scholarly journals A Comparative Study of Machine Learning and Evolutionary Computation Approaches for Protein Secondary Structure Classification

10.5772/19991 ◽  
2011 ◽  
Author(s):  
Cesar Manuel Vargas Benitez ◽  
Chidambaram Chidambaram ◽  
Fernanda Hembecker ◽  
Heitor Silverio
Keyword(s):  
Machine Learning ◽  
Secondary Structure ◽  
Comparative Study ◽  
Evolutionary Computation ◽  
Protein Secondary Structure

A Comparative Study on Filtering Protein Secondary Structure Prediction

2012 ◽  
Vol 9 (3) ◽  
pp. 731-739 ◽  
Author(s):  
P. Kountouris ◽  
M. Agathocleous ◽  
V. J. Promponas ◽  
G. Christodoulou ◽  
S. Hadjicostas ◽  
...  
Keyword(s):  
Secondary Structure ◽  
Comparative Study ◽  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
Protein Secondary Structure ◽  
Protein Secondary Structure Prediction

scholarly journals Hermes: an ensemble machine learning architecture for protein secondary structure prediction

2019 ◽  
Author(s):  
Larry Bliss ◽  
Ben Pascoe ◽  
Samuel K Sheppard
Keyword(s):  
Machine Learning ◽  
Protein Structure ◽  
Secondary Structure ◽  
Structure Prediction ◽  
Cross Validation ◽  
Secondary Structure Prediction ◽  
Protein Structures ◽  
Lower Boundary ◽  
Protein Secondary Structure ◽  
Homologous Proteins

AbstractMotivationProtein structure predictions, that combine theoretical chemistry and bioinformatics, are an increasingly important technique in biotechnology and biomedical research, for example in the design of novel enzymes and drugs. Here, we present a new ensemble bi-layered machine learning architecture, that directly builds on ten existing pipelines providing rapid, high accuracy, 3-State secondary structure prediction of proteins.ResultsAfter training on 1348 solved protein structures, we evaluated the model with four independent datasets: JPRED4 - compiled by the authors of the successful predictor with the same name, and CASP11, CASP12 & CASP13 - assembled by the Critical Assessment of protein Structure Prediction consortium who run biannual experiments focused on objective testing of predictors. These rigorous, pre-established protocols included 7-fold cross-validation and blind testing. This led to a mean Hermes accuracy of 95.5%, significantly (p<0.05) better than the ten previously published models analysed in this paper. Furthermore, Hermes yielded a reduction in standard deviation, lower boundary outliers, and reduced dependency on solved structures of homologous proteins, as measured by NEFF score. This architecture provides advantages over other pipelines, while remaining accessible to users at any level of bioinformatics experience.Availability and ImplementationThe source code for Hermes is freely available at: https://github.com/HermesPrediction/Hermes. This page also includes the cross-validation with corresponding models, and all training/testing data presented in this study with predictions and accuracy.

Machine Learning Techniques for Protein Secondary Structure Prediction:An Overview and Evaluation

2008 ◽  
Vol 3 (2) ◽  
pp. 74-86 ◽  
Author(s):  
Paul Yoo ◽  
Bing Zhou ◽  
Albert Zomaya
Keyword(s):  
Machine Learning ◽  
Secondary Structure ◽  
Protein Secondary Structure ◽  
Machine Learning Techniques ◽  
Learning Techniques

Machine learning approach for the prediction of protein secondary structure

1990 ◽  
Vol 216 (2) ◽  
pp. 441-457 ◽  
Author(s):  
Ross D. King ◽  
Michael J.E. Sternberg
Keyword(s):  
Machine Learning ◽  
Secondary Structure ◽  
Protein Secondary Structure ◽  
Learning Approach ◽  
Machine Learning Approach

Application of machine learning to structural molecular biology

1994 ◽  
Vol 344 (1310) ◽  
pp. 365-371 ◽  
Keyword(s):  
Machine Learning ◽  
Secondary Structure ◽  
Molecular Biology ◽  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
Inductive Logic ◽  
Protein Secondary Structure ◽  
Quantitative Structure Activity Relationship ◽  
Machine Leaning ◽  
Human Inspection

A technique of machine learning, inductive logic programming implemented in the program GOLEM, has been applied to three problems in structural molecular biology. These problems are: the prediction of protein secondary structure; the identification of rules governing the arrangement of β-sheets strands in the tertiary folding of proteins; and the modelling of a quantitative structure activity relationship (QSAR) of a series of drugs. For secondary structure prediction and the QSAR, GOLEM yielded predictions comparable with contemporary approaches including neural networks. Rules for β-strand arrangement are derived and it is planned to contrast their accuracy with those obtained by human inspection. In all three studies GOLEM discovered rules that provided insight into the stereochemistry of the system. We conclude machine leaning used together with human intervention will provide a powerful tool to discover patterns in biological sequences and structures.

A Machine Learning Approach for the Identification of Protein Secondary Structure Elements from Electron Cryo-Microscopy Density Maps

Biopolymers ◽  
2012 ◽  
Vol 97 (9) ◽  
pp. 698-708 ◽  
Author(s):  
Dong Si ◽  
Shuiwang Ji ◽  
Kamal Al Nasr ◽  
Jing He
Keyword(s):  
Machine Learning ◽  
Secondary Structure ◽  
Protein Secondary Structure ◽  
Learning Approach ◽  
Density Maps ◽  
Machine Learning Approach
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