ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2018
MaxMIF: A New Method for Identifying Cancer Driver Genes through Effective Data Integration.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed, 54 citations in OpenAlex.
- MODCAN: driver gene identification based on multi-omics features and differential co-association networks for tumor subtypes.BMC bioinformatics · 2026Article
- Revealing cancer driver genes through integrative transcriptomic and epigenomic analyses with Moonlight.PLoS computational biology · 2025Article
- Identifying somatic driver mutations in cancer with a language model of the human genome.Computational and structural biotechnology journal · 2025Article
- IMI-driver: Integrating multi-level gene networks and multi-omics for cancer driver gene identification.PLoS computational biology · 2024Article
- Exploring gene-patient association to identify personalized cancer driver genes by linear neighborhood propagation.BMC bioinformatics · 2024Article
- Prediction of cancer driver genes and mutations: the potential of integrative computational frameworks.Briefings in bioinformatics · 2024Review
- DeepAlloDriver: a deep learning-based strategy to predict cancer driver mutations.Nucleic acids research · 2023Article
- DriverMP enables improved identification of cancer driver genes.GigaScience · 2022Article
- A new machine learning method for cancer mutation analysis.PLoS computational biology · 2022Article
- High Performance Gold Nanorods@DNA Self-Assembled Drug-Loading System for Cancer Thermo-Chemotherapy in the Second Near-Infrared Optical Window.Pharmaceutics · 2022Article
- The molecular characteristics of gastric cancer patients living in Qinghai-Tibetan Plateau.BMC gastroenterology · 2022Article
- Comprehensive evaluation of computational methods for predicting cancer driver genes.Briefings in bioinformatics · 2022Article
- Identification of driver genes based on gene mutational effects and network centrality.BMC bioinformatics · 2021Article
- Identification of Common Driver Gene Modules and Associations between Cancers through Integrated Network Analysis.Global challenges (Hoboken, NJ) · 2021Article
- A novel method to identify gene interaction patterns.BMC genomics · 2021Article
- GEOlimma: differential expression analysis and feature selection using pre-existing microarray data.BMC bioinformatics · 2021Article
- OncoVar: an integrated database and analysis platform for oncogenic driver variants in cancers.Nucleic acids research · 2021Article
- Genome-wide functional association networks: background, data & state-of-the-art resources.Briefings in bioinformatics · 2020Review
- The Clinical and Molecular Characterization of Gastric Cancer Patients in Qinghai-Tibetan Plateau.Frontiers in oncology · 2020Article
- DriverSubNet: A Novel Algorithm for Identifying Cancer Driver Genes by Subnetwork Enrichment Analysis.Frontiers in genetics · 2020Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
Abstract
Identification of a few cancer driver mutation genes from a much larger number of passenger mutation genes in cancer samples remains a highly challenging task. Here, a novel method for distinguishing the driver genes from the passenger genes by effective integration of somatic mutation data and molecular interaction data using a maximal mutational impact function (MaxMIF) is presented. When evaluated on six somatic mutation datasets of Pan-Cancer and 19 datasets of different cancer types from TCGA, MaxMIF almost always significantly outperforms all the existing state-of-the-art methods in terms of predictive accuracy, sensitivity, and specificity. It recovers about 30% more known cancer genes in 500 top-ranked candidate genes than the best among the other tools evaluated. MaxMIF is also highly robust to data perturbation. Intriguingly, MaxMIF is able to identify potential cancer driver genes, with strong experimental data support. Therefore, MaxMIF can be very useful for identifying or prioritizing cancer driver genes in the increasing number of available cancer genomic data.
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