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
-
Role of miRNAs in Cancer Diagnostics and Therapy: A Recent Update
Current Pharmaceutical Design Co-Morbidity and Self Medication in Schizophrenia: Involvement of Endogenous Morphine Signaling Mechanisms
Current Drug Targets Does Parkinson’s Disease and Type-2 Diabetes Mellitus Present Common Pathophysiological Mechanisms and Treatments?
CNS & Neurological Disorders - Drug Targets Cancer Stem Cells: The ‘Achilles Heel’ of Chemo-Resistant Tumors
Recent Patents on Anti-Cancer Drug Discovery The Potential of Flavonoids and Tannins from Medicinal Plants as Anticancer Agents
Anti-Cancer Agents in Medicinal Chemistry The Role of Nitric Oxide Synthase Inhibitors in Schizophrenia
Current Medicinal Chemistry Anti-Inflammatory and Anti-Neoplastic Actions of Resveratrol
Current Nutrition & Food Science From Peptides to Small Molecules: An Intriguing but Intricated Way to New Drugs
Current Medicinal Chemistry Novel Aspects of Neuronal Differentiation In Vitro and Monitoring with Advanced Biosensor Tools
Current Medicinal Chemistry Structure-Activity Relationship Studies on ADAM Protein-Integrin Interactions
Cardiovascular & Hematological Agents in Medicinal Chemistry microRNAs: Small Molecules with a Potentially Role in Oral Squamous Cell Carcinoma
Current Pharmaceutical Design Peptide-Based Anticancer Vaccines: Recent Advances and Future Perspectives
Current Medicinal Chemistry Targeted Tumor Immunotherapy: Are Vaccines the Future of Cancer Treatment?
Current Drug Therapy MicroRNAs in Genetic Disease: Rethinking the Dosage
Current Gene Therapy Pharmacological Properties and Therapeutic Potential of Naringenin: A Citrus Flavonoid of Pharmaceutical Promise
Current Pharmaceutical Design Targeting Class IIa HDACs: Insights from Phenotypes and Inhibitors
Current Medicinal Chemistry Meridianins: Marine-Derived Potent Kinase Inhibitors
Mini-Reviews in Medicinal Chemistry Protein Tyrosine Signaling and its Potential Therapeutic Implications in Carcinogenesis
Current Pharmaceutical Design Carbon Nanotubes in the Diagnosis and Treatment of Malignant Melanoma
Anti-Cancer Agents in Medicinal Chemistry Role of Chemokines and Their Receptors in Cancer
Current Pharmaceutical Design