ReviewBriefings in bioinformatics2016
Advances in computational approaches for prioritizing driver mutations and significantly mutated genes in cancer genomes.
Review in Briefings in bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 71 papers.
What it found
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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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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.
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Who cites it
71 citing papers in PubMed.
- A Novel Cancer Driver Genes Identification Method Based on Self-Supervised Dual Masked Graph Autoencoder.Interdisciplinary sciences, computational life sciences · 2026Article
- GRAFT: a graph-aware fusion transformer for cancer driver gene prediction.Briefings in bioinformatics · 2026Article
- Microbial-Derived Anti-Cancer Compounds: Advances in Drug Discovery, Bioengineering, and Therapeutic Applications.Anti-cancer agents in medicinal chemistry · 2026Review
- A pan-cancer analysis of the oncogenic role of KDELR2 in human cancers.Discover oncology · 2025Article
- MLGCN-Driver: a cancer driver gene identification method based on multi-layer graph convolutional neural network.BMC bioinformatics · 2025Article
- Integrating gene mutation spectra from tumors and the general population with gene expression topological networks to identify novel cancer driver genes.Human genetics · 2025Article
- MNMO: discover driver genes from a multi-omics data based-multi-layer network.Bioinformatics (Oxford, England) · 2025Article
- An ensemble machine learning-based performance evaluation identifies top In-Silico pathogenicity prediction methods that best classify driver mutations in cancer.BioData mining · 2025Article
- MODIG: An Attention Mechanism-Based Approach to Cancer Driver Gene Identification.Methods in molecular biology (Clifton, N.J.) · 2025Article
- IMI-driver: Integrating multi-level gene networks and multi-omics for cancer driver gene identification.PLoS computational biology · 2024Article
- Network analysis of driver genes in human cancers.Frontiers in bioinformatics · 2024Article
- Unraveling the Drivers of Tumorigenesis in the Context of Evolution: Theoretical Models and Bioinformatics Tools.Journal of molecular evolution · 2023Review
- Computational Methods Summarizing Mutational Patterns in Cancer: Promise and Limitations for Clinical Applications.Cancers · 2023Review
- Article
- DGMP: Identifying Cancer Driver Genes by Jointing DGCN and MLP from Multi-omics Genomic Data.Genomics, proteomics & bioinformatics · 2022Article
- DriverRWH: discovering cancer driver genes by random walk on a gene mutation hypergraph.BMC bioinformatics · 2022Article
- Article
- Network Control Models With Personalized Genomics Data for Understanding Tumor Heterogeneity in Cancer.Frontiers in oncology · 2022Review
- Drug Discovery in Spinal Cord Injury With Ankylosing Spondylitis Identified by Text Mining and Biomedical Databases.Frontiers in genetics · 2022Article
- Constructing a novel prognostic signature of tumor driver genes for breast cancer.American journal of translational research · 2022Article
11 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Cancer is often driven by the accumulation of genetic alterations, including single nucleotide variants, small insertions or deletions, gene fusions, copy-number variations, and large chromosomal rearrangements. Recent advances in next-generation sequencing technologies have helped investigators generate massive amounts of cancer genomic data and catalog somatic mutations in both common and rare cancer types. So far, the somatic mutation landscapes and signatures of >10 major cancer types have been reported; however, pinpointing driver mutations and cancer genes from millions of available cancer somatic mutations remains a monumental challenge. To tackle this important task, many methods and computational tools have been developed during the past several years and, thus, a review of its advances is urgently needed. Here, we first summarize the main features of these methods and tools for whole-exome, whole-genome and whole-transcriptome sequencing data. Then, we discuss major challenges like tumor intra-heterogeneity, tumor sample saturation and functionality of synonymous mutations in cancer, all of which may result in false-positive discoveries. Finally, we highlight new directions in studying regulatory roles of noncoding somatic mutations and quantitatively measuring circulating tumor DNA in cancer. This review may help investigators find an appropriate tool for detecting potential driver or actionable mutations in rapidly emerging precision cancer medicine.
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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.