Abstract
Identifying the disease-related genes of important human diseases from genomics can provide valuable clues for the discovery of potential therapeutic targets. However, discovering the disease-related genes by traditional biological experiments methods is usually laborious and time-consuming. Therefore, it is necessary to develop a powerful computational approach to improve the effectiveness of disease-related gene identification. In this study, multiple sequence features of known disease-related genes in 62 kinds of diseases were extracted, and then the selected features were further optimized and analyzed for disease-related genes prediction. The leave-one-out cross-validation tests demonstrated that 55% of the disease-related genes can be ranked within the top 10 of the prediction results, which confirmed the reliability of this multi-feature fusion approach.
Keywords: Disease-related gene, sequence features, usage bias, F-statistic
Current Proteomics
Title: Prediction of Disease-Related Genes Based on Hybrid Features
Volume: 7 Issue: 2
Author(s): Mingxiao Li, Zhibin Li, Zhenran Jiang and Dandan Li
Affiliation:
Keywords: Disease-related gene, sequence features, usage bias, F-statistic
Abstract: Identifying the disease-related genes of important human diseases from genomics can provide valuable clues for the discovery of potential therapeutic targets. However, discovering the disease-related genes by traditional biological experiments methods is usually laborious and time-consuming. Therefore, it is necessary to develop a powerful computational approach to improve the effectiveness of disease-related gene identification. In this study, multiple sequence features of known disease-related genes in 62 kinds of diseases were extracted, and then the selected features were further optimized and analyzed for disease-related genes prediction. The leave-one-out cross-validation tests demonstrated that 55% of the disease-related genes can be ranked within the top 10 of the prediction results, which confirmed the reliability of this multi-feature fusion approach.
Export Options
About this article
Cite this article as:
Li Mingxiao, Li Zhibin, Jiang Zhenran and Li Dandan, Prediction of Disease-Related Genes Based on Hybrid Features, Current Proteomics 2010; 7 (2) . https://dx.doi.org/10.2174/157016410791330525
DOI https://dx.doi.org/10.2174/157016410791330525 |
Print ISSN 1570-1646 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-6247 |
Call for Papers in Thematic Issues
Mass spectrometry data acquisition and analysis for proteomics
The Thematic Issue on "Mass spectrometry data acquisition and analysis for proteomics" aims to explore the latest advancements and challenges in the field of proteomics through the lens of mass spectrometry. Proteomics, the large-scale study of proteins and their functions, plays a crucial role in understanding various biological processes and ...read more
Peptides: State-of-Art and Commercialisation Hurdles
The Editors of the Current Proteomics (CP) journal are highly privileged to welcome scientists to submit their scientific research and review articles to be considered for publication in the upcoming thematic issue. The topics should cover various aspects of peptides in regard to their synthetic methodologies, formulation approaches, pharmacological challenges, ...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
- Announcements
Related Articles
-
Microparticles in Health and Disease: Small Mediators, Large Role?
Current Vascular Pharmacology Recombinant Human Insulin-Like Growth Factor-1: A New Cardiovascular Disease Treatment Option?
Cardiovascular & Hematological Agents in Medicinal Chemistry Mitochondrial Respiratory Complex I: Structure, Function and Implication in Human Diseases
Current Medicinal Chemistry Reversal of Cardiac Iron Loading and Dysfunction in Thalassemic Mice by Curcuminoids
Medicinal Chemistry Iodinated Contrast Media in Diagnostic Imaging: Cardiovascular Side Effects
Current Pharmacogenomics and Personalized Medicine Celiac Disease: An Emerging Epidemic
Current Nutrition & Food Science Mitochondrial Dysfunction and Oxidative Stress in Insulin Resistance
Current Pharmaceutical Design Insulin Resistance, Oxidative Stress and Cardiovascular Complications: Role of Sirtuins
Current Pharmaceutical Design Toll Like Receptors Signaling Pathways as a Target for Therapeutic Interventions
Current Signal Transduction Therapy SIRT1 as a Promising Novel Therapeutic Target for Myocardial Ischemia Reperfusion Injury and Cardiometabolic Disease
Current Drug Targets Biotechnological Engineering of Heparin/Heparan Sulphate: A Novel Area of Multi-Target Drug Discovery
Current Pharmaceutical Design Heme Oxygenase -1 Gene Therapy: Recent Advances and Therapeutic Applications
Current Gene Therapy Resident Cardiac Stem Cells
Current Pharmaceutical Design Amiodarone - A ‘Broad Spectrum’ Antiarrhythmic Drug
Cardiovascular & Hematological Disorders-Drug Targets Electrotransfer into Skeletal Muscle for Protein Expression
Current Gene Therapy Withdrawal Notice: COVID-19: Outbreak to Global
Anti-Infective Agents Role of Differential Signaling Pathways and Oxidative Stress in Diabetic Cardiomyopathy
Current Cardiology Reviews Ionophores as Potent Anti-malarials: A Miracle in the Making
Current Topics in Medicinal Chemistry Possible Involvement of TRP Channels in Cardiac Hypertrophy and Arrhythmia
Current Topics in Medicinal Chemistry Rho Kinase Inhibitors: Potential Treatments for Diabetes and Diabetic Complications
Current Pharmaceutical Design