ArticleBriefings in bioinformatics2022
Comprehensive evaluation of computational methods for predicting cancer driver genes.
Article in Briefings in bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- AI-Driven Drug Discovery: Focus on Targets for Solid Tumors.Pharmaceutics · 2026Review
- UNITe: exploiting conserved links between oncogenesis and homeostasis to identify novel cancer drivers.Frontiers in oncology · 2026Article
- Identifying potential risk genes for clear cell renal cell carcinoma with deep reinforcement learning.Nature communications · 2025Article
- Decoding the functional impact of the cancer genome through protein-protein interactions.Nature reviews. Cancer · 2025Review
- Enhancing Molecular Network-Based Cancer Driver Gene Prediction Using Machine Learning Approaches: Current Challenges and Opportunities.Journal of cellular and molecular medicine · 2025Review
- Article
- CIBRA identifies genomic alterations with a system-wide impact on tumor biology.Bioinformatics (Oxford, England) · 2024Article
- IMI-driver: Integrating multi-level gene networks and multi-omics for cancer driver gene identification.PLoS computational biology · 2024Article
- A feature extraction framework for discovering pan-cancer driver genes based on multi-omics data.Quantitative biology (Beijing, China) · 2024Article
- Essentiality, protein-protein interactions and evolutionary properties are key predictors for identifying cancer-associated genes using machine learning.Scientific reports · 2024Article
- Personalized Driver Gene Prediction Using Graph Convolutional Networks with Conditional Random Fields.Biology · 2024Article
- Prediction of cancer driver genes and mutations: the potential of integrative computational frameworks.Briefings in bioinformatics · 2024Review
- A workflow to study mechanistic indicators for driver gene prediction with Moonlight.Briefings in bioinformatics · 2023Article
- High-Throughput Identification, Modeling, and Analysis of Cancer Driver Genes In Vivo.Cold Spring Harbor perspectives in medicine · 2023Review
- Annotation and evaluation of base editing outcomes in multiple cell types using CRISPRbase.Nucleic acids research · 2023Article
- A new machine learning method for cancer mutation analysis.PLoS computational biology · 2022Article
- Construction and contextualization approaches for protein-protein interaction networks.Computational and structural biotechnology journal · 2022Review
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Optimal methods could effectively improve the accuracy of predicting and identifying candidate driver genes. Various computational methods based on mutational frequency, network and function approaches have been developed to identify mutation driver genes in cancer genomes. However, a comprehensive evaluation of the performance levels of network-, function- and frequency-based methods is lacking. In the present study, we assessed and compared eight performance criteria for eight network-based, one function-based and three frequency-based algorithms using eight benchmark datasets. Under different conditions, the performance of approaches varied in terms of network, measurement and sample size. The frequency-based driverMAPS and network-based HotNet2 methods showed the best overall performance. Network-based algorithms using protein-protein interaction networks outperformed the function- and the frequency-based approaches. Precision, F1 score and Matthews correlation coefficient were low for most approaches. Thus, most of these algorithms require stringent cutoffs to correctly distinguish driver and non-driver genes. We constructed a website named Cancer Driver Catalog (http://159.226.67.237/sun/cancer_driver/), wherein we integrated the gene scores predicted by the foregoing software programs. This resource provides valuable guidance for cancer researchers and clinical oncologists prioritizing cancer driver gene candidates by using an optimal tool.
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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 Socratic graph.