Abstract
It is important to understand the cause of amyloid illnesses by predicting the short protein fragments capable of forming amyloid-like fibril motifs aiding in the discovery of sequence-targeted anti-aggregation drugs. It is extremely desirable to design computational tools to provide affordable in silico predictions owing to the limitations of molecular techniques for their identification. In this research article, we tried to study, from a machine learning perspective, the performance of several machine learning classifiers that use heterogenous features based on biochemical and biophysical properties of amino acids to discriminate between amyloidogenic and non-amyloidogenic regions in peptides. Four conventional machine learning classifiers namely Support Vector Machine, Neural network, Decision tree and Random forest were trained and tested to find the best classifier that fits the problem domain well. Prior to classification, novel implementations of two biologically-inspired feature optimization techniques based on evolutionary algorithms and methodologies that mimic social life and a multivariate method based on projection are utilized in order to remove the unimportant and uninformative features. Among the dimenionality reduction algorithms considered under the study, prediction results show that algorithms based on evolutionary computation is the most effective. SVM best suits the problem domain in its fitment among the classifiers considered. The best classifier is also compared with an online predictor to evidence the equilibrium maintained between true positive rates and false positive rates in the proposed classifier. This exploratory study suggests that these methods are promising in providing amyloidogenity prediction and may be further extended for large-scale proteomic studies.
Keywords: Amyloid fibrils, ant colony optimization, biophysiochemical properties, decision tree, memetic algorithm, neural network, principal component analysis, random forest, support vector machine
Protein & Peptide Letters
Title:Machine Learning Study of Classifiers Trained with Biophysiochemical Properties of Amino Acids to Predict Fibril Forming Peptide Motifs
Volume: 19 Issue: 9
Author(s): Smitha Sunil Kumaran Nair, N. V. Subba Reddy and K. S. Hareesha
Affiliation:
Keywords: Amyloid fibrils, ant colony optimization, biophysiochemical properties, decision tree, memetic algorithm, neural network, principal component analysis, random forest, support vector machine
Abstract: It is important to understand the cause of amyloid illnesses by predicting the short protein fragments capable of forming amyloid-like fibril motifs aiding in the discovery of sequence-targeted anti-aggregation drugs. It is extremely desirable to design computational tools to provide affordable in silico predictions owing to the limitations of molecular techniques for their identification. In this research article, we tried to study, from a machine learning perspective, the performance of several machine learning classifiers that use heterogenous features based on biochemical and biophysical properties of amino acids to discriminate between amyloidogenic and non-amyloidogenic regions in peptides. Four conventional machine learning classifiers namely Support Vector Machine, Neural network, Decision tree and Random forest were trained and tested to find the best classifier that fits the problem domain well. Prior to classification, novel implementations of two biologically-inspired feature optimization techniques based on evolutionary algorithms and methodologies that mimic social life and a multivariate method based on projection are utilized in order to remove the unimportant and uninformative features. Among the dimenionality reduction algorithms considered under the study, prediction results show that algorithms based on evolutionary computation is the most effective. SVM best suits the problem domain in its fitment among the classifiers considered. The best classifier is also compared with an online predictor to evidence the equilibrium maintained between true positive rates and false positive rates in the proposed classifier. This exploratory study suggests that these methods are promising in providing amyloidogenity prediction and may be further extended for large-scale proteomic studies.
Export Options
About this article
Cite this article as:
Sunil Kumaran Nair Smitha, V. Subba Reddy N. and S. Hareesha K., Machine Learning Study of Classifiers Trained with Biophysiochemical Properties of Amino Acids to Predict Fibril Forming Peptide Motifs, Protein & Peptide Letters 2012; 19 (9) . https://dx.doi.org/10.2174/092986612802084429
DOI https://dx.doi.org/10.2174/092986612802084429 |
Print ISSN 0929-8665 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-5305 |

- Author Guidelines
- Bentham Author Support Services (BASS)
- 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
-
Neurostimulation in Clinical and Sub-clinical Eating Disorders: A Systematic Update of the Literature
Current Neuropharmacology Glycolipid Stimulators for NKT Cells Bearing Invariant Vα19-Jα33 TCR α Chains
Mini-Reviews in Medicinal Chemistry Propofol Pretreatment Prevents Oxygen-Glucose Deprivation/Reoxygenation (OGD/R)-induced Inflammation Through Nuclear Transcription Factor κB (NF-κB) Pathway in Neuroblastoma Cells
Current Neurovascular Research Novel Therapeutic Strategy in the Management of COPD: A Systems Medicine Approach
Current Medicinal Chemistry The Role of COX-2 in Acute Pain and the Use of Selective COX-2 Inhibitors for Acute Pain Relief
Current Pharmaceutical Design A Review of Inadequate and Excessive Weight Gain in Pregnancy
Current Women`s Health Reviews The Relationship of Mother's Spiritual Well-being and Forgiveness with Mother-to-infant Attachment in Women Referring to Maternity Hospital Affiliated to Shiraz University of Medical Sciences
Current Women`s Health Reviews Potential Application of Centrifuges to Protect the CNS in Space and on Earth
Current Alzheimer Research Editorial [ Monitoring Drugs of Abuse in Wastewater and Air ]
Current Drug Abuse Reviews Therapeutic Potential of Co-enzyme Q10 in Retinal Diseases
Current Medicinal Chemistry Mitochondrial Abnormalities in a Streptozotocin-Induced Rat Model of Sporadic Alzheimer's Disease
Current Alzheimer Research Hepatocyte Growth Factor (HGF): Neurotrophic Functions and Therapeutic Implications for Neuronal Injury/Diseases
Current Signal Transduction Therapy Nitric Oxide: Cancer Target or Anticancer Agent?
Current Cancer Drug Targets Vasculogenic and Angiogenic Pathways in Moyamoya Disease
Current Medicinal Chemistry STIP Regulates ERK1/2 Signaling Pathway Involved in Interaction with PP1γ in Lymphoblastic Leukemia
Current Molecular Medicine The Association of Hot/Cold Status of Temperament with Depression and Hopelessness Scores in Females
Current Traditional Medicine Nanocarriers for Tracking and Treating Diseases
Current Medicinal Chemistry Nose to Brain Delivery of Nanoformulations for Neurotherapeutics in Parkinson’s Disease: Defining the Preclinical, Clinical and Toxicity Issues
Current Drug Delivery A Comprehensive Review of Monoamine Oxidase-A Inhibitors in their Syntheses and Potencies
Combinatorial Chemistry & High Throughput Screening Regulators of Chemokine Receptor Activity as Promising Anticancer Therapeutics
Current Cancer Drug Targets