ArticlePeerJ. Computer science2020
A comparative study of machine learning and deep learning algorithms to classify cancer types based on microarray gene expression data.
Article in PeerJ. Computer science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- AI based multiomics integration for cancer diagnosis and prognosis.Journal, genetic engineering & biotechnology · 2026Article
- Exploring potential gene signatures in dengue through machine learning and deep learning approaches.Virus genes · 2026Article
- Ensemble techniques for predictive modeling of leishmanial activity via molecular fingerprints.BMC medical informatics and decision making · 2025Article
- Gene regulatory network prediction using machine learning, deep learning, and hybrid approaches.Forestry research · 2025Article
- Review
- Cancer genetics and deep learning applications for diagnosis, prognosis, and categorization.Journal of biological methods · 2024Review
- A novel and innovative cancer classification framework through a consecutive utilization of hybrid feature selection.BMC bioinformatics · 2023Article
- Article
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- On the challenges of predicting treatment response in Hodgkin's Lymphoma using transcriptomic data.BMC medical genomics · 2023Article
- A New Fuzzy-Based Classification Method for Use in Smart/Precision Medicine.Bioengineering (Basel, Switzerland) · 2023Article
- Deep Learning Framework for Complex Disease Risk Prediction Using Genomic Variations.Sensors (Basel, Switzerland) · 2023Article
- Diagnostic Performance Evaluation of Multiparametric Magnetic Resonance Imaging in the Detection of Prostate Cancer with Supervised Machine Learning Methods.Diagnostics (Basel, Switzerland) · 2023Review
- Machine Learning Methods for Cancer Classification Using Gene Expression Data: A Review.Bioengineering (Basel, Switzerland) · 2023Review
- Article
- Automatic curation of LTR retrotransposon libraries from plant genomes through machine learning.Journal of integrative bioinformatics · 2022Article
- Article
- Applying Information Gain to Explore Factors Affecting Small-Incision Lenticule Extraction: A Multicenter Retrospective Study.Frontiers in medicine · 2022Article
- A comprehensive survey on computational learning methods for analysis of gene expression data.Frontiers in molecular biosciences · 2022Review
- Feature Subset Selection with Optimal Adaptive Neuro-Fuzzy Systems for Bioinformatics Gene Expression Classification.Computational intelligence and neuroscience · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cancer classification is a topic of major interest in medicine since it allows accurate and efficient diagnosis and facilitates a successful outcome in medical treatments. Previous studies have classified human tumors using a large-scale RNA profiling and supervised Machine Learning (ML) algorithms to construct a molecular-based classification of carcinoma cells from breast, bladder, adenocarcinoma, colorectal, gastro esophagus, kidney, liver, lung, ovarian, pancreas, and prostate tumors. These datasets are collectively known as the 11_tumor database, although this database has been used in several works in the ML field, no comparative studies of different algorithms can be found in the literature. On the other hand, advances in both hardware and software technologies have fostered considerable improvements in the precision of solutions that use ML, such as Deep Learning (DL). In this study, we compare the most widely used algorithms in classical ML and DL to classify the tumors described in the 11_tumor database. We obtained tumor identification accuracies between 90.6% (Logistic Regression) and 94.43% (Convolutional Neural Networks) using
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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 Socratic graph.