ReviewFrontiers in molecular biosciences2022
A comprehensive survey on computational learning methods for analysis of gene expression data.
Review in Frontiers in molecular biosciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Progress in the Application of Machine Learning in the Field of Single-Cell and Spatial Transcriptomics.Genes · 2026Review
- Hybrid feature selection and classification model using high-dimensional data based on a metaheuristic algorithm for brain cancer diagnosis.Scientific reports · 2026Article
- National data meets AI: Machine learning for predicting overweight/obesity among ever-married Bangladeshi women.PloS one · 2026Article
- Network analysis of multivariate time series data in biological systems: methods and applications.Briefings in bioinformatics · 2025Article
- Integrated machine learning and single-cell RNA sequencing reveal COL4A2 and CXCL6 as oxidative stress-associated biomarkers in periodontitis.Frontiers in immunology · 2025Article
- Multi-omics integration analysis based on plasma circulating proteins reveals potential therapeutic targets for ulcerative colitis.Frontiers in molecular biosciences · 2025Article
- Biomarker-driven drug repurposing for NAFLD-associated hepatocellular carcinoma using machine learning integrated ensemble feature selection.Frontiers in bioinformatics · 2025Article
- Normalization and Selecting Non-Differentially Expressed Genes Improve Machine Learning Modelling of Cross-Platform Transcriptomic Data.Transactions on artificial intelligence · 2025Article
- Mechanism exploration and biomarker identification of glycemic deterioration in patients with diseases of the exocrine pancreas.Scientific reports · 2024Article
- Gene Expression-Based Cancer Classification for Handling the Class Imbalance Problem and Curse of Dimensionality.International journal of molecular sciences · 2024Article
- The Diagnostic Features of Peripheral Blood Biomarkers in Identifying Osteoarthritis Individuals: Machine Learning Strategies and Clinical Evidence.Current computer-aided drug design · 2024Article
- Machine Learning Methods for Cancer Classification Using Gene Expression Data: A Review.Bioengineering (Basel, Switzerland) · 2023Review
- Cortical type: a conceptual tool for meaningful biological interpretation of high-throughput gene expression data in the human cerebral cortex.Frontiers in neuroanatomy · 2023Article
- Machine Learning Models for Identification and Prediction of Toxic Organic Compounds UsingLife (Basel, Switzerland) · 2022Article
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Authors and funding
4 authors.
Funding
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Abstract
Computational analysis methods including machine learning have a significant impact in the fields of genomics and medicine. High-throughput gene expression analysis methods such as microarray technology and RNA sequencing produce enormous amounts of data. Traditionally, statistical methods are used for comparative analysis of gene expression data. However, more complex analysis for classification of sample observations, or discovery of feature genes requires sophisticated computational approaches. In this review, we compile various statistical and computational tools used in analysis of expression microarray data. Even though the methods are discussed in the context of expression microarrays, they can also be applied for the analysis of RNA sequencing and quantitative proteomics datasets. We discuss the types of missing values, and the methods and approaches usually employed in their imputation. We also discuss methods of data normalization, feature selection, and feature extraction. Lastly, methods of classification and class discovery along with their evaluation parameters are described in detail. We believe that this detailed review will help the users to select appropriate methods for preprocessing and analysis of their data based on the expected outcome.
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