Abstract
Aim and Objective: Gene selection method as an important data preprocessing work has been followed. The criteria Maximum relevance and minimum redundancy (MRMR) has been commonly used for gene selection, which has a satisfactory performance in evaluating the correlation between two genes. However, for viewing genes in isolation, it ignores the influence of other genes.
Methods: In this study, we propose a new method based on sparse representation and MRMR algorithm (SRCMRM), using the sparse representation coefficient to represent the relevance of genes and correlation between genes and categories. The SRCMRMR algorithm contains two steps. Firstly, the genes irrelevant to the classification target are removed by using sparse representation coefficient. Secondly, sparse representation coefficient is used to calculate the correlation between genes and the most representative gene with the highest evaluation. Results: To validate the performance of the SRCMRM, our method is compared with various algorithms. The proposed method achieves better classification accuracy for all datasets. Conclusion: The effectiveness and stability of our method have been proven through various experiments, which means that our method has practical significance.Keywords: Sparse representation, MRMR, gene selection.
Combinatorial Chemistry & High Throughput Screening
Title:A Novel Gene Selection Method Based on Sparse Representation and Max-Relevance and Min-Redundancy
Volume: 20 Issue: 2
Author(s): Min Chen, Xiaoming He, ShaoBin Duan*YingWei Deng
Affiliation:
- Department of Physical Education, Hunan Institute of Technology, 421002 Hengyang,China
Keywords: Sparse representation, MRMR, gene selection.
Abstract: Aim and Objective: Gene selection method as an important data preprocessing work has been followed. The criteria Maximum relevance and minimum redundancy (MRMR) has been commonly used for gene selection, which has a satisfactory performance in evaluating the correlation between two genes. However, for viewing genes in isolation, it ignores the influence of other genes.
Methods: In this study, we propose a new method based on sparse representation and MRMR algorithm (SRCMRM), using the sparse representation coefficient to represent the relevance of genes and correlation between genes and categories. The SRCMRMR algorithm contains two steps. Firstly, the genes irrelevant to the classification target are removed by using sparse representation coefficient. Secondly, sparse representation coefficient is used to calculate the correlation between genes and the most representative gene with the highest evaluation. Results: To validate the performance of the SRCMRM, our method is compared with various algorithms. The proposed method achieves better classification accuracy for all datasets. Conclusion: The effectiveness and stability of our method have been proven through various experiments, which means that our method has practical significance.Export Options
About this article
Cite this article as:
Chen Min, He Xiaoming, Duan ShaoBin*, Deng YingWei, A Novel Gene Selection Method Based on Sparse Representation and Max-Relevance and Min-Redundancy, Combinatorial Chemistry & High Throughput Screening 2017; 20 (2) . https://dx.doi.org/10.2174/1386207320666170126114051
DOI https://dx.doi.org/10.2174/1386207320666170126114051 |
Print ISSN 1386-2073 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-5402 |
Call for Papers in Thematic Issues
Artificial Intelligence Methods for Biomedical, Biochemical and Bioinformatics Problems
Recently, a large number of technologies based on artificial intelligence have been developed and applied to solve a diverse range of problems in the areas of biomedical, biochemical and bioinformatics problems. By utilizing powerful computing resources and massive amounts of data, methods based on artificial intelligence can significantly improve the ...read more
Eco-friendly Agents for Biological Control of Pathogenic Diseases
The discovery of an alternative biological approach to disease management includes work on medicinal products derived from natural sources as a starting point for the development of eco-friendly agents for these diseases and the injuries they cause, as well as reducing human contact with hazardous chemicals and their residues. We ...read more
Emerging trends in diseases mechanisms, noble drug targets and therapeutic strategies: focus on immunological and inflammatory disorders
Recently infectious and inflammatory diseases have been a key concern worldwide due to tremendous morbidity and mortality world Wide. Recent, nCOVID-9 pandemic is a good example for the emerging infectious disease outbreak. The world is facing many emerging and re-emerging diseases out breaks at present however, there is huge lack ...read more
Exploring Spectral Graph Theory in Combinatorial Chemistry
Scope of the Thematic Issue: Combinatorial chemistry involves the synthesis and analysis of a large number of diverse compounds simultaneously. Traditional methods rely on brute force experimentation, which can be time-consuming and resource-intensive. Spectral Graph Theory, a branch of mathematics dealing with the properties of graphs in relation to the ...read more
- 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
-
Therapeutic Targeting of G-Protein Coupled Receptor-Mediated Epidermal Growth Factor Receptor Transactivation in Human Glioma Brain Tumors
Mini-Reviews in Medicinal Chemistry Tankyrase Inhibitors as Therapeutic Targets for Cancer
Current Topics in Medicinal Chemistry Potentials of ES Cell Therapy in Neurodegenerative Diseases
Current Pharmaceutical Design Imaging of Cyclooxygenase-2 (COX-2) Expression: Potential Use in Diagnosis and Drug Evaluation
Current Pharmaceutical Design Emerging Features in the Regulation of MMP-9 Gene Expression for the Development of Novel Molecular Targets and Therapeutic Strategies
Current Drug Targets - Inflammation & Allergy The Effectiveness of Nanoparticles on Gene Therapy for Glioblastoma Cells Apoptosis: A Systematic Review
Current Gene Therapy Suramin: Clinical Uses and Structure-Activity Relationships
Mini-Reviews in Medicinal Chemistry Cancer Vaccines: Emphasis on Pediatric Cancers
Current Pharmaceutical Design Nanofibers: An Effective Tool for Controlled and Sustained Drug Delivery
Current Drug Delivery Mitochondrial and Nuclear Genes of Mitochondrial Components in Cancer
Current Genomics Rational Drug Design for Identifying Novel Multi-target Inhibitors for Hepatocellular Carcinoma
Anti-Cancer Agents in Medicinal Chemistry Nanoparticle Systems Modulating Myeloid-Derived Suppressor Cells for Cancer Immunotherapy
Current Topics in Medicinal Chemistry Is the Epithelial-to-Mesenchymal Transition Clinically Relevant for the Cancer Patient?
Current Pharmaceutical Biotechnology Targeting Calcium Channels to Block Tumor Vascularization
Recent Patents on Anti-Cancer Drug Discovery Endogenous Angiogenesis Inhibitors as Therapeutic Agents: Historical Perspective and Future Direction
Recent Patents on Anti-Cancer Drug Discovery Impact of Hybrid-polar Histone Deacetylase Inhibitor m-Carboxycinnamic Acid bis-Hydroxyamide on Human Pancreatic Adenocarcinoma Cells
Anti-Cancer Agents in Medicinal Chemistry A Systems Biology Road Map for the Discovery of Drugs Targeting Cancer Cell Metabolism
Current Pharmaceutical Design Design of New Improved Curcumin Derivatives to Multi-targets of Cancer and Inflammation
Current Drug Targets Sp/KLF Family and Tumor Angiogenesis in Pancreatic Cancer
Current Pharmaceutical Design A Fatal Case of Acute Interstitial Pneumonia (AIP) in a Woman Affected by Glioblastoma
Current Drug Safety