ArticleBMC medical genomics2019
NeoMutate: an ensemble machine learning framework for the prediction of somatic mutations in cancer.
Article in BMC medical genomics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed.
- Decoding cancer with artificial intelligence: Transforming research, diagnosis, and therapy with future insights.Translational oncology · 2026Review
- VariantMedium: sensitive and generalizable somatic point mutation calling with 3D DenseNets trained and evaluated on experimental data.Genome medicine · 2026Article
- A review of deep learning-based multimodal data integration of lung cancer.Clinical and experimental medicine · 2026Review
- Opportunities for machine learning and artificial intelligence in physiologically-based pharmacokinetic (PBPK) modeling.Advanced drug delivery reviews · 2025Review
- Comparative Evaluation of Mutect2, Strelka2, and FreeBayes for Somatic SNV Detection in Synthetic and Clinical Whole-Exome Sequencing Data.Biomolecules · 2025Article
- Predicting high confidence ctDNA somatic variants with ensemble machine learning models.Scientific reports · 2025Article
- Review
- Neoantigen-based immunotherapy: advancing precision medicine in cancer and glioblastoma treatment through discovery and innovation.Exploration of targeted anti-tumor therapy · 2025Review
- Rare disease genomics and precision medicine.Genomics & informatics · 2024Review
- Emerging research trends in artificial intelligence for cancer diagnostic systems: A comprehensive review.Heliyon · 2024Review
- Benchmarking UMI-aware and standard variant callers for low frequency ctDNA variant detection.BMC genomics · 2024Article
- A Novel Affordable and Reliable Framework for Accurate Detection and Comprehensive Analysis of Somatic Mutations in Cancer.International journal of molecular sciences · 2024Article
- COSAP: Comparative Sequencing Analysis Platform.BMC bioinformatics · 2024Article
- Clinical Activity of Combined Telomerase Vaccination and Pembrolizumab in Advanced Melanoma: Results from a Phase I Trial.Clinical cancer research : an official journal of the American Association for Cancer Research · 2023Article
- RFcaller: a machine learning approach combined with read-level features to detect somatic mutations.NAR genomics and bioinformatics · 2023Article
- Simple combination of multiple somatic variant callers to increase accuracy.Scientific reports · 2023Article
- The interplay between neoantigens and immune cells in sarcomas treated with checkpoint inhibition.Frontiers in immunology · 2023Article
- 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
- Artificial intelligence and database for NGS-based diagnosis in rare disease.Frontiers in genetics · 2023Review
- A Multimodal Ensemble Driven by Multiobjective Optimisation to Predict Overall Survival in Non-Small-Cell Lung Cancer.Journal of imaging · 2022Article
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Authors and funding
4 authors.
Funding
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Abstract
backgroundThe accurate screening of tumor genomic landscapes for somatic mutations using high-throughput sequencing involves a crucial step in precise clinical diagnosis and targeted therapy. However, the complex inherent features of cancer tissue, especially, tumor genetic intra-heterogeneity coupled with the problem of sequencing and alignment artifacts, makes somatic variant calling a challenging task. Current variant filtering strategies, such as rule-based filtering and consensus voting of different algorithms, have previously helped to increase specificity, although comes at the cost of sensitivity.
methodsIn light of this, we have developed the NeoMutate framework which incorporates 7 supervised machine learning (ML) algorithms to exploit the strengths of multiple variant callers, using a non-redundant set of biological and sequence features. We benchmarked NeoMutate by simulating more than 10,000 bona fide cancer-related mutations into three well-characterized Genome in a Bottle (GIAB) reference samples.
resultsA robust and exhaustive evaluation of NeoMutate's performance based on 5-fold cross validation experiments, in addition to 3 independent tests, demonstrated a substantially improved variant detection accuracy compared to any of its individual composite variant callers and consensus calling of multiple tools.
conclusionsWe show here that integrating multiple tools in an ensemble ML layer optimizes somatic variant detection rates, leading to a potentially improved variant selection framework for the diagnosis and treatment of cancer.
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