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Assessment of subnetwork detection methods for breast cancer.

Jiang B, Gribskov M - Cancer Inform (2014)

Bottom Line: Here, we compare the results of eight methods: simulated annealing-based jActiveModules, greedy search-based jActiveModules, DEGAS, BioNet, NetBox, ClustEx, OptDis, and NetWalker.While the number of genes/proteins and protein interactions detected by the eight methods vary widely, a core set of 60 genes and 50 interactions was found to be shared by the subnetworks identified by five or more of the methods.Within the core set, 12 genes were found to be known breast cancer genes.

View Article: PubMed Central - PubMed

Affiliation: Department of Biological Sciences, Purdue University, West Lafayette, IN, USA.

ABSTRACT
Subnetwork detection is often used with differential expression analysis to identify modules or pathways associated with a disease or condition. Many computational methods are available for subnetwork analysis. Here, we compare the results of eight methods: simulated annealing-based jActiveModules, greedy search-based jActiveModules, DEGAS, BioNet, NetBox, ClustEx, OptDis, and NetWalker. These methods represent distinctly different computational strategies and are among the most widely used. Each of these methods was used to analyze gene expression data consisting of paired tumor and normal samples from 50 breast cancer patients. While the number of genes/proteins and protein interactions detected by the eight methods vary widely, a core set of 60 genes and 50 interactions was found to be shared by the subnetworks identified by five or more of the methods. Within the core set, 12 genes were found to be known breast cancer genes.

No MeSH data available.


Related in: MedlinePlus

Prediction of the 462 breast cancer genes by the eight subnetworks. F1 score is defined as 2 × precision × recall/(precision + recall).
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f4-cin-suppl.6-2014-015: Prediction of the 462 breast cancer genes by the eight subnetworks. F1 score is defined as 2 × precision × recall/(precision + recall).

Mentions: We tested whether the detected subnetworks contain putative breast cancer genes. First, we collected 462 breast cancer genes from the KEGG Orthology Based Annotation System (KOBAS) version 2.011 functional enrichment list, which integrates Online Mendelian Inheritance in Man (OMIM),12 KEGG DISEASE,7 Functional Disease Ontology (FunDO),13 Genetic Association Database (GAD),14 and the National Human Genome Research Institute (NHGRI) Genome-Wide Association Studies (GWAS) Catalog15 disease databases. With those 462 genes as ground truth, we calculated the precision and recall of each of the eight subnetworks (Fig. 4) and found that the top subnetworks in identifying the true breast cancer genes are those produced by BioNet, NetWalker, NetBox, and jAM.GR. Surprisingly, these four methods use totally different algorithms for subnetwork detection (see Table 1). And NetWalker displayed its potential for predicting true disease genes, even though its coverage of significantly differentially expressed genes was relatively poor; this may be due to its use of random walks to diffuse information through the whole network without any restriction to shortest paths and greedy search.


Assessment of subnetwork detection methods for breast cancer.

Jiang B, Gribskov M - Cancer Inform (2014)

Prediction of the 462 breast cancer genes by the eight subnetworks. F1 score is defined as 2 × precision × recall/(precision + recall).
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4256043&req=5

f4-cin-suppl.6-2014-015: Prediction of the 462 breast cancer genes by the eight subnetworks. F1 score is defined as 2 × precision × recall/(precision + recall).
Mentions: We tested whether the detected subnetworks contain putative breast cancer genes. First, we collected 462 breast cancer genes from the KEGG Orthology Based Annotation System (KOBAS) version 2.011 functional enrichment list, which integrates Online Mendelian Inheritance in Man (OMIM),12 KEGG DISEASE,7 Functional Disease Ontology (FunDO),13 Genetic Association Database (GAD),14 and the National Human Genome Research Institute (NHGRI) Genome-Wide Association Studies (GWAS) Catalog15 disease databases. With those 462 genes as ground truth, we calculated the precision and recall of each of the eight subnetworks (Fig. 4) and found that the top subnetworks in identifying the true breast cancer genes are those produced by BioNet, NetWalker, NetBox, and jAM.GR. Surprisingly, these four methods use totally different algorithms for subnetwork detection (see Table 1). And NetWalker displayed its potential for predicting true disease genes, even though its coverage of significantly differentially expressed genes was relatively poor; this may be due to its use of random walks to diffuse information through the whole network without any restriction to shortest paths and greedy search.

Bottom Line: Here, we compare the results of eight methods: simulated annealing-based jActiveModules, greedy search-based jActiveModules, DEGAS, BioNet, NetBox, ClustEx, OptDis, and NetWalker.While the number of genes/proteins and protein interactions detected by the eight methods vary widely, a core set of 60 genes and 50 interactions was found to be shared by the subnetworks identified by five or more of the methods.Within the core set, 12 genes were found to be known breast cancer genes.

View Article: PubMed Central - PubMed

Affiliation: Department of Biological Sciences, Purdue University, West Lafayette, IN, USA.

ABSTRACT
Subnetwork detection is often used with differential expression analysis to identify modules or pathways associated with a disease or condition. Many computational methods are available for subnetwork analysis. Here, we compare the results of eight methods: simulated annealing-based jActiveModules, greedy search-based jActiveModules, DEGAS, BioNet, NetBox, ClustEx, OptDis, and NetWalker. These methods represent distinctly different computational strategies and are among the most widely used. Each of these methods was used to analyze gene expression data consisting of paired tumor and normal samples from 50 breast cancer patients. While the number of genes/proteins and protein interactions detected by the eight methods vary widely, a core set of 60 genes and 50 interactions was found to be shared by the subnetworks identified by five or more of the methods. Within the core set, 12 genes were found to be known breast cancer genes.

No MeSH data available.


Related in: MedlinePlus