ArticleBioinformatics (Oxford, England)2011
Optimally discriminative subnetwork markers predict response to chemotherapy.
Article in Bioinformatics (Oxford, England), 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed.
- Community cohesion looseness in gene networks reveals individualized drug targets and resistance.Briefings in bioinformatics · 2024Article
- De novo identification of maximally deregulated subnetworks based on multi-omics data with DeRegNet.BMC bioinformatics · 2022Article
- CORAL: Building up QSAR models for the chromosome aberration test.Saudi journal of biological sciences · 2019Article
- Identifying subpathway signatures for individualized anticancer drug response by integrating multi-omics data.Journal of translational medicine · 2019Article
- NoMAS: A Computational Approach to Find Mutated Subnetworks Associated With Survival in Genome-Wide Cancer Studies.Frontiers in genetics · 2019Article
- Article
- Differentially mutated subnetworks discovery.Algorithms for molecular biology : AMB · 2019Article
- Network module identification-A widespread theoretical bias and best practices.Methods (San Diego, Calif.) · 2018Article
- HIT'nDRIVE: patient-specific multidriver gene prioritization for precision oncology.Genome research · 2017Article
- On the performance of de novo pathway enrichment.NPJ systems biology and applications · 2017Article
- HTS-Net: An integrated regulome-interactome approach for establishing network regulation models in high-throughput screenings.PloS one · 2017Article
- Pathway-Based Genomics Prediction using Generalized Elastic Net.PLoS computational biology · 2016Article
- A New Structure-Activity Relationship (SAR) Model for Predicting Drug-Induced Liver Injury, Based on Statistical and Expert-Based Structural Alerts.Frontiers in pharmacology · 2016Article
- Data Requirements for Model-Based Cancer Prognosis Prediction.Cancer informatics · 2015Article
- Integrative analysis of cancer imaging readouts by networks.Molecular oncology · 2015Review
- SPECTRA: An Integrated Knowledge Base for Comparing Tissue and Tumor-Specific PPI Networks in Human.Frontiers in bioengineering and biotechnology · 2015Article
- Functional module search in protein networks based on semantic similarity improves the analysis of proteomics data.Molecular & cellular proteomics : MCP · 2014Article
- Clinical proteomic biomarkers: relevant issues on study design & technical considerations in biomarker development.Clinical and translational medicine · 2014Article
- Assessment of subnetwork detection methods for breast cancer.Cancer informatics · 2014Article
- RRHGE: a novel approach to classify the estrogen receptor based breast cancer subtypes.TheScientificWorldJournal · 2014Article
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Authors and funding
6 authors.
Funding
Abstract
motivationMolecular profiles of tumour samples have been widely and successfully used for classification problems. A number of algorithms have been proposed to predict classes of tumor samples based on expression profiles with relatively high performance. However, prediction of response to cancer treatment has proved to be more challenging and novel approaches with improved generalizability are still highly needed. Recent studies have clearly demonstrated the advantages of integrating protein-protein interaction (PPI) data with gene expression profiles for the development of subnetwork markers in classification problems.
resultsWe describe a novel network-based classification algorithm (OptDis) using color coding technique to identify optimally discriminative subnetwork markers. Focusing on PPI networks, we apply our algorithm to drug response studies: we evaluate our algorithm using published cohorts of breast cancer patients treated with combination chemotherapy. We show that our OptDis method improves over previously published subnetwork methods and provides better and more stable performance compared with other subnetwork and single gene methods. We also show that our subnetwork method produces predictive markers that are more reproducible across independent cohorts and offer valuable insight into biological processes underlying response to therapy. AVAILABILITY: The implementation is available at: http://www.cs.sfu.ca/~pdao/personal/OptDis.html CONTACT: cenk@cs.sfu.ca; alapuk@prostatecentre.com; ccollins@prostatecentre.com.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.