ArticlePacific Symposium on Biocomputing. Pacific Symposium on Biocomputing2013
Module cover - a new approach to genotype-phenotype studies.
Article in Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed.
- Identifying Drug Sensitivity Subnetworks with NETPHIX.iScience · 2020Article
- Identification of conserved evolutionary trajectories in tumors.Bioinformatics (Oxford, England) · 2020Article
- Network-based approaches elucidate differences within APOBEC and clock-like signatures in breast cancer.Genome medicine · 2020Article
- Combinatorial Detection of Conserved Alteration Patterns for Identifying Cancer Subnetworks.GigaScience · 2019Article
- A Computational Method of Defining Potential Biomarkers based on Differential Sub-Networks.Scientific reports · 2017Article
- Enriching Traditional Protein-protein Interaction Networks with Alternative Conformations of Proteins.Scientific reports · 2017Article
- On the performance of de novo pathway enrichment.NPJ systems biology and applications · 2017Article
- Signal-Oriented Pathway Analyses Reveal a Signaling Complex as a Synthetic Lethal Target for p53 Mutations.Cancer research · 2016Article
- Understanding Genotype-Phenotype Effects in Cancer via Network Approaches.PLoS computational biology · 2016Review
- Quantitative assessment of gene expression network module-validation methods.Scientific reports · 2015Article
- Identifying Driver Genomic Alterations in Cancers by Searching Minimum-Weight, Mutually Exclusive Sets.PLoS computational biology · 2015Article
- MEMCover: integrated analysis of mutual exclusivity and functional network reveals dysregulated pathways across multiple cancer types.Bioinformatics (Oxford, England) · 2015Article
- Dissecting cancer heterogeneity with a probabilistic genotype-phenotype model.Nucleic acids research · 2013Article
- Chapter 5: Network biology approach to complex diseases.PLoS computational biology · 2012Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Uncovering and interpreting phenotype/genotype relationships are among the most challenging open questions in disease studies. Set cover approaches are explicitly designed to provide a representative set for diverse disease cases and thus are valuable in studies of heterogeneous datasets. At the same time pathway-centric methods have emerged as key approaches that significantly empower studies of genotype-phenotype relationships. Combining the utility of set cover techniques with the power of network-centric approaches, we designed a novel approach that extends the concept of set cover to network modules cover. We developed two alternative methods to solve the module cover problem: (i) an integrated method that simultaneously determines network modules and optimizes the coverage of disease cases. (ii) a two-step method where we first determined a candidate set of network modules and subsequently selected modules that provided the best coverage of the disease cases. The integrated method showed superior performance in the context of our application. We demonstrated the utility of the module cover approach for the identification of groups of related genes whose activity is perturbed in a coherent way by specific genomic alterations, allowing the interpretation of the heterogeneity of cancer cases.
Indexed as
Identifiers
23424119PMC3595055What OpenQuestion holds
Registered trials
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.