Abstract
Most essential functions are associated with various protein–protein interactions, particularly the cytokine–receptor interaction. Knowledge of the heterogeneous network of cytokine– receptor interactions provides insights into various human physiological functions. However, only a few studies are focused on the computational prediction of these interactions. In this study, we propose a novel machine-learning-based method for predicting cytokine–receptor interactions. A protein sequence is first transformed by incorporating the sequence evolutional information and then formulated with the following three aspects: (1) the k-skip-n-gram model, (2) physicochemical properties, and (3) local pseudo position-specific score matrix (local PsePSSM). The random forest classifier is subsequently employed to predict potential cytokine–receptor interactions. Experimental results on a dataset of Homo sapiens show that the proposed method exhibits improved performance, with 3.4% higher overall prediction accuracy, than existing methods.
Keywords: Cytokine–receptor interaction prediction, feature extraction, random forest, sequence evolutional information.
Combinatorial Chemistry & High Throughput Screening
Title:A novel machine learning method for cytokine-receptor interaction prediction
Volume: 19 Issue: 2
Author(s): Leyi Wei, Quan Zou, Minghong Liao, Huijuan Lu and Yuming Zhao
Affiliation:
Keywords: Cytokine–receptor interaction prediction, feature extraction, random forest, sequence evolutional information.
Abstract: Most essential functions are associated with various protein–protein interactions, particularly the cytokine–receptor interaction. Knowledge of the heterogeneous network of cytokine– receptor interactions provides insights into various human physiological functions. However, only a few studies are focused on the computational prediction of these interactions. In this study, we propose a novel machine-learning-based method for predicting cytokine–receptor interactions. A protein sequence is first transformed by incorporating the sequence evolutional information and then formulated with the following three aspects: (1) the k-skip-n-gram model, (2) physicochemical properties, and (3) local pseudo position-specific score matrix (local PsePSSM). The random forest classifier is subsequently employed to predict potential cytokine–receptor interactions. Experimental results on a dataset of Homo sapiens show that the proposed method exhibits improved performance, with 3.4% higher overall prediction accuracy, than existing methods.
Export Options
About this article
Cite this article as:
Wei Leyi, Zou Quan, Liao Minghong, Lu Huijuan and Zhao Yuming, A novel machine learning method for cytokine-receptor interaction prediction, Combinatorial Chemistry & High Throughput Screening 2016; 19 (2) . https://dx.doi.org/10.2174/1386207319666151110122621
DOI https://dx.doi.org/10.2174/1386207319666151110122621 |
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
-
Multi-Target Profile of Oleocanthal, An Extra-Virgin Olive Oil Component
Current Bioactive Compounds Peripartum Cardiomyopathy: Moving Towards a More Central Role of Genetics
Current Cardiology Reviews New Biotechnological Methods to Reduce Oxidative Stress in the Cardiovascular System: Focusing on the Bach1/Heme Oxygenase-1 Pathway
Current Pharmaceutical Biotechnology The Oxygen Therapy
Current Medicinal Chemistry Pharmacogenomics of Human ABC Transporter ABCC11 (MRP8): Potential Risk of Breast Cancer and Chemotherapy Failure
Anti-Cancer Agents in Medicinal Chemistry New Generation Calcium Channel Blockers in Hypertensive Treatment
Current Hypertension Reviews Role of Amylin and its Receptors in Neurodegeneration
Current Protein & Peptide Science Novel Possible Pharmaceutical Research Tools: Stem Cells, Gene Delivery and their Combination
Current Pharmaceutical Design Recent Methods in Antimalarial Susceptibility Testing
Anti-Infective Agents in Medicinal Chemistry Molecular Basis of Therapeutic Strategies for Breast Cancer
Current Drug Targets - Immune, Endocrine & Metabolic Disorders Recent Advancements in Nanodiamond Mediated Brain Targeted Drug Delivery and Bioimaging of Brain Ailments: A Holistic Review
Pharmaceutical Nanotechnology Unraveling Major Proteins of <i>Mycobacterium tuberculosis</i> Envelope
Current Proteomics Points of Therapeutic Intervention Along the Wnt Signaling Pathway in Hepatocellular Carcinoma
Anti-Cancer Agents in Medicinal Chemistry Bugs as Drugs: Understanding the Linkage between Gut Microbiota and Cancer Treatment
Current Drug Targets Radiation-Induced Pulmonary Epithelial-Mesenchymal Transition: A Review on Targeting Molecular Pathways and Mediators
Current Drug Targets Promising Use of the New Biologics in the Management of Drug-Induced Hypersensitivity Reactions: Preliminary Approaches
Endocrine, Metabolic & Immune Disorders - Drug Targets Snake Venom Metalloproteinases: Structure, Mechanism and Induced Diseases
Current Chemical Biology Serum Interleukin-33 as a Biomarker in Predicting Neonatal Sepsis in Premature Infants
Combinatorial Chemistry & High Throughput Screening Structural Patterning Used for Polyfunctional Devices in Diagnostics and for the Delivery of Therapeutics
Recent Patents on Materials Science Targeting CXCL12/CXCR4 Axis in Tumor Immunotherapy
Current Medicinal Chemistry