ArticleBMC bioinformatics2021
Deep learning for cancer type classification and driver gene identification.
Article in BMC bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 2 of them syntheses that pooled it.
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Who cites it
25 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Deep learning approaches for resolving genomic discrepancies in cancer: a systematic review and clinical perspective.Briefings in bioinformatics · 2025Pooled it
- Multimodal deep learning for predicting neoadjuvant treatment outcomes in breast cancer: a systematic review.Biology direct · 2025Pooled it
- Enhancing cancer classification accuracy with a self-attention network using panel capture sequencing data.Briefings in bioinformatics · 2026Article
- AI based multiomics integration for cancer diagnosis and prognosis.Journal, genetic engineering & biotechnology · 2026Article
- Artificial intelligence-enabled multi-scale virtual cell: perspective, challenges, and opportunities.Briefings in bioinformatics · 2026Review
- Unpacking Genomic Biomarkers for Programmed Cell Death Receptor-1 Immunotherapy Success in Non-Small Cell Lung Cancer Using Deep Neural Networks: Quantitative Study.JMIR bioinformatics and biotechnology · 2026Article
- Hybrid Feature Selection-Based Machine Learning and Deep Learning Framework for Biomarker Prediction From RNA-seq Data During Dengue Fever to Severe Dengue Progression.Evolutionary bioinformatics online · 2026Article
- A machine learning framework for predictive interpretation of variants of uncertain significance in hereditary cancer.Frontiers in systems biology · 2026Article
- The impact of deep learning and omics data in transforming precision therapy for brain cancer.Frontiers in bioinformatics · 2026Review
- A novel statistical feature selection framework for biomarker discovery and cancer classification via multiomics integration.BMC medical research methodology · 2025Article
- Explainable deep learning for identifying cancer driver genes based on the Cancer Dependency Map.bioRxiv : the preprint server for biology · 2025Article
- From Data to Cure: A Comprehensive Exploration of Multi-omics Data Analysis for Targeted Therapies.Molecular biotechnology · 2025Review
- Identifying somatic driver mutations in cancer with a language model of the human genome.Computational and structural biotechnology journal · 2025Article
- Northwestern University resource and education development initiatives to advance collaborative artificial intelligence across the learning health system.Learning health systems · 2024Article
- Applications of Machine Learning (ML) and Mathematical Modeling (MM) in Healthcare with Special Focus on Cancer Prognosis and Anticancer Therapy: Current Status and Challenges.Pharmaceutics · 2024Review
- Prediction of cancer driver genes and mutations: the potential of integrative computational frameworks.Briefings in bioinformatics · 2024Review
- ReGeNNe: genetic pathway-based deep neural network using canonical correlation regularizer for disease prediction.Bioinformatics (Oxford, England) · 2023Article
- An overview and a roadmap for artificial intelligence in hematology and oncology.Journal of cancer research and clinical oncology · 2023Review
- Cancer driver mutations: predictions and reality.Trends in molecular medicine · 2023Review
- Targeting tumor heterogeneity: multiplex-detection-based multiple instance learning for whole slide image classification.Bioinformatics (Oxford, England) · 2023Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
backgroundGenetic information is becoming more readily available and is increasingly being used to predict patient cancer types as well as their subtypes. Most classification methods thus far utilize somatic mutations as independent features for classification and are limited by study power. We aim to develop a novel method to effectively explore the landscape of genetic variants, including germline variants, and small insertions and deletions for cancer type prediction.
resultsWe proposed DeepCues, a deep learning model that utilizes convolutional neural networks to unbiasedly derive features from raw cancer DNA sequencing data for disease classification and relevant gene discovery. Using raw whole-exome sequencing as features, germline variants and somatic mutations, including insertions and deletions, were interactively amalgamated for feature generation and cancer prediction. We applied DeepCues to a dataset from TCGA to classify seven different types of major cancers and obtained an overall accuracy of 77.6%. We compared DeepCues to conventional methods and demonstrated a significant overall improvement (p < 0.001). Strikingly, using DeepCues, the top 20 breast cancer relevant genes we have identified, had a 40% overlap with the top 20 known breast cancer driver genes.
conclusionOur results support DeepCues as a novel method to improve the representational resolution of DNA sequencings and its power in deriving features from raw sequences for cancer type prediction, as well as discovering new cancer relevant genes.
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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.