ReviewFrontiers in genetics2020
Machine Learning Based Computational Gene Selection Models: A Survey, Performance Evaluation, Open Issues, and Future Research Directions.
Review in Frontiers in genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 2 of them syntheses that pooled it.
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Who cites it
31 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Integrated transcriptomic meta-analysis and comparative artificial intelligence models in maize under biotic stress.Scientific reports · 2023Pooled it
- Deciphering the molecular basis of abiotic stress response in cucumber (Cucumis sativus L.) using RNA-Seq meta-analysis, systems biology, and machine learning approaches.Scientific reports · 2023Pooled it
- Computational theranostics strategy for pancreatic ductal adenocarcinoma.Molecular diversity · 2026Article
- Transfer learning models for bacterial strain dissemination biomarkers using weighted non-parallel proximal support vector machines.BMC bioinformatics · 2026Article
- Establishment of a Predictive Model for Mortality in Sepsis Patients Using WGCNA and Machine Learning Algorithms.Journal of inflammation research · 2026Article
- Bioinformatics strategies and biomarker refinement using high-throughput transcriptome data in transplantation.Frontiers in bioinformatics · 2026Article
- Cell type-specific inference from bulk RNA-sequencing data by integrating single-cell reference profiles via EPIC-unmix.Genome biology · 2025Article
- Machine learning-based discovery of informative SNPs for population assignment through whole genome sequencing.BMC genomics · 2025Article
- Decision tree-based machine learning methods for identifying colorectal cancer-associated microRNA signatures and their regulatory networks.Scientific reports · 2025Article
- Predictive prioritization of genes significantly associated with biotic and abiotic stresses in maize using machine learning algorithms.Frontiers in plant science · 2025Article
- A comparative analysis of gene expression profiling by statistical and machine learning approaches.Bioinformatics advances · 2025Article
- Machine Learning Meets Meta-Heuristics: Bald Eagle Search Optimization and Red Deer Optimization for Feature Selection in Type II Diabetes Diagnosis.Bioengineering (Basel, Switzerland) · 2024Article
- Elucidating the role of angiogenesis-related genes in colorectal cancer: a multi-omics analysis.Frontiers in oncology · 2024Article
- FPR1, as a Potential Biomarker of Diagnosis and Infliximab Therapy Responses for Crohn's Disease, is Related to Disease Activity, Inflammation and Macrophage Polarization.Journal of inflammation research · 2024Article
- Unraveling the mechanisms underlying drug-induced cholestatic liver injury: identifying key genes using machine learning techniques on human in vitro data sets.Archives of toxicology · 2023Article
- Application of Machine Learning in Predicting Hepatic Metastasis or Primary Site in Gastroenteropancreatic Neuroendocrine Tumors.Current oncology (Toronto, Ont.) · 2023Article
- Immunoglobulin genes expressed in lymphoblastoid cell lines discern and predict lithium response in bipolar disorder patients.Molecular psychiatry · 2023Article
- A two-stage hybrid biomarker selection method based on ensemble filter and binary differential evolution incorporating binary African vultures optimization.BMC bioinformatics · 2023Article
- Article
- Combining a machine-learning derived 4-lncRNA signature with AFP and TNM stages in predicting early recurrence of hepatocellular carcinoma.BMC genomics · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Gene Expression is the process of determining the physical characteristics of living beings by generating the necessary proteins. Gene Expression takes place in two steps, translation and transcription. It is the flow of information from DNA to RNA with enzymes' help, and the end product is proteins and other biochemical molecules. Many technologies can capture Gene Expression from the DNA or RNA. One such technique is Microarray DNA. Other than being expensive, the main issue with Microarray DNA is that it generates high-dimensional data with minimal sample size. The issue in handling such a heavyweight dataset is that the learning model will be over-fitted. This problem should be addressed by reducing the dimension of the data source to a considerable amount. In recent years, Machine Learning has gained popularity in the field of genomic studies. In the literature, many Machine Learning-based Gene Selection approaches have been discussed, which were proposed to improve dimensionality reduction precision. This paper does an extensive review of the various works done on Machine Learning-based gene selection in recent years, along with its performance analysis. The study categorizes various feature selection algorithms under Supervised, Unsupervised, and Semi-supervised learning. The works done in recent years to reduce the features for diagnosing tumors are discussed in detail. Furthermore, the performance of several discussed methods in the literature is analyzed. This study also lists out and briefly discusses the open issues in handling the high-dimension and less sample size data.
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