ArticleNature biomedical engineering2024
Multiple-instance learning of somatic mutations for the classification of tumour type and the prediction of microsatellite status.
Article in Nature biomedical engineering, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Advancing AI for multi-omics and clinical data integration in basic and translational cancer research.Nature reviews. Cancer · 2026Review
- Normal breast tissue (NBT)-classifiers: advancing compartment classification in normal breast histology.NPJ breast cancer · 2026Article
- Multi-omics driven computational framework for cancer molecular subtype classification.Scientific reports · 2025Article
- PathOrchestra: a comprehensive foundation model for computational pathology with over 100 diverse clinical-grade tasks.NPJ digital medicine · 2025Article
- Integration of Gene Expression and Digital Histology to Predict Treatment-Specific Responses in Breast Cancer.medRxiv : the preprint server for health sciences · 2025Article
- A Self-Supervised Equivariant Refinement Classification Network for Diabetic Retinopathy Classification.Journal of imaging informatics in medicine · 2025Article
- Deep Learning for Biomarker Discovery in Cancer Genomes.bioRxiv : the preprint server for biology · 2025Article
- Lynch Syndrome and Somatic Mismatch Repair Variants in Pancreas Cancer.JAMA oncology · 2024Article
- Spatial oncology: Translating contextual biology to the clinic.Cancer cell · 2024Review
- A guide to artificial intelligence for cancer researchers.Nature reviews. Cancer · 2024Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Large-scale genomic data are well suited to analysis by deep learning algorithms. However, for many genomic datasets, labels are at the level of the sample rather than for individual genomic measures. Machine learning models leveraging these datasets generate predictions by using statically encoded measures that are then aggregated at the sample level. Here we show that a single weakly supervised end-to-end multiple-instance-learning model with multi-headed attention can be trained to encode and aggregate the local sequence context or genomic position of somatic mutations, hence allowing for the modelling of the importance of individual measures for sample-level classification and thus providing enhanced explainability. The model solves synthetic tasks that conventional models fail at, and achieves best-in-class performance for the classification of tumour type and for predicting microsatellite status. By improving the performance of tasks that require aggregate information from genomic datasets, multiple-instance deep learning may generate biological insight.
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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.