Abstract
Schizophrenia is a complex disease, with both genetic and environmental influence. Machine learning techniques can be used to associate different genetic variations at different genes with a (schizophrenic or non-schizophrenic) phenotype. Several machine learning techniques were applied to schizophrenia data to obtain the results presented in this study. Considering these data, Quantitative Genotype – Disease Relationships (QDGRs) can be used for disease prediction. One of the best machine learning-based models obtained after this exhaustive comparative study was implemented online; this model is an artificial neural network (ANN). Thus, the tool offers the possibility to introduce Single Nucleotide Polymorphism (SNP) sequences in order to classify a patient with schizophrenia. Besides this comparative study, a method for variable selection, based on ANNs and evolutionary computation (EC), is also presented. This method uses half the number of variables as the original ANN and the variables obtained are among those found in other publications. In the future, QDGR models based on nucleic acid information could be expanded to other diseases.
Keywords: Bioinformatics, Data mining, Machine learning, Neural networks, Schizophrenia, SNP, Support vector machines.
Current Topics in Medicinal Chemistry
Title:Applied Computational Techniques on Schizophrenia Using Genetic Mutations
Volume: 13 Issue: 5
Author(s): Vanessa Aguiar-Pulido, Marcos Gestal, Carlos Fernandez-Lozano, Daniel Rivero and Cristian R. Munteanu
Affiliation:
Keywords: Bioinformatics, Data mining, Machine learning, Neural networks, Schizophrenia, SNP, Support vector machines.
Abstract: Schizophrenia is a complex disease, with both genetic and environmental influence. Machine learning techniques can be used to associate different genetic variations at different genes with a (schizophrenic or non-schizophrenic) phenotype. Several machine learning techniques were applied to schizophrenia data to obtain the results presented in this study. Considering these data, Quantitative Genotype – Disease Relationships (QDGRs) can be used for disease prediction. One of the best machine learning-based models obtained after this exhaustive comparative study was implemented online; this model is an artificial neural network (ANN). Thus, the tool offers the possibility to introduce Single Nucleotide Polymorphism (SNP) sequences in order to classify a patient with schizophrenia. Besides this comparative study, a method for variable selection, based on ANNs and evolutionary computation (EC), is also presented. This method uses half the number of variables as the original ANN and the variables obtained are among those found in other publications. In the future, QDGR models based on nucleic acid information could be expanded to other diseases.
Export Options
About this article
Cite this article as:
Aguiar-Pulido Vanessa, Gestal Marcos, Fernandez-Lozano Carlos, Rivero Daniel and R. Munteanu Cristian, Applied Computational Techniques on Schizophrenia Using Genetic Mutations, Current Topics in Medicinal Chemistry 2013; 13 (5) . https://dx.doi.org/10.2174/1568026611313050010
DOI https://dx.doi.org/10.2174/1568026611313050010 |
Print ISSN 1568-0266 |
Publisher Name Bentham Science Publisher |
Online ISSN 1873-4294 |
Call for Papers in Thematic Issues
Chemistry Based on Natural Products for Therapeutic Purposes
The development of new pharmaceuticals for a wide range of medical conditions has long relied on the identification of promising natural products (NPs). There are over sixty percent of cancer, infectious illness, and CNS disease medications that include an NP pharmacophore, according to the Food and Drug Administration. Since NP ...read more
Current Trends in Drug Discovery Based on Artificial Intelligence and Computer-Aided Drug Design
Drug development discovery has faced several challenges over the years. In fact, the evolution of classical approaches to modern methods using computational methods, or Computer-Aided Drug Design (CADD), has shown promising and essential results in any drug discovery campaign. Among these methods, molecular docking is one of the most notable ...read more
Drug Discovery in the Age of Artificial Intelligence
In the age of artificial intelligence (AI), we have witnessed a significant boom in AI techniques for drug discovery. AI techniques are increasingly integrated and accelerating the drug discovery process. These developments have not only attracted the attention of academia and industry but also raised important questions regarding the selection ...read more
From Biodiversity to Chemical Diversity: Focus of Flavonoids
Flavonoids are the largest group of polyphenols, plant secondary metabolites arising from the essential aromatic amino acid phenylalanine (or more rarely from tyrosine) via the phenylpropanoid pathway. The flavan nucleus is the basic 15-carbon skeleton of flavonoids (C6-C3-C6), which consists of two phenyl rings (A and B) and a heterocyclic ...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
-
Raloxifene and Cardiovascular Health: Its Relationship to Lipid and Glucose Metabolism, Hemostatic and Inflammation Factors and Cardiovascular Function in Postmenopausal Women
Current Pharmaceutical Design Recent Developments on Coronary Microvasculopathy after Heart Transplantation:A New Target in the Therapy of Cardiac Allograft Vasculopathy
Current Vascular Pharmacology Reduction of Sodium Intake is a Prerequisite for Preventing and Curing High Blood Pressure in Hypertensive Patients - First Part: Therapy
Current Hypertension Reviews Matrix Metalloproteinase Inhibition in Atherosclerosis and Stroke
Current Molecular Medicine Vascular Toxicity of Chemotherapeutic Agents
Current Vascular Pharmacology Diabetes and the Chronic Care Model: A Review
Current Diabetes Reviews Calcium Sensitizers in Cardiac Surgery: Who, When, How and Why?
Current Vascular Pharmacology Natural Products Towards the Discovery of Potential Future Antithrombotic Drugs
Current Pharmaceutical Design Anti-Oxidative Stress and Beyond: Multiple Functions of the Protein Glutathionylation
Protein & Peptide Letters The Entirely Subcutaneous Defibrillator (S-Icd): State of the Art and Selection of the Ideal Candidate
Current Cardiology Reviews Neuroprotective Properties of Erythropoietin in Cerebral Ischemia
Central Nervous System Agents in Medicinal Chemistry Opportunities and Challenges for Niosomes as Drug Delivery Systems
Current Drug Delivery Oxygen Sensing, Cardiac Ischemia, HIF-1α and Some Emerging Concepts
Current Cardiology Reviews Genetics of Cholesterol and Lipoprotein Metabolism
Recent Patents on Cardiovascular Drug Discovery Diabetes Therapy: Novel Patents Targeting the Glucose-Induced Insulin Secretion
Recent Patents on DNA & Gene Sequences Technological Innovations in Magnetic Resonance for Early Detection of Cardiovascular Diseases
Current Pharmaceutical Design The Role of Nicotinamide Phosphoribosyltransferase in Cerebral Ischemia
Current Topics in Medicinal Chemistry Novel 4-Oxothienopyrimidinyl Propanoic Acid Derivatives as AMPActivated Protein Kinase (AMPK) Activators
Letters in Drug Design & Discovery Biomarkers Associated with Stroke Risk in Atrial Fibrillation
Current Medicinal Chemistry Reducing Perioperative Myocardial Infarction with Anesthetic Drugs and Techniques
Current Drug Targets