Model selection resulted in the inclusion of 14 SNPs from eight genes (six transporter genes, ABCA9, ABCB11, ABCC10, ATP1A1, ATP1B2, ATP8B3, and two metabolism genes GSTM5, GRHPR), which significantly improved model fit. Using bootstrap analysis we show an improvement of the prediction error of 3.7% in patients treated with oxaliplatin. Several variants in genes involved in metabolism and transport could thus be potential predictive markers for oxaliplatin treatment in CRC patients. PubMed:26835885

g(HGNC:ABCA9) association bp(GOBP:transport)

Model selection resulted in the inclusion of 14 SNPs from eight genes (six transporter genes, ABCA9, ABCB11, ABCC10, ATP1A1, ATP1B2, ATP8B3, and two metabolism genes GSTM5, GRHPR), which significantly improved model fit. Using bootstrap analysis we show an improvement of the prediction error of 3.7% in patients treated with oxaliplatin. Several variants in genes involved in metabolism and transport could thus be potential predictive markers for oxaliplatin treatment in CRC patients. PubMed:26835885

g(HGNC:ATP1A1) association bp(GOBP:transport)

Model selection resulted in the inclusion of 14 SNPs from eight genes (six transporter genes, ABCA9, ABCB11, ABCC10, ATP1A1, ATP1B2, ATP8B3, and two metabolism genes GSTM5, GRHPR), which significantly improved model fit. Using bootstrap analysis we show an improvement of the prediction error of 3.7% in patients treated with oxaliplatin. Several variants in genes involved in metabolism and transport could thus be potential predictive markers for oxaliplatin treatment in CRC patients. PubMed:26835885

g(HGNC:ABCC10) association bp(GOBP:transport)

Model selection resulted in the inclusion of 14 SNPs from eight genes (six transporter genes, ABCA9, ABCB11, ABCC10, ATP1A1, ATP1B2, ATP8B3, and two metabolism genes GSTM5, GRHPR), which significantly improved model fit. Using bootstrap analysis we show an improvement of the prediction error of 3.7% in patients treated with oxaliplatin. Several variants in genes involved in metabolism and transport could thus be potential predictive markers for oxaliplatin treatment in CRC patients. PubMed:26835885

g(HGNC:ATP1B2) association bp(GOBP:transport)

Model selection resulted in the inclusion of 14 SNPs from eight genes (six transporter genes, ABCA9, ABCB11, ABCC10, ATP1A1, ATP1B2, ATP8B3, and two metabolism genes GSTM5, GRHPR), which significantly improved model fit. Using bootstrap analysis we show an improvement of the prediction error of 3.7% in patients treated with oxaliplatin. Several variants in genes involved in metabolism and transport could thus be potential predictive markers for oxaliplatin treatment in CRC patients. PubMed:26835885

p(HGNC:HEXA, pmod(Glyco)) association bp(GOBP:transport)

We have identified the sites of N-linked glycosylation and oligosaccharide phosphorylation on the alpha-subunit of beta-hexosaminidase and have determined the influence of the oligosaccharides on the folding and transport of the enzyme. PubMed:1533633

Annotations
NCBI Taxonomy Ids
9606
Experimental Factor Ontology (EFO)
COS-1 cell
Disease Ontology (DO)
colorectal cancer
MeSH
Tay-Sachs Disease

g(HGNC:ABCB11) association bp(GOBP:transport)

Model selection resulted in the inclusion of 14 SNPs from eight genes (six transporter genes, ABCA9, ABCB11, ABCC10, ATP1A1, ATP1B2, ATP8B3, and two metabolism genes GSTM5, GRHPR), which significantly improved model fit. Using bootstrap analysis we show an improvement of the prediction error of 3.7% in patients treated with oxaliplatin. Several variants in genes involved in metabolism and transport could thus be potential predictive markers for oxaliplatin treatment in CRC patients. PubMed:26835885

Annotations
MeSH
Microtubules
Experimental Factor Ontology (EFO)
HCT-15 cell
MeSH
Colorectal Neoplasms
Evidence and Conclusion Ontology
immunohistochemistry
MeSH
Allografts
NCBI Taxonomy Names
Mus musculus
Disease Ontology (DO)
colorectal cancer
NCBI Taxonomy Ids
10117

p(HGNC:HEXA, pmod(Glyco)) association bp(GOBP:transport)

We have identified the sites of N-linked glycosylation and oligosaccharide phosphorylation on the alpha-subunit of beta-hexosaminidase and have determined the influence of the oligosaccharides on the folding and transport of the enzyme. PubMed:1533633

Annotations
MeSH
Microtubules
Experimental Factor Ontology (EFO)
COS-1 cell
MeSH
Tay-Sachs Disease
Evidence and Conclusion Ontology
immunohistochemistry
MeSH
Plasma
NCBI Taxonomy Names
Mus musculus
Disease Ontology (DO)
colorectal cancer
NCBI Taxonomy Ids
9606

g(HGNC:ATP8B3) association bp(GOBP:transport)

Model selection resulted in the inclusion of 14 SNPs from eight genes (six transporter genes, ABCA9, ABCB11, ABCC10, ATP1A1, ATP1B2, ATP8B3, and two metabolism genes GSTM5, GRHPR), which significantly improved model fit. Using bootstrap analysis we show an improvement of the prediction error of 3.7% in patients treated with oxaliplatin. Several variants in genes involved in metabolism and transport could thus be potential predictive markers for oxaliplatin treatment in CRC patients. PubMed:26835885

Annotations
MeSH
Microtubules
Experimental Factor Ontology (EFO)
HCT-15 cell
MeSH
Colorectal Neoplasms
Evidence and Conclusion Ontology
immunohistochemistry
MeSH
Allografts
NCBI Taxonomy Names
Mus musculus
Disease Ontology (DO)
colorectal cancer
NCBI Taxonomy Ids
10117

g(HGNC:ABCA9) association bp(GOBP:transport)

Model selection resulted in the inclusion of 14 SNPs from eight genes (six transporter genes, ABCA9, ABCB11, ABCC10, ATP1A1, ATP1B2, ATP8B3, and two metabolism genes GSTM5, GRHPR), which significantly improved model fit. Using bootstrap analysis we show an improvement of the prediction error of 3.7% in patients treated with oxaliplatin. Several variants in genes involved in metabolism and transport could thus be potential predictive markers for oxaliplatin treatment in CRC patients. PubMed:26835885

Annotations
MeSH
Microtubules
Experimental Factor Ontology (EFO)
HCT-15 cell
MeSH
Colorectal Neoplasms
Evidence and Conclusion Ontology
immunohistochemistry
MeSH
Allografts
NCBI Taxonomy Names
Mus musculus
Disease Ontology (DO)
colorectal cancer
NCBI Taxonomy Ids
10117

g(HGNC:ABCC10) association bp(GOBP:transport)

Model selection resulted in the inclusion of 14 SNPs from eight genes (six transporter genes, ABCA9, ABCB11, ABCC10, ATP1A1, ATP1B2, ATP8B3, and two metabolism genes GSTM5, GRHPR), which significantly improved model fit. Using bootstrap analysis we show an improvement of the prediction error of 3.7% in patients treated with oxaliplatin. Several variants in genes involved in metabolism and transport could thus be potential predictive markers for oxaliplatin treatment in CRC patients. PubMed:26835885

Annotations
MeSH
Microtubules
Experimental Factor Ontology (EFO)
HCT-15 cell
MeSH
Colorectal Neoplasms
Evidence and Conclusion Ontology
immunohistochemistry
MeSH
Allografts
NCBI Taxonomy Names
Mus musculus
Disease Ontology (DO)
colorectal cancer
NCBI Taxonomy Ids
10117

g(HGNC:ATP1A1) association bp(GOBP:transport)

Model selection resulted in the inclusion of 14 SNPs from eight genes (six transporter genes, ABCA9, ABCB11, ABCC10, ATP1A1, ATP1B2, ATP8B3, and two metabolism genes GSTM5, GRHPR), which significantly improved model fit. Using bootstrap analysis we show an improvement of the prediction error of 3.7% in patients treated with oxaliplatin. Several variants in genes involved in metabolism and transport could thus be potential predictive markers for oxaliplatin treatment in CRC patients. PubMed:26835885

Annotations
MeSH
Microtubules
Experimental Factor Ontology (EFO)
HCT-15 cell
MeSH
Colorectal Neoplasms
Evidence and Conclusion Ontology
immunohistochemistry
MeSH
Allografts
NCBI Taxonomy Names
Mus musculus
Disease Ontology (DO)
colorectal cancer
NCBI Taxonomy Ids
10117

g(HGNC:ATP1B2) association bp(GOBP:transport)

Model selection resulted in the inclusion of 14 SNPs from eight genes (six transporter genes, ABCA9, ABCB11, ABCC10, ATP1A1, ATP1B2, ATP8B3, and two metabolism genes GSTM5, GRHPR), which significantly improved model fit. Using bootstrap analysis we show an improvement of the prediction error of 3.7% in patients treated with oxaliplatin. Several variants in genes involved in metabolism and transport could thus be potential predictive markers for oxaliplatin treatment in CRC patients. PubMed:26835885

Annotations
MeSH
Microtubules
Experimental Factor Ontology (EFO)
HCT-15 cell
MeSH
Colorectal Neoplasms
Evidence and Conclusion Ontology
immunohistochemistry
MeSH
Allografts
NCBI Taxonomy Names
Mus musculus
Disease Ontology (DO)
colorectal cancer
NCBI Taxonomy Ids
10117

p(HGNC:HEXA, pmod(Glyco)) association bp(GOBP:transport)

We have identified the sites of N-linked glycosylation and oligosaccharide phosphorylation on the alpha-subunit of beta-hexosaminidase and have determined the influence of the oligosaccharides on the folding and transport of the enzyme. PubMed:1533633

g(HGNC:ABCA9) association bp(GOBP:transport)

Model selection resulted in the inclusion of 14 SNPs from eight genes (six transporter genes, ABCA9, ABCB11, ABCC10, ATP1A1, ATP1B2, ATP8B3, and two metabolism genes GSTM5, GRHPR), which significantly improved model fit. Using bootstrap analysis we show an improvement of the prediction error of 3.7% in patients treated with oxaliplatin. Several variants in genes involved in metabolism and transport could thus be potential predictive markers for oxaliplatin treatment in CRC patients. PubMed:26835885

Annotations
Experimental Factor Ontology (EFO)
HCT-15 cell
MeSH
Intracellular Space
MeSH
Condylomata Acuminata
MeSH
Mucus
NCBI Taxonomy Names
Atractylodes macrocephala
NCBI Taxonomy Names
Panax ginseng
NCBI Taxonomy Names
Astragalus membranaceus
NCBI Taxonomy Names
Glycyrrhiza uralensis
NCBI Taxonomy Names
Poria cocos
NCBI Taxonomy Names
Coix lacryma-jobi
Disease Ontology (DO)
colorectal cancer
Uberon
intestinal epithelium
Cell Ontology (CL)
stem cell
NCBI Taxonomy Ids
9606

g(HGNC:ATP1B2) association bp(GOBP:transport)

Model selection resulted in the inclusion of 14 SNPs from eight genes (six transporter genes, ABCA9, ABCB11, ABCC10, ATP1A1, ATP1B2, ATP8B3, and two metabolism genes GSTM5, GRHPR), which significantly improved model fit. Using bootstrap analysis we show an improvement of the prediction error of 3.7% in patients treated with oxaliplatin. Several variants in genes involved in metabolism and transport could thus be potential predictive markers for oxaliplatin treatment in CRC patients. PubMed:26835885

Annotations
Experimental Factor Ontology (EFO)
HCT-15 cell
MeSH
Intracellular Space
MeSH
Condylomata Acuminata
MeSH
Mucus
NCBI Taxonomy Names
Atractylodes macrocephala
NCBI Taxonomy Names
Panax ginseng
NCBI Taxonomy Names
Astragalus membranaceus
NCBI Taxonomy Names
Glycyrrhiza uralensis
NCBI Taxonomy Names
Poria cocos
NCBI Taxonomy Names
Coix lacryma-jobi
Disease Ontology (DO)
colorectal cancer
Uberon
intestinal epithelium
Cell Ontology (CL)
stem cell
NCBI Taxonomy Ids
9606

g(HGNC:ABCB11) association bp(GOBP:transport)

Model selection resulted in the inclusion of 14 SNPs from eight genes (six transporter genes, ABCA9, ABCB11, ABCC10, ATP1A1, ATP1B2, ATP8B3, and two metabolism genes GSTM5, GRHPR), which significantly improved model fit. Using bootstrap analysis we show an improvement of the prediction error of 3.7% in patients treated with oxaliplatin. Several variants in genes involved in metabolism and transport could thus be potential predictive markers for oxaliplatin treatment in CRC patients. PubMed:26835885

Annotations
Experimental Factor Ontology (EFO)
HCT-15 cell
MeSH
Intracellular Space
MeSH
Condylomata Acuminata
MeSH
Mucus
NCBI Taxonomy Names
Atractylodes macrocephala
NCBI Taxonomy Names
Panax ginseng
NCBI Taxonomy Names
Astragalus membranaceus
NCBI Taxonomy Names
Glycyrrhiza uralensis
NCBI Taxonomy Names
Poria cocos
NCBI Taxonomy Names
Coix lacryma-jobi
Disease Ontology (DO)
colorectal cancer
Uberon
intestinal epithelium
Cell Ontology (CL)
stem cell
NCBI Taxonomy Ids
9606

g(HGNC:ATP8B3) association bp(GOBP:transport)

Model selection resulted in the inclusion of 14 SNPs from eight genes (six transporter genes, ABCA9, ABCB11, ABCC10, ATP1A1, ATP1B2, ATP8B3, and two metabolism genes GSTM5, GRHPR), which significantly improved model fit. Using bootstrap analysis we show an improvement of the prediction error of 3.7% in patients treated with oxaliplatin. Several variants in genes involved in metabolism and transport could thus be potential predictive markers for oxaliplatin treatment in CRC patients. PubMed:26835885

Annotations
Experimental Factor Ontology (EFO)
HCT-15 cell
MeSH
Intracellular Space
MeSH
Condylomata Acuminata
MeSH
Mucus
NCBI Taxonomy Names
Atractylodes macrocephala
NCBI Taxonomy Names
Panax ginseng
NCBI Taxonomy Names
Astragalus membranaceus
NCBI Taxonomy Names
Glycyrrhiza uralensis
NCBI Taxonomy Names
Poria cocos
NCBI Taxonomy Names
Coix lacryma-jobi
Disease Ontology (DO)
colorectal cancer
Uberon
intestinal epithelium
Cell Ontology (CL)
stem cell
NCBI Taxonomy Ids
9606

g(HGNC:ATP1A1) association bp(GOBP:transport)

Model selection resulted in the inclusion of 14 SNPs from eight genes (six transporter genes, ABCA9, ABCB11, ABCC10, ATP1A1, ATP1B2, ATP8B3, and two metabolism genes GSTM5, GRHPR), which significantly improved model fit. Using bootstrap analysis we show an improvement of the prediction error of 3.7% in patients treated with oxaliplatin. Several variants in genes involved in metabolism and transport could thus be potential predictive markers for oxaliplatin treatment in CRC patients. PubMed:26835885

Annotations
Experimental Factor Ontology (EFO)
HCT-15 cell
MeSH
Intracellular Space
MeSH
Condylomata Acuminata
MeSH
Mucus
NCBI Taxonomy Names
Atractylodes macrocephala
NCBI Taxonomy Names
Panax ginseng
NCBI Taxonomy Names
Astragalus membranaceus
NCBI Taxonomy Names
Glycyrrhiza uralensis
NCBI Taxonomy Names
Poria cocos
NCBI Taxonomy Names
Coix lacryma-jobi
Disease Ontology (DO)
colorectal cancer
Uberon
intestinal epithelium
Cell Ontology (CL)
stem cell
NCBI Taxonomy Ids
9606

p(HGNC:HEXA, pmod(Glyco)) association bp(GOBP:transport)

We have identified the sites of N-linked glycosylation and oligosaccharide phosphorylation on the alpha-subunit of beta-hexosaminidase and have determined the influence of the oligosaccharides on the folding and transport of the enzyme. PubMed:1533633

Annotations
Experimental Factor Ontology (EFO)
COS-1 cell
MeSH
Intracellular Space
MeSH
Tay-Sachs Disease
MeSH
Mucus
NCBI Taxonomy Names
Crataegus azarolus
Disease Ontology (DO)
colorectal cancer
Uberon
intestinal epithelium
Cell Ontology (CL)
stem cell
NCBI Taxonomy Ids
9606

g(HGNC:ABCC10) association bp(GOBP:transport)

Model selection resulted in the inclusion of 14 SNPs from eight genes (six transporter genes, ABCA9, ABCB11, ABCC10, ATP1A1, ATP1B2, ATP8B3, and two metabolism genes GSTM5, GRHPR), which significantly improved model fit. Using bootstrap analysis we show an improvement of the prediction error of 3.7% in patients treated with oxaliplatin. Several variants in genes involved in metabolism and transport could thus be potential predictive markers for oxaliplatin treatment in CRC patients. PubMed:26835885

Annotations
Experimental Factor Ontology (EFO)
HCT-15 cell
MeSH
Intracellular Space
MeSH
Condylomata Acuminata
MeSH
Mucus
NCBI Taxonomy Names
Atractylodes macrocephala
NCBI Taxonomy Names
Panax ginseng
NCBI Taxonomy Names
Astragalus membranaceus
NCBI Taxonomy Names
Glycyrrhiza uralensis
NCBI Taxonomy Names
Poria cocos
NCBI Taxonomy Names
Coix lacryma-jobi
Disease Ontology (DO)
colorectal cancer
Uberon
intestinal epithelium
Cell Ontology (CL)
stem cell
NCBI Taxonomy Ids
9606

g(HGNC:ABCA9) association bp(GOBP:transport)

Model selection resulted in the inclusion of 14 SNPs from eight genes (six transporter genes, ABCA9, ABCB11, ABCC10, ATP1A1, ATP1B2, ATP8B3, and two metabolism genes GSTM5, GRHPR), which significantly improved model fit. Using bootstrap analysis we show an improvement of the prediction error of 3.7% in patients treated with oxaliplatin. Several variants in genes involved in metabolism and transport could thus be potential predictive markers for oxaliplatin treatment in CRC patients. PubMed:26835885

g(HGNC:ATP1B2) association bp(GOBP:transport)

Model selection resulted in the inclusion of 14 SNPs from eight genes (six transporter genes, ABCA9, ABCB11, ABCC10, ATP1A1, ATP1B2, ATP8B3, and two metabolism genes GSTM5, GRHPR), which significantly improved model fit. Using bootstrap analysis we show an improvement of the prediction error of 3.7% in patients treated with oxaliplatin. Several variants in genes involved in metabolism and transport could thus be potential predictive markers for oxaliplatin treatment in CRC patients. PubMed:26835885

g(HGNC:ABCB11) association bp(GOBP:transport)

Model selection resulted in the inclusion of 14 SNPs from eight genes (six transporter genes, ABCA9, ABCB11, ABCC10, ATP1A1, ATP1B2, ATP8B3, and two metabolism genes GSTM5, GRHPR), which significantly improved model fit. Using bootstrap analysis we show an improvement of the prediction error of 3.7% in patients treated with oxaliplatin. Several variants in genes involved in metabolism and transport could thus be potential predictive markers for oxaliplatin treatment in CRC patients. PubMed:26835885

g(HGNC:ATP8B3) association bp(GOBP:transport)

Model selection resulted in the inclusion of 14 SNPs from eight genes (six transporter genes, ABCA9, ABCB11, ABCC10, ATP1A1, ATP1B2, ATP8B3, and two metabolism genes GSTM5, GRHPR), which significantly improved model fit. Using bootstrap analysis we show an improvement of the prediction error of 3.7% in patients treated with oxaliplatin. Several variants in genes involved in metabolism and transport could thus be potential predictive markers for oxaliplatin treatment in CRC patients. PubMed:26835885

g(HGNC:ATP1A1) association bp(GOBP:transport)

Model selection resulted in the inclusion of 14 SNPs from eight genes (six transporter genes, ABCA9, ABCB11, ABCC10, ATP1A1, ATP1B2, ATP8B3, and two metabolism genes GSTM5, GRHPR), which significantly improved model fit. Using bootstrap analysis we show an improvement of the prediction error of 3.7% in patients treated with oxaliplatin. Several variants in genes involved in metabolism and transport could thus be potential predictive markers for oxaliplatin treatment in CRC patients. PubMed:26835885

g(HGNC:ABCC10) association bp(GOBP:transport)

Model selection resulted in the inclusion of 14 SNPs from eight genes (six transporter genes, ABCA9, ABCB11, ABCC10, ATP1A1, ATP1B2, ATP8B3, and two metabolism genes GSTM5, GRHPR), which significantly improved model fit. Using bootstrap analysis we show an improvement of the prediction error of 3.7% in patients treated with oxaliplatin. Several variants in genes involved in metabolism and transport could thus be potential predictive markers for oxaliplatin treatment in CRC patients. PubMed:26835885

g(HGNC:ABCA9) association bp(GOBP:transport)

Model selection resulted in the inclusion of 14 SNPs from eight genes (six transporter genes, ABCA9, ABCB11, ABCC10, ATP1A1, ATP1B2, ATP8B3, and two metabolism genes GSTM5, GRHPR), which significantly improved model fit. Using bootstrap analysis we show an improvement of the prediction error of 3.7% in patients treated with oxaliplatin. Several variants in genes involved in metabolism and transport could thus be potential predictive markers for oxaliplatin treatment in CRC patients. PubMed:26835885

Annotations
Disease Ontology (DO)
colorectal cancer

g(HGNC:ATP1B2) association bp(GOBP:transport)

Model selection resulted in the inclusion of 14 SNPs from eight genes (six transporter genes, ABCA9, ABCB11, ABCC10, ATP1A1, ATP1B2, ATP8B3, and two metabolism genes GSTM5, GRHPR), which significantly improved model fit. Using bootstrap analysis we show an improvement of the prediction error of 3.7% in patients treated with oxaliplatin. Several variants in genes involved in metabolism and transport could thus be potential predictive markers for oxaliplatin treatment in CRC patients. PubMed:26835885

Annotations
Disease Ontology (DO)
colorectal cancer

g(HGNC:ABCB11) association bp(GOBP:transport)

Model selection resulted in the inclusion of 14 SNPs from eight genes (six transporter genes, ABCA9, ABCB11, ABCC10, ATP1A1, ATP1B2, ATP8B3, and two metabolism genes GSTM5, GRHPR), which significantly improved model fit. Using bootstrap analysis we show an improvement of the prediction error of 3.7% in patients treated with oxaliplatin. Several variants in genes involved in metabolism and transport could thus be potential predictive markers for oxaliplatin treatment in CRC patients. PubMed:26835885

Annotations
Disease Ontology (DO)
colorectal cancer

g(HGNC:ATP8B3) association bp(GOBP:transport)

Model selection resulted in the inclusion of 14 SNPs from eight genes (six transporter genes, ABCA9, ABCB11, ABCC10, ATP1A1, ATP1B2, ATP8B3, and two metabolism genes GSTM5, GRHPR), which significantly improved model fit. Using bootstrap analysis we show an improvement of the prediction error of 3.7% in patients treated with oxaliplatin. Several variants in genes involved in metabolism and transport could thus be potential predictive markers for oxaliplatin treatment in CRC patients. PubMed:26835885

Annotations
Disease Ontology (DO)
colorectal cancer

g(HGNC:ATP1A1) association bp(GOBP:transport)

Model selection resulted in the inclusion of 14 SNPs from eight genes (six transporter genes, ABCA9, ABCB11, ABCC10, ATP1A1, ATP1B2, ATP8B3, and two metabolism genes GSTM5, GRHPR), which significantly improved model fit. Using bootstrap analysis we show an improvement of the prediction error of 3.7% in patients treated with oxaliplatin. Several variants in genes involved in metabolism and transport could thus be potential predictive markers for oxaliplatin treatment in CRC patients. PubMed:26835885

Annotations
Disease Ontology (DO)
colorectal cancer

g(HGNC:ABCC10) association bp(GOBP:transport)

Model selection resulted in the inclusion of 14 SNPs from eight genes (six transporter genes, ABCA9, ABCB11, ABCC10, ATP1A1, ATP1B2, ATP8B3, and two metabolism genes GSTM5, GRHPR), which significantly improved model fit. Using bootstrap analysis we show an improvement of the prediction error of 3.7% in patients treated with oxaliplatin. Several variants in genes involved in metabolism and transport could thus be potential predictive markers for oxaliplatin treatment in CRC patients. PubMed:26835885

Annotations
Disease Ontology (DO)
colorectal cancer

Out-Edges 37

bp(GOBP:transport) association bp(PTS:"Cerebrospinal fluid _CSF_ pathway")

The extent to which these findings depend on release of NSE from injured neurons or lysed erythrocytes is currently unknown, so studies in which NSE concentrations are compared with free haemoglobin levels are needed. PubMed:27632903

Annotations
Confidence
Very High
NCBI Taxonomy Names
Homo sapiens

bp(GOBP:transport) association bp(PTS:"Cerebrospinal fluid _CSF_ pathway")

bp(GOBP:transport) association bp(PTS:"Cerebrospinal fluid _CSF_ pathway")

The extent to which these findings depend on release of NSE from injured neurons or lysed erythrocytes is currently unknown, so studies in which NSE concentrations are compared with free haemoglobin levels are needed. PubMed:27632903

bp(GOBP:transport) association g(HGNC:ABCC10)

Model selection resulted in the inclusion of 14 SNPs from eight genes (six transporter genes, ABCA9, ABCB11, ABCC10, ATP1A1, ATP1B2, ATP8B3, and two metabolism genes GSTM5, GRHPR), which significantly improved model fit. Using bootstrap analysis we show an improvement of the prediction error of 3.7% in patients treated with oxaliplatin. Several variants in genes involved in metabolism and transport could thus be potential predictive markers for oxaliplatin treatment in CRC patients. PubMed:26835885

bp(GOBP:transport) association g(HGNC:ATP1A1)

Model selection resulted in the inclusion of 14 SNPs from eight genes (six transporter genes, ABCA9, ABCB11, ABCC10, ATP1A1, ATP1B2, ATP8B3, and two metabolism genes GSTM5, GRHPR), which significantly improved model fit. Using bootstrap analysis we show an improvement of the prediction error of 3.7% in patients treated with oxaliplatin. Several variants in genes involved in metabolism and transport could thus be potential predictive markers for oxaliplatin treatment in CRC patients. PubMed:26835885

bp(GOBP:transport) association g(HGNC:ATP8B3)

Model selection resulted in the inclusion of 14 SNPs from eight genes (six transporter genes, ABCA9, ABCB11, ABCC10, ATP1A1, ATP1B2, ATP8B3, and two metabolism genes GSTM5, GRHPR), which significantly improved model fit. Using bootstrap analysis we show an improvement of the prediction error of 3.7% in patients treated with oxaliplatin. Several variants in genes involved in metabolism and transport could thus be potential predictive markers for oxaliplatin treatment in CRC patients. PubMed:26835885

bp(GOBP:transport) association g(HGNC:ATP1B2)

Model selection resulted in the inclusion of 14 SNPs from eight genes (six transporter genes, ABCA9, ABCB11, ABCC10, ATP1A1, ATP1B2, ATP8B3, and two metabolism genes GSTM5, GRHPR), which significantly improved model fit. Using bootstrap analysis we show an improvement of the prediction error of 3.7% in patients treated with oxaliplatin. Several variants in genes involved in metabolism and transport could thus be potential predictive markers for oxaliplatin treatment in CRC patients. PubMed:26835885

bp(GOBP:transport) association g(HGNC:ABCB11)

Model selection resulted in the inclusion of 14 SNPs from eight genes (six transporter genes, ABCA9, ABCB11, ABCC10, ATP1A1, ATP1B2, ATP8B3, and two metabolism genes GSTM5, GRHPR), which significantly improved model fit. Using bootstrap analysis we show an improvement of the prediction error of 3.7% in patients treated with oxaliplatin. Several variants in genes involved in metabolism and transport could thus be potential predictive markers for oxaliplatin treatment in CRC patients. PubMed:26835885

bp(GOBP:transport) association g(HGNC:ABCA9)

Model selection resulted in the inclusion of 14 SNPs from eight genes (six transporter genes, ABCA9, ABCB11, ABCC10, ATP1A1, ATP1B2, ATP8B3, and two metabolism genes GSTM5, GRHPR), which significantly improved model fit. Using bootstrap analysis we show an improvement of the prediction error of 3.7% in patients treated with oxaliplatin. Several variants in genes involved in metabolism and transport could thus be potential predictive markers for oxaliplatin treatment in CRC patients. PubMed:26835885

bp(GOBP:transport) association p(HGNC:HEXA, pmod(Glyco))

We have identified the sites of N-linked glycosylation and oligosaccharide phosphorylation on the alpha-subunit of beta-hexosaminidase and have determined the influence of the oligosaccharides on the folding and transport of the enzyme. PubMed:1533633

Annotations
NCBI Taxonomy Ids
9606
Experimental Factor Ontology (EFO)
COS-1 cell
Disease Ontology (DO)
colorectal cancer
MeSH
Tay-Sachs Disease

bp(GOBP:transport) association g(HGNC:ATP1B2)

Model selection resulted in the inclusion of 14 SNPs from eight genes (six transporter genes, ABCA9, ABCB11, ABCC10, ATP1A1, ATP1B2, ATP8B3, and two metabolism genes GSTM5, GRHPR), which significantly improved model fit. Using bootstrap analysis we show an improvement of the prediction error of 3.7% in patients treated with oxaliplatin. Several variants in genes involved in metabolism and transport could thus be potential predictive markers for oxaliplatin treatment in CRC patients. PubMed:26835885

Annotations
MeSH
Microtubules
Experimental Factor Ontology (EFO)
HCT-15 cell
MeSH
Colorectal Neoplasms
Evidence and Conclusion Ontology
immunohistochemistry
MeSH
Allografts
NCBI Taxonomy Names
Mus musculus
Disease Ontology (DO)
colorectal cancer
NCBI Taxonomy Ids
10117

bp(GOBP:transport) association p(HGNC:HEXA, pmod(Glyco))

We have identified the sites of N-linked glycosylation and oligosaccharide phosphorylation on the alpha-subunit of beta-hexosaminidase and have determined the influence of the oligosaccharides on the folding and transport of the enzyme. PubMed:1533633

Annotations
MeSH
Microtubules
Experimental Factor Ontology (EFO)
COS-1 cell
MeSH
Tay-Sachs Disease
Evidence and Conclusion Ontology
immunohistochemistry
MeSH
Plasma
NCBI Taxonomy Names
Mus musculus
Disease Ontology (DO)
colorectal cancer
NCBI Taxonomy Ids
9606

bp(GOBP:transport) association g(HGNC:ABCB11)

Model selection resulted in the inclusion of 14 SNPs from eight genes (six transporter genes, ABCA9, ABCB11, ABCC10, ATP1A1, ATP1B2, ATP8B3, and two metabolism genes GSTM5, GRHPR), which significantly improved model fit. Using bootstrap analysis we show an improvement of the prediction error of 3.7% in patients treated with oxaliplatin. Several variants in genes involved in metabolism and transport could thus be potential predictive markers for oxaliplatin treatment in CRC patients. PubMed:26835885

Annotations
MeSH
Microtubules
Experimental Factor Ontology (EFO)
HCT-15 cell
MeSH
Colorectal Neoplasms
Evidence and Conclusion Ontology
immunohistochemistry
MeSH
Allografts
NCBI Taxonomy Names
Mus musculus
Disease Ontology (DO)
colorectal cancer
NCBI Taxonomy Ids
10117

bp(GOBP:transport) association g(HGNC:ATP1A1)

Model selection resulted in the inclusion of 14 SNPs from eight genes (six transporter genes, ABCA9, ABCB11, ABCC10, ATP1A1, ATP1B2, ATP8B3, and two metabolism genes GSTM5, GRHPR), which significantly improved model fit. Using bootstrap analysis we show an improvement of the prediction error of 3.7% in patients treated with oxaliplatin. Several variants in genes involved in metabolism and transport could thus be potential predictive markers for oxaliplatin treatment in CRC patients. PubMed:26835885

Annotations
MeSH
Microtubules
Experimental Factor Ontology (EFO)
HCT-15 cell
MeSH
Colorectal Neoplasms
Evidence and Conclusion Ontology
immunohistochemistry
MeSH
Allografts
NCBI Taxonomy Names
Mus musculus
Disease Ontology (DO)
colorectal cancer
NCBI Taxonomy Ids
10117

bp(GOBP:transport) association g(HGNC:ABCA9)

Model selection resulted in the inclusion of 14 SNPs from eight genes (six transporter genes, ABCA9, ABCB11, ABCC10, ATP1A1, ATP1B2, ATP8B3, and two metabolism genes GSTM5, GRHPR), which significantly improved model fit. Using bootstrap analysis we show an improvement of the prediction error of 3.7% in patients treated with oxaliplatin. Several variants in genes involved in metabolism and transport could thus be potential predictive markers for oxaliplatin treatment in CRC patients. PubMed:26835885

Annotations
MeSH
Microtubules
Experimental Factor Ontology (EFO)
HCT-15 cell
MeSH
Colorectal Neoplasms
Evidence and Conclusion Ontology
immunohistochemistry
MeSH
Allografts
NCBI Taxonomy Names
Mus musculus
Disease Ontology (DO)
colorectal cancer
NCBI Taxonomy Ids
10117

bp(GOBP:transport) association g(HGNC:ABCC10)

Model selection resulted in the inclusion of 14 SNPs from eight genes (six transporter genes, ABCA9, ABCB11, ABCC10, ATP1A1, ATP1B2, ATP8B3, and two metabolism genes GSTM5, GRHPR), which significantly improved model fit. Using bootstrap analysis we show an improvement of the prediction error of 3.7% in patients treated with oxaliplatin. Several variants in genes involved in metabolism and transport could thus be potential predictive markers for oxaliplatin treatment in CRC patients. PubMed:26835885

Annotations
MeSH
Microtubules
Experimental Factor Ontology (EFO)
HCT-15 cell
MeSH
Colorectal Neoplasms
Evidence and Conclusion Ontology
immunohistochemistry
MeSH
Allografts
NCBI Taxonomy Names
Mus musculus
Disease Ontology (DO)
colorectal cancer
NCBI Taxonomy Ids
10117

bp(GOBP:transport) association g(HGNC:ATP8B3)

Model selection resulted in the inclusion of 14 SNPs from eight genes (six transporter genes, ABCA9, ABCB11, ABCC10, ATP1A1, ATP1B2, ATP8B3, and two metabolism genes GSTM5, GRHPR), which significantly improved model fit. Using bootstrap analysis we show an improvement of the prediction error of 3.7% in patients treated with oxaliplatin. Several variants in genes involved in metabolism and transport could thus be potential predictive markers for oxaliplatin treatment in CRC patients. PubMed:26835885

Annotations
MeSH
Microtubules
Experimental Factor Ontology (EFO)
HCT-15 cell
MeSH
Colorectal Neoplasms
Evidence and Conclusion Ontology
immunohistochemistry
MeSH
Allografts
NCBI Taxonomy Names
Mus musculus
Disease Ontology (DO)
colorectal cancer
NCBI Taxonomy Ids
10117

bp(GOBP:transport) association p(HGNC:HEXA, pmod(Glyco))

We have identified the sites of N-linked glycosylation and oligosaccharide phosphorylation on the alpha-subunit of beta-hexosaminidase and have determined the influence of the oligosaccharides on the folding and transport of the enzyme. PubMed:1533633

bp(GOBP:transport) association g(HGNC:ATP1B2)

Model selection resulted in the inclusion of 14 SNPs from eight genes (six transporter genes, ABCA9, ABCB11, ABCC10, ATP1A1, ATP1B2, ATP8B3, and two metabolism genes GSTM5, GRHPR), which significantly improved model fit. Using bootstrap analysis we show an improvement of the prediction error of 3.7% in patients treated with oxaliplatin. Several variants in genes involved in metabolism and transport could thus be potential predictive markers for oxaliplatin treatment in CRC patients. PubMed:26835885

Annotations
Experimental Factor Ontology (EFO)
HCT-15 cell
MeSH
Intracellular Space
MeSH
Condylomata Acuminata
MeSH
Mucus
NCBI Taxonomy Names
Atractylodes macrocephala
NCBI Taxonomy Names
Panax ginseng
NCBI Taxonomy Names
Astragalus membranaceus
NCBI Taxonomy Names
Glycyrrhiza uralensis
NCBI Taxonomy Names
Poria cocos
NCBI Taxonomy Names
Coix lacryma-jobi
Disease Ontology (DO)
colorectal cancer
Uberon
intestinal epithelium
Cell Ontology (CL)
stem cell
NCBI Taxonomy Ids
9606

bp(GOBP:transport) association g(HGNC:ABCB11)

Model selection resulted in the inclusion of 14 SNPs from eight genes (six transporter genes, ABCA9, ABCB11, ABCC10, ATP1A1, ATP1B2, ATP8B3, and two metabolism genes GSTM5, GRHPR), which significantly improved model fit. Using bootstrap analysis we show an improvement of the prediction error of 3.7% in patients treated with oxaliplatin. Several variants in genes involved in metabolism and transport could thus be potential predictive markers for oxaliplatin treatment in CRC patients. PubMed:26835885

Annotations
Experimental Factor Ontology (EFO)
HCT-15 cell
MeSH
Intracellular Space
MeSH
Condylomata Acuminata
MeSH
Mucus
NCBI Taxonomy Names
Atractylodes macrocephala
NCBI Taxonomy Names
Panax ginseng
NCBI Taxonomy Names
Astragalus membranaceus
NCBI Taxonomy Names
Glycyrrhiza uralensis
NCBI Taxonomy Names
Poria cocos
NCBI Taxonomy Names
Coix lacryma-jobi
Disease Ontology (DO)
colorectal cancer
Uberon
intestinal epithelium
Cell Ontology (CL)
stem cell
NCBI Taxonomy Ids
9606

bp(GOBP:transport) association g(HGNC:ATP8B3)

Model selection resulted in the inclusion of 14 SNPs from eight genes (six transporter genes, ABCA9, ABCB11, ABCC10, ATP1A1, ATP1B2, ATP8B3, and two metabolism genes GSTM5, GRHPR), which significantly improved model fit. Using bootstrap analysis we show an improvement of the prediction error of 3.7% in patients treated with oxaliplatin. Several variants in genes involved in metabolism and transport could thus be potential predictive markers for oxaliplatin treatment in CRC patients. PubMed:26835885

Annotations
Experimental Factor Ontology (EFO)
HCT-15 cell
MeSH
Intracellular Space
MeSH
Condylomata Acuminata
MeSH
Mucus
NCBI Taxonomy Names
Atractylodes macrocephala
NCBI Taxonomy Names
Panax ginseng
NCBI Taxonomy Names
Astragalus membranaceus
NCBI Taxonomy Names
Glycyrrhiza uralensis
NCBI Taxonomy Names
Poria cocos
NCBI Taxonomy Names
Coix lacryma-jobi
Disease Ontology (DO)
colorectal cancer
Uberon
intestinal epithelium
Cell Ontology (CL)
stem cell
NCBI Taxonomy Ids
9606

bp(GOBP:transport) association p(HGNC:HEXA, pmod(Glyco))

We have identified the sites of N-linked glycosylation and oligosaccharide phosphorylation on the alpha-subunit of beta-hexosaminidase and have determined the influence of the oligosaccharides on the folding and transport of the enzyme. PubMed:1533633

Annotations
Experimental Factor Ontology (EFO)
COS-1 cell
MeSH
Intracellular Space
MeSH
Tay-Sachs Disease
MeSH
Mucus
NCBI Taxonomy Names
Crataegus azarolus
Disease Ontology (DO)
colorectal cancer
Uberon
intestinal epithelium
Cell Ontology (CL)
stem cell
NCBI Taxonomy Ids
9606

bp(GOBP:transport) association g(HGNC:ATP1A1)

Model selection resulted in the inclusion of 14 SNPs from eight genes (six transporter genes, ABCA9, ABCB11, ABCC10, ATP1A1, ATP1B2, ATP8B3, and two metabolism genes GSTM5, GRHPR), which significantly improved model fit. Using bootstrap analysis we show an improvement of the prediction error of 3.7% in patients treated with oxaliplatin. Several variants in genes involved in metabolism and transport could thus be potential predictive markers for oxaliplatin treatment in CRC patients. PubMed:26835885

Annotations
Experimental Factor Ontology (EFO)
HCT-15 cell
MeSH
Intracellular Space
MeSH
Condylomata Acuminata
MeSH
Mucus
NCBI Taxonomy Names
Atractylodes macrocephala
NCBI Taxonomy Names
Panax ginseng
NCBI Taxonomy Names
Astragalus membranaceus
NCBI Taxonomy Names
Glycyrrhiza uralensis
NCBI Taxonomy Names
Poria cocos
NCBI Taxonomy Names
Coix lacryma-jobi
Disease Ontology (DO)
colorectal cancer
Uberon
intestinal epithelium
Cell Ontology (CL)
stem cell
NCBI Taxonomy Ids
9606

bp(GOBP:transport) association g(HGNC:ABCA9)

Model selection resulted in the inclusion of 14 SNPs from eight genes (six transporter genes, ABCA9, ABCB11, ABCC10, ATP1A1, ATP1B2, ATP8B3, and two metabolism genes GSTM5, GRHPR), which significantly improved model fit. Using bootstrap analysis we show an improvement of the prediction error of 3.7% in patients treated with oxaliplatin. Several variants in genes involved in metabolism and transport could thus be potential predictive markers for oxaliplatin treatment in CRC patients. PubMed:26835885

Annotations
Experimental Factor Ontology (EFO)
HCT-15 cell
MeSH
Intracellular Space
MeSH
Condylomata Acuminata
MeSH
Mucus
NCBI Taxonomy Names
Atractylodes macrocephala
NCBI Taxonomy Names
Panax ginseng
NCBI Taxonomy Names
Astragalus membranaceus
NCBI Taxonomy Names
Glycyrrhiza uralensis
NCBI Taxonomy Names
Poria cocos
NCBI Taxonomy Names
Coix lacryma-jobi
Disease Ontology (DO)
colorectal cancer
Uberon
intestinal epithelium
Cell Ontology (CL)
stem cell
NCBI Taxonomy Ids
9606

bp(GOBP:transport) association g(HGNC:ABCC10)

Model selection resulted in the inclusion of 14 SNPs from eight genes (six transporter genes, ABCA9, ABCB11, ABCC10, ATP1A1, ATP1B2, ATP8B3, and two metabolism genes GSTM5, GRHPR), which significantly improved model fit. Using bootstrap analysis we show an improvement of the prediction error of 3.7% in patients treated with oxaliplatin. Several variants in genes involved in metabolism and transport could thus be potential predictive markers for oxaliplatin treatment in CRC patients. PubMed:26835885

Annotations
Experimental Factor Ontology (EFO)
HCT-15 cell
MeSH
Intracellular Space
MeSH
Condylomata Acuminata
MeSH
Mucus
NCBI Taxonomy Names
Atractylodes macrocephala
NCBI Taxonomy Names
Panax ginseng
NCBI Taxonomy Names
Astragalus membranaceus
NCBI Taxonomy Names
Glycyrrhiza uralensis
NCBI Taxonomy Names
Poria cocos
NCBI Taxonomy Names
Coix lacryma-jobi
Disease Ontology (DO)
colorectal cancer
Uberon
intestinal epithelium
Cell Ontology (CL)
stem cell
NCBI Taxonomy Ids
9606

bp(GOBP:transport) association g(HGNC:ATP1B2)

Model selection resulted in the inclusion of 14 SNPs from eight genes (six transporter genes, ABCA9, ABCB11, ABCC10, ATP1A1, ATP1B2, ATP8B3, and two metabolism genes GSTM5, GRHPR), which significantly improved model fit. Using bootstrap analysis we show an improvement of the prediction error of 3.7% in patients treated with oxaliplatin. Several variants in genes involved in metabolism and transport could thus be potential predictive markers for oxaliplatin treatment in CRC patients. PubMed:26835885

bp(GOBP:transport) association g(HGNC:ABCB11)

Model selection resulted in the inclusion of 14 SNPs from eight genes (six transporter genes, ABCA9, ABCB11, ABCC10, ATP1A1, ATP1B2, ATP8B3, and two metabolism genes GSTM5, GRHPR), which significantly improved model fit. Using bootstrap analysis we show an improvement of the prediction error of 3.7% in patients treated with oxaliplatin. Several variants in genes involved in metabolism and transport could thus be potential predictive markers for oxaliplatin treatment in CRC patients. PubMed:26835885

bp(GOBP:transport) association g(HGNC:ATP8B3)

Model selection resulted in the inclusion of 14 SNPs from eight genes (six transporter genes, ABCA9, ABCB11, ABCC10, ATP1A1, ATP1B2, ATP8B3, and two metabolism genes GSTM5, GRHPR), which significantly improved model fit. Using bootstrap analysis we show an improvement of the prediction error of 3.7% in patients treated with oxaliplatin. Several variants in genes involved in metabolism and transport could thus be potential predictive markers for oxaliplatin treatment in CRC patients. PubMed:26835885

bp(GOBP:transport) association g(HGNC:ATP1A1)

Model selection resulted in the inclusion of 14 SNPs from eight genes (six transporter genes, ABCA9, ABCB11, ABCC10, ATP1A1, ATP1B2, ATP8B3, and two metabolism genes GSTM5, GRHPR), which significantly improved model fit. Using bootstrap analysis we show an improvement of the prediction error of 3.7% in patients treated with oxaliplatin. Several variants in genes involved in metabolism and transport could thus be potential predictive markers for oxaliplatin treatment in CRC patients. PubMed:26835885

bp(GOBP:transport) association g(HGNC:ABCA9)

Model selection resulted in the inclusion of 14 SNPs from eight genes (six transporter genes, ABCA9, ABCB11, ABCC10, ATP1A1, ATP1B2, ATP8B3, and two metabolism genes GSTM5, GRHPR), which significantly improved model fit. Using bootstrap analysis we show an improvement of the prediction error of 3.7% in patients treated with oxaliplatin. Several variants in genes involved in metabolism and transport could thus be potential predictive markers for oxaliplatin treatment in CRC patients. PubMed:26835885

bp(GOBP:transport) association g(HGNC:ABCC10)

Model selection resulted in the inclusion of 14 SNPs from eight genes (six transporter genes, ABCA9, ABCB11, ABCC10, ATP1A1, ATP1B2, ATP8B3, and two metabolism genes GSTM5, GRHPR), which significantly improved model fit. Using bootstrap analysis we show an improvement of the prediction error of 3.7% in patients treated with oxaliplatin. Several variants in genes involved in metabolism and transport could thus be potential predictive markers for oxaliplatin treatment in CRC patients. PubMed:26835885

bp(GOBP:transport) association g(HGNC:ATP1B2)

Model selection resulted in the inclusion of 14 SNPs from eight genes (six transporter genes, ABCA9, ABCB11, ABCC10, ATP1A1, ATP1B2, ATP8B3, and two metabolism genes GSTM5, GRHPR), which significantly improved model fit. Using bootstrap analysis we show an improvement of the prediction error of 3.7% in patients treated with oxaliplatin. Several variants in genes involved in metabolism and transport could thus be potential predictive markers for oxaliplatin treatment in CRC patients. PubMed:26835885

Annotations
Disease Ontology (DO)
colorectal cancer

bp(GOBP:transport) association g(HGNC:ABCB11)

Model selection resulted in the inclusion of 14 SNPs from eight genes (six transporter genes, ABCA9, ABCB11, ABCC10, ATP1A1, ATP1B2, ATP8B3, and two metabolism genes GSTM5, GRHPR), which significantly improved model fit. Using bootstrap analysis we show an improvement of the prediction error of 3.7% in patients treated with oxaliplatin. Several variants in genes involved in metabolism and transport could thus be potential predictive markers for oxaliplatin treatment in CRC patients. PubMed:26835885

Annotations
Disease Ontology (DO)
colorectal cancer

bp(GOBP:transport) association g(HGNC:ATP8B3)

Model selection resulted in the inclusion of 14 SNPs from eight genes (six transporter genes, ABCA9, ABCB11, ABCC10, ATP1A1, ATP1B2, ATP8B3, and two metabolism genes GSTM5, GRHPR), which significantly improved model fit. Using bootstrap analysis we show an improvement of the prediction error of 3.7% in patients treated with oxaliplatin. Several variants in genes involved in metabolism and transport could thus be potential predictive markers for oxaliplatin treatment in CRC patients. PubMed:26835885

Annotations
Disease Ontology (DO)
colorectal cancer

bp(GOBP:transport) association g(HGNC:ATP1A1)

Model selection resulted in the inclusion of 14 SNPs from eight genes (six transporter genes, ABCA9, ABCB11, ABCC10, ATP1A1, ATP1B2, ATP8B3, and two metabolism genes GSTM5, GRHPR), which significantly improved model fit. Using bootstrap analysis we show an improvement of the prediction error of 3.7% in patients treated with oxaliplatin. Several variants in genes involved in metabolism and transport could thus be potential predictive markers for oxaliplatin treatment in CRC patients. PubMed:26835885

Annotations
Disease Ontology (DO)
colorectal cancer

bp(GOBP:transport) association g(HGNC:ABCA9)

Model selection resulted in the inclusion of 14 SNPs from eight genes (six transporter genes, ABCA9, ABCB11, ABCC10, ATP1A1, ATP1B2, ATP8B3, and two metabolism genes GSTM5, GRHPR), which significantly improved model fit. Using bootstrap analysis we show an improvement of the prediction error of 3.7% in patients treated with oxaliplatin. Several variants in genes involved in metabolism and transport could thus be potential predictive markers for oxaliplatin treatment in CRC patients. PubMed:26835885

Annotations
Disease Ontology (DO)
colorectal cancer

bp(GOBP:transport) association g(HGNC:ABCC10)

Model selection resulted in the inclusion of 14 SNPs from eight genes (six transporter genes, ABCA9, ABCB11, ABCC10, ATP1A1, ATP1B2, ATP8B3, and two metabolism genes GSTM5, GRHPR), which significantly improved model fit. Using bootstrap analysis we show an improvement of the prediction error of 3.7% in patients treated with oxaliplatin. Several variants in genes involved in metabolism and transport could thus be potential predictive markers for oxaliplatin treatment in CRC patients. PubMed:26835885

Annotations
Disease Ontology (DO)
colorectal cancer

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BEL Commons is developed and maintained in an academic capacity by Charles Tapley Hoyt and Daniel Domingo-Fernández at the Fraunhofer SCAI Department of Bioinformatics with support from the IMI project, AETIONOMY. It is built on top of the open source project, PyBEL. Please feel free to contact us here to give us feedback or report any issues.