ReviewWiley interdisciplinary reviews. Systems biology and medicine2017
Computational approaches for the identification of cancer genes and pathways.
Review in Wiley interdisciplinary reviews. Systems biology and medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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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.
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Who cites it
32 citing papers in PubMed.
- Review
- Identifying key genes in cancer networks using persistent homology.Scientific reports · 2025Article
- Article
- Identification of cancer driver genes based on hierarchical weak consensus model.Health information science and systems · 2024Article
- Network propagation for GWAS analysis: a practical guide to leveraging molecular networks for disease gene discovery.Briefings in bioinformatics · 2024Review
- Cancer-keeper genes as therapeutic targets.iScience · 2023Article
- NetMix2: A Principled Network Propagation Algorithm for Identifying Altered Subnetworks.Journal of computational biology : a journal of computational molecular cell biology · 2022Article
- Multi-omics peripheral and core regions of cancer.NPJ systems biology and applications · 2022Article
- A new machine learning method for cancer mutation analysis.PLoS computational biology · 2022Article
- Extracting functional insights from loss-of-function screens using deep link prediction.Cell reports methods · 2022Article
- gcMECM: graph clustering of mutual exclusivity of cancer mutations.BMC bioinformatics · 2021Article
- A computational approach for the discovery of significant cancer genes by weighted mutation and asymmetric spreading strength in networks.Scientific reports · 2021Article
- NetMix: A Network-Structured Mixture Model for Reduced-Bias Estimation of Altered Subnetworks.Journal of computational biology : a journal of computational molecular cell biology · 2021Article
- Ranking cancer drivers via betweenness-based outlier detection and random walks.BMC bioinformatics · 2021Article
- Predicting therapeutic drugs for hepatocellular carcinoma based on tissue-specific pathways.PLoS computational biology · 2021Article
- A new precision medicine initiative at the dawn of exascale computing.Signal transduction and targeted therapy · 2021Review
- Computational methods for cancer driver discovery: A survey.Theranostics · 2021Review
- A Network-Centric Framework for the Evaluation of Mutual Exclusivity Tests on Cancer Drivers.Frontiers in genetics · 2021Article
- MicroRNA-based regulation of Aurora A kinase in breast cancer.Oncotarget · 2020Article
- Robust Sampling of Defective Pathways in Alzheimer's Disease. Implications in Drug Repositioning.International journal of molecular sciences · 2020Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
High-throughput DNA sequencing techniques enable large-scale measurement of somatic mutations in tumors. Cancer genomics research aims at identifying all cancer-related genes and solid interpretation of their contribution to cancer initiation and development. However, this venture is characterized by various challenges, such as the high number of neutral passenger mutations and the complexity of the biological networks affected by driver mutations. Based on biological pathway and network information, sophisticated computational methods have been developed to facilitate the detection of cancer driver mutations and pathways. They can be categorized into (1) methods using known pathways from public databases, (2) network-based methods, and (3) methods learning cancer pathways de novo. Methods in the first two categories use and integrate different types of data, such as biological pathways, protein interaction networks, and gene expression measurements. The third category consists of de novo methods that detect combinatorial patterns of somatic mutations across tumor samples, such as mutual exclusivity and co-occurrence. In this review, we discuss recent advances, current limitations, and future challenges of these approaches for detecting cancer genes and pathways. We also discuss the most important current resources of cancer-related genes. WIREs Syst Biol Med 2017, 9:e1364. doi: 10.1002/wsbm.1364 For further resources related to this article, please visit the WIREs website.
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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.