ArticleJournal of biomedical informatics2019
Cancer classification and pathway discovery using non-negative matrix factorization.
Article in Journal of biomedical informatics, 2019. 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, 33 citations in OpenAlex.
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- Integration of Multi-Omics Data for the Classification of Glioma Types and Identification of Novel Biomarkers.Bioinformatics and biology insights · 2024Article
- Machine Learning for Lung Cancer Diagnosis, Treatment, and Prognosis.Genomics, proteomics & bioinformatics · 2022Review
- Application of non-negative matrix factorization in oncology: one approach for establishing precision medicine.Briefings in bioinformatics · 2022Review
- Using an Unsupervised Clustering Model to Detect the Early Spread of SARS-CoV-2 Worldwide.Genes · 2022Article
- Radiomics of Contrast-Enhanced Computed Tomography: A Potential Biomarker for Pretreatment Prediction of the Response toFrontiers in cell and developmental biology · 2022Article
- AX-Unet: A Deep Learning Framework for Image Segmentation to Assist Pancreatic Tumor Diagnosis.Frontiers in oncology · 2022Article
- Multi-omics assessment of dilated cardiomyopathy using non-negative matrix factorization.PloS one · 2022Article
- Unsupervised clustering analysis of SARS-Cov-2 population structure reveals six major subtypes at early stage across the world.bioRxiv : the preprint server for biology · 2021Article
- Deep learning for cancer type classification and driver gene identification.BMC bioinformatics · 2021Article
- Unsupervised phenotyping of sepsis using nonnegative matrix factorization of temporal trends from a multivariate panel of physiological measurements.BMC medical informatics and decision making · 2021Article
- Tumor immune microenvironment-based classifications of bladder cancer for enhancing the response rate of immunotherapy.Molecular therapy oncolytics · 2021Article
- Comprehensive Genomic and Epigenomic Analyses on Transcriptomic Regulation in Stomach Adenocarcinoma.Frontiers in genetics · 2021Article
- Performance-weighted-voting model: An ensemble machine learning method for cancer type classification using whole-exome sequencing mutation.Quantitative biology (Beijing, China) · 2020Article
- Analysis of immune-related signatures of colorectal cancer identifying two different immune phenotypes: Evidence for immune checkpoint inhibitor therapy.Oncology letters · 2020Article
- A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases.NPJ digital medicine · 2020Review
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
objectivesExtracting genetic information from a full range of sequencing data is important for understanding disease. We propose a novel method to effectively explore the landscape of genetic mutations and aggregate them to predict cancer type.
designWe applied non-smooth non-negative matrix factorization (nsNMF) and support vector machine (SVM) to utilize the full range of sequencing data, aiming to better aggregate genetic mutations and improve their power to predict disease type. More specifically, we introduce a novel classifier to distinguish cancer types using somatic mutations obtained from whole-exome sequencing data. Mutations were identified from multiple cancers and scored using SIFT, PP2, and CADD, and collapsed at the individual gene level. nsNMF was then applied to reduce dimensionality and obtain coefficient and basis matrices. A feature matrix was derived from the obtained matrices to train a classifier for cancer type classification with the SVM model.
resultsWe have demonstrated that the classifier was able to distinguish four cancer types with reasonable accuracy. In five-fold cross-validations using mutation counts as features, the average prediction accuracy was 80% (SEM = 0.1%), significantly outperforming baselines and outperforming models using mutation scores as features.
conclusionUsing the factor matrices derived from the nsNMF, we identified multiple genes and pathways that are significantly associated with each cancer type. This study presents a generic and complete pipeline to study the associations between somatic mutations and cancers. The proposed method can be adapted to other studies for disease status classification and pathway discovery.
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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.