ArticlePloS one2012
Using rule-based machine learning for candidate disease gene prioritization and sample classification of cancer gene expression data.
Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed.
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- On the challenges of predicting treatment response in Hodgkin's Lymphoma using transcriptomic data.BMC medical genomics · 2023Article
- DeepGenePrior: A deep learning model for prioritizing genes affected by copy number variants.PLoS computational biology · 2023Article
- Utilization of Computer Classification Methods for Exposure Prediction and Gene Selection inBiology · 2023Article
- Clinical Decision Support System for Diabetic Patients by Predicting Type 2 Diabetes Using Machine Learning Algorithms.Journal of healthcare engineering · 2023Article
- Machine learning methods to predict 30-day hospital readmission outcome among US adults with pneumonia: analysis of the national readmission database.BMC medical informatics and decision making · 2022Article
- Construction of a multiple-class classifier based on mRNAs and lncRNA FAM66A and PSORS1C3 for predicting distant metastasis in lung adenocarcinoma.Annals of translational medicine · 2022Article
- Ten quick tips for biomarker discovery and validation analyses using machine learning.PLoS computational biology · 2022Article
- Interpretable machine learning identifies paediatric Systemic Lupus Erythematosus subtypes based on gene expression data.Scientific reports · 2022Article
- Optimal Deep Learning Enabled Prostate Cancer Detection Using Microarray Gene Expression.Journal of healthcare engineering · 2022Article
- TLGP: a flexible transfer learning algorithm for gene prioritization based on heterogeneous source domain.BMC bioinformatics · 2021Article
- EARN: an ensemble machine learning algorithm to predict driver genes in metastatic breast cancer.BMC medical genomics · 2021Article
- A Novel Coupled Reaction-Diffusion System for Explainable Gene Expression Profiling.Sensors (Basel, Switzerland) · 2021Article
- Molecular Determinants of Antibiotic Resistance in the Costa RicanPhenomics (Cham, Switzerland) · 2021Review
- Machine Learning Methods as a Tool for Predicting Risk of Illness Applying Next-Generation Sequencing Data.Risk analysis : an official publication of the Society for Risk Analysis · 2019Article
- Identification of gene signatures from RNA-seq data using Pareto-optimal cluster algorithm.BMC systems biology · 2018Article
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4 authors.
Funding
Abstract
Microarray data analysis has been shown to provide an effective tool for studying cancer and genetic diseases. Although classical machine learning techniques have successfully been applied to find informative genes and to predict class labels for new samples, common restrictions of microarray analysis such as small sample sizes, a large attribute space and high noise levels still limit its scientific and clinical applications. Increasing the interpretability of prediction models while retaining a high accuracy would help to exploit the information content in microarray data more effectively. For this purpose, we evaluate our rule-based evolutionary machine learning systems, BioHEL and GAssist, on three public microarray cancer datasets, obtaining simple rule-based models for sample classification. A comparison with other benchmark microarray sample classifiers based on three diverse feature selection algorithms suggests that these evolutionary learning techniques can compete with state-of-the-art methods like support vector machines. The obtained models reach accuracies above 90% in two-level external cross-validation, with the added value of facilitating interpretation by using only combinations of simple if-then-else rules. As a further benefit, a literature mining analysis reveals that prioritizations of informative genes extracted from BioHEL's classification rule sets can outperform gene rankings obtained from a conventional ensemble feature selection in terms of the pointwise mutual information between relevant disease terms and the standardized names of top-ranked 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.