ArticleBMC bioinformatics2008
Predicting cancer involvement of genes from heterogeneous data.
Article in BMC bioinformatics, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- Predicting links between tumor samples and genes using 2-Layered graph based diffusion approach.BMC bioinformatics · 2019Article
- Interactome Analysis of Microtubule-targeting Agents Reveals Cytotoxicity Bases in Normal Cells.Genomics, proteomics & bioinformatics · 2017Article
- Distinctive Behaviors of Druggable Proteins in Cellular Networks.PLoS computational biology · 2015Article
- Associations of SNPs located at candidate genes to bovine growth traits, prioritized with an interaction networks construction approach.BMC genetics · 2015Article
- Prediction of cancer proteins by integrating protein interaction, domain frequency, and domain interaction data using machine learning algorithms.BioMed research international · 2015Article
- A systematic in silico mining of the mechanistic implications and therapeutic potentials of estrogen receptor (ER)-α in breast cancer.PloS one · 2014Article
- Survey of network-based approaches to research of cardiovascular diseases.BioMed research international · 2014Review
- A transcriptome-proteome integrated network identifies endoplasmic reticulum thiol oxidoreductase (ERp57) as a hub that mediates bone metastasis.Molecular & cellular proteomics : MCP · 2013Article
- EnRICH: Extraction and Ranking using Integration and Criteria Heuristics.BMC systems biology · 2013Article
- Search for signatures in miRNAs associated with cancer.Bioinformation · 2013Article
- Improved classification of lung cancer tumors based on structural and physicochemical properties of proteins using data mining models.PloS one · 2013Article
- Network topology reveals key cardiovascular disease genes.PloS one · 2013Article
- Article
- Predicting disease-related subnetworks for type 1 diabetes using a new network activity score.Omics : a journal of integrative biology · 2012Article
- Finding minimum gene subsets with heuristic breadth-first search algorithm for robust tumor classification.BMC bioinformatics · 2012Article
- Interactogeneous: disease gene prioritization using heterogeneous networks and full topology scores.PloS one · 2012Article
- Classification of lung cancer tumors based on structural and physicochemical properties of proteins by bioinformatics models.PloS one · 2012Article
- Exploiting protein-protein interaction networks for genome-wide disease-gene prioritization.PloS one · 2012Article
- Integrative computational biology for cancer research.Human genetics · 2011Review
- Expression of endoplasmic reticulum stress proteins is a candidate marker of brain metastasis in both ErbB-2+ and ErbB-2- primary breast tumors.The American journal of pathology · 2011Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSystematic approaches for identifying proteins involved in different types of cancer are needed. Experimental techniques such as microarrays are being used to characterize cancer, but validating their results can be a laborious task. Computational approaches are used to prioritize between genes putatively involved in cancer, usually based on further analyzing experimental data.
resultsWe implemented a systematic method using the PIANA software that predicts cancer involvement of genes by integrating heterogeneous datasets. Specifically, we produced lists of genes likely to be involved in cancer by relying on: (i) protein-protein interactions; (ii) differential expression data; and (iii) structural and functional properties of cancer genes. The integrative approach that combines multiple sources of data obtained positive predictive values ranging from 23% (on a list of 811 genes) to 73% (on a list of 22 genes), outperforming the use of any of the data sources alone. We analyze a list of 20 cancer gene predictions, finding that most of them have been recently linked to cancer in literature.
conclusionOur approach to identifying and prioritizing candidate cancer genes can be used to produce lists of genes likely to be involved in cancer. Our results suggest that differential expression studies yielding high numbers of candidate cancer genes can be filtered using protein interaction networks.
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