ArticlePLoS computational biology2019
LOTUS: A single- and multitask machine learning algorithm for the prediction of cancer driver genes.
Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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26 citing papers in PubMed, 58 citations in OpenAlex.
- A Novel Cancer Driver Genes Identification Method Based on Self-Supervised Dual Masked Graph Autoencoder.Interdisciplinary sciences, computational life sciences · 2026Article
- ONCOPLEX: an oncology-inspired hypergraph model integrating diverse biological knowledge for cancer driver gene prediction.Scientific reports · 2026Article
- Article
- Integrating Multi-Source Directed Gene Networks and Multi-Omics Data to Identify Cancer Driver Genes Based on Graph Neural Networks.International journal of molecular sciences · 2025Article
- MLGCN-Driver: a cancer driver gene identification method based on multi-layer graph convolutional neural network.BMC bioinformatics · 2025Article
- Enhancing Molecular Network-Based Cancer Driver Gene Prediction Using Machine Learning Approaches: Current Challenges and Opportunities.Journal of cellular and molecular medicine · 2025Review
- Towards simplified graph neural networks for identifying cancer driver genes in heterophilic networks.Briefings in bioinformatics · 2024Article
- SSCI: Self-Supervised Deep Learning Improves Network Structure for Cancer Driver Gene Identification.International journal of molecular sciences · 2024Article
- Multiview representation learning for identification of novel cancer genes and their causative biological mechanisms.Briefings in bioinformatics · 2024Article
- CGMega: explainable graph neural network framework with attention mechanisms for cancer gene module dissection.Nature communications · 2024Article
- A feature extraction framework for discovering pan-cancer driver genes based on multi-omics data.Quantitative biology (Beijing, China) · 2024Article
- Integrated image and location analysis for wound classification: a deep learning approach.Scientific reports · 2024Article
- Prediction of cancer driver genes and mutations: the potential of integrative computational frameworks.Briefings in bioinformatics · 2024Review
- SMG: self-supervised masked graph learning for cancer gene identification.Briefings in bioinformatics · 2023Article
- Unraveling the Drivers of Tumorigenesis in the Context of Evolution: Theoretical Models and Bioinformatics Tools.Journal of molecular evolution · 2023Review
- BDL-SP: A Bio-inspired DL model for the identification of altered Signaling Pathways in Multiple Myeloma using WES data.American journal of cancer research · 2023Article
- DriverGenePathway: Identifying driver genes and driver pathways in cancer based on MutSigCV and statistical methods.Computational and structural biotechnology journal · 2023Article
- EPIMUTESTR: a nearest neighbor machine learning approach to predict cancer driver genes from the evolutionary action of coding variants.Nucleic acids research · 2022Article
- driveR: a novel method for prioritizing cancer driver genes using somatic genomics data.BMC bioinformatics · 2021Article
- Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions.Medicinal research reviews · 2021Review
Corrections and comments
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Authors and funding
3 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cancer driver genes, i.e., oncogenes and tumor suppressor genes, are involved in the acquisition of important functions in tumors, providing a selective growth advantage, allowing uncontrolled proliferation and avoiding apoptosis. It is therefore important to identify these driver genes, both for the fundamental understanding of cancer and to help finding new therapeutic targets or biomarkers. Although the most frequently mutated driver genes have been identified, it is believed that many more remain to be discovered, particularly for driver genes specific to some cancer types. In this paper, we propose a new computational method called LOTUS to predict new driver genes. LOTUS is a machine-learning based approach which allows to integrate various types of data in a versatile manner, including information about gene mutations and protein-protein interactions. In addition, LOTUS can predict cancer driver genes in a pan-cancer setting as well as for specific cancer types, using a multitask learning strategy to share information across cancer types. We empirically show that LOTUS outperforms five other state-of-the-art driver gene prediction methods, both in terms of intrinsic consistency and prediction accuracy, and provide predictions of new cancer genes across many cancer types.
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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.