Abstract
In this review, we have discussed the class-prediction and discovery methods that are applied to gene expression data, along with the implications of the findings. We attempted to present a unified approach that considers both class-prediction and class-discovery. We devoted a substantial part of this review to an overview of pattern classification/recognition methods and discussed important issues such as preprocessing of gene expression data, curse of dimensionality, feature extraction/selection, and measuring or estimating classifier performance. We discussed and summarized important properties such as generalizability (sensitivity to overtraining), built-in feature selection, ability to report prediction strength, and transparency (ease of understanding of the operation) of different class-predictor design approaches to provide a quick and concise reference. We have also covered the topic of biclustering, which is an emerging clustering method that processes the entries of the gene expression data matrix in both gene and sample directions simultaneously, in detail.
Keywords: cDNA microarrays, Fisher's Linear Discriminant Analysis (FLDA), Artificial Neural Networks, multidimensional scaling, cross-validation (CV), Super-Paramagnetic Clustering algorithm
Current Bioinformatics
Title: Gene Expression Profile Classification: A Review
Volume: 1 Issue: 1
Author(s): Musa H. Asyali, Dilek Colak, Omer Demirkaya and Mehmet S. Inan
Affiliation:
Keywords: cDNA microarrays, Fisher's Linear Discriminant Analysis (FLDA), Artificial Neural Networks, multidimensional scaling, cross-validation (CV), Super-Paramagnetic Clustering algorithm
Abstract: In this review, we have discussed the class-prediction and discovery methods that are applied to gene expression data, along with the implications of the findings. We attempted to present a unified approach that considers both class-prediction and class-discovery. We devoted a substantial part of this review to an overview of pattern classification/recognition methods and discussed important issues such as preprocessing of gene expression data, curse of dimensionality, feature extraction/selection, and measuring or estimating classifier performance. We discussed and summarized important properties such as generalizability (sensitivity to overtraining), built-in feature selection, ability to report prediction strength, and transparency (ease of understanding of the operation) of different class-predictor design approaches to provide a quick and concise reference. We have also covered the topic of biclustering, which is an emerging clustering method that processes the entries of the gene expression data matrix in both gene and sample directions simultaneously, in detail.
Export Options
About this article
Cite this article as:
Asyali H. Musa, Colak Dilek, Demirkaya Omer and Inan S. Mehmet, Gene Expression Profile Classification: A Review, Current Bioinformatics 2006; 1 (1) . https://dx.doi.org/10.2174/157489306775330615
DOI https://dx.doi.org/10.2174/157489306775330615 |
Print ISSN 1574-8936 |
Publisher Name Bentham Science Publisher |
Online ISSN 2212-392X |
- Author Guidelines
- Graphical Abstracts
- Fabricating and Stating False Information
- Research Misconduct
- Post Publication Discussions and Corrections
- Publishing Ethics and Rectitude
- Increase Visibility of Your Article
- Archiving Policies
- Peer Review Workflow
- Order Your Article Before Print
- Promote Your Article
- Manuscript Transfer Facility
- Editorial Policies
- Allegations from Whistleblowers
Related Articles
-
Genetic Predisposition to Neonatal Tumors
Current Pediatric Reviews Dietary Phytochemicals in Chemoprevention of Cancer: An Update
Immunology, Endocrine & Metabolic Agents in Medicinal Chemistry (Discontinued) Novel Agents Aiming at Specific Molecular Targets Increase Chemosensitivity and Overcome Chemoresistance in Hematopoietic Malignancies
Current Pharmaceutical Design Vitamin D Analogs as Anti-Carcinogenic Agents
Anti-Cancer Agents in Medicinal Chemistry Design, Synthesis, and Evaluation of (2-(Pyridinyl)methylene)-1-tetralone Chalcones for Anticancer and Antimicrobial Activity
Medicinal Chemistry Nitric Oxide and the Regulation of Apoptosis in Tumour Cells
Current Pharmaceutical Design Small Molecular Inhibitors Targeting Chromatin Regulating Proteins for Cancer
Current Protein & Peptide Science Methionine AminoPeptidase Type-2 Inhibitors Targeting Angiogenesis
Current Topics in Medicinal Chemistry Mechanism of CNS Drugs and their Combinations for Alzheimers Disease
Central Nervous System Agents in Medicinal Chemistry Human Sirtuins: An Overview of an Emerging Drug Target in Age-Related Diseases and Cancer
Current Drug Targets No Significant Effect of 7,8-Dihydroxyflavone on APP Processing and Alzheimer-Associated Phenotypes
Current Alzheimer Research Novel Nucleic Acid-Based Agents: siRNAs and miRNAs
Central Nervous System Agents in Medicinal Chemistry Stem Cells in Brain Tumorigenesis and their Impact on Therapy
Current Stem Cell Research & Therapy Erythropoietin Signaling and Neuroprotection
Current Signal Transduction Therapy Combinations of Plant Polyphenols & Anti-Cancer Molecules: A Novel Treatment Strategy for Cancer Chemotherapy
Anti-Cancer Agents in Medicinal Chemistry Establishing Genomic/Transcriptomic Links Between Alzheimer’s Disease and Type 2 Diabetes Mellitus by Meta-Analysis Approach
CNS & Neurological Disorders - Drug Targets The Pharmacokinetics and Toxicology of Aluminum in the Brain
Current Inorganic Chemistry (Discontinued) Pathogenic Mechanisms and Therapeutic Strategies in Spinobulbar Muscular Atrophy
CNS & Neurological Disorders - Drug Targets Sigma Receptors in Oncology: Therapeutic and Diagnostic Applications of Sigma Ligands
Current Pharmaceutical Design Loss in Toxic Function of Aggregates of α -Synuclein Mutants by a β-Synuclein Derived Peptide
Protein & Peptide Letters