ArticleNAR genomics and bioinformatics2020
AI-Driver: an ensemble method for identifying driver mutations in personal cancer genomes.
Article in NAR genomics and bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 34 citations in OpenAlex.
- An interpretable molecular framework for predicting cancer driver missense mutations.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Classification of driver and passenger mutations in different cancer types using deep neural networks.Bioinformatics advances · 2026Article
- AI-powered precision medicine: utilizing genetic risk factor optimization to revolutionize healthcare.NAR genomics and bioinformatics · 2025Review
- Germline Whole-Exome Sequencing in Non-Smoker Lung Cancer Patients Reveals Pathogenic Variants in Lung Cancer Driver Genes.Genes, chromosomes & cancer · 2025Article
- Identifying somatic driver mutations in cancer with a language model of the human genome.Computational and structural biotechnology journal · 2025Article
- Prediction of cancer driver genes and mutations: the potential of integrative computational frameworks.Briefings in bioinformatics · 2024Review
- Annotation and evaluation of base editing outcomes in multiple cell types using CRISPRbase.Nucleic acids research · 2023Article
- Integrative analysis of DNA methylomes reveals novel cell-free biomarkers in lung adenocarcinoma.Frontiers in genetics · 2023Article
- PredDSMC: A predictor for driver synonymous mutations in human cancers.Frontiers in genetics · 2023Article
- Identifying cancer driver genes in individual tumours.Computational and structural biotechnology journal · 2023Review
- Shared and divergent contribution of vitamin A and oxytocin to the aetiology of autism spectrum disorder.Computational and structural biotechnology journal · 2023Article
- A New View of Activating Mutations in Cancer.Cancer research · 2022Article
- AlphaFold, Artificial Intelligence (AI), and Allostery.The journal of physical chemistry. B · 2022Review
- Genetic association and single-cell transcriptome analyses reveal distinct features connecting autoimmunity with cancers.iScience · 2022Article
- Integrative analysis prioritised oxytocin-related biomarkers associated with the aetiology of autism spectrum disorder.EBioMedicine · 2022Article
- Comprehensive evaluation of computational methods for predicting cancer driver genes.Briefings in bioinformatics · 2022Article
- OncoVar: an integrated database and analysis platform for oncogenic driver variants in cancers.Nucleic acids research · 2021Article
- Transcriptomic signatures and repurposing drugs for COVID-19 patients: findings of bioinformatics analyses.Computational and structural biotechnology journal · 2021Article
Corrections and comments
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Authors and funding
7 authors at 7 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The current challenge in cancer research is to increase the resolution of driver prediction from gene-level to mutation-level, which is more closely aligned with the goal of precision cancer medicine. Improved methods to distinguish drivers from passengers are urgently needed to dig out driver mutations from increasing exome sequencing studies. Here, we developed an ensemble method, AI-Driver (AI-based driver classifier, https://github.com/hatchetProject/AI-Driver), to predict the driver status of somatic missense mutations based on 23 pathogenicity features. AI-Driver has the best overall performance compared with any individual tool and two cancer-specific driver predicting methods. We demonstrate the superior and stable performance of our model using four independent benchmarks. We provide pre-computed AI-Driver scores for all possible human missense variants (http://aidriver.maolab.org/) to identify driver mutations in the sea of somatic mutations discovered by personal cancer sequencing. We believe that AI-Driver together with pre-computed database will play vital important roles in the human cancer studies, such as identification of driver mutation in personal cancer genomes, discovery of targeting sites for cancer therapeutic treatments and prediction of tumor biomarkers for early diagnosis by liquid biopsy.
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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.