Bioinformatics: the machine learning approach
Bioinformatics: the machine learning approach
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Integrated feature analysis and fuzzy rule-based systemidentification in a neuro-fuzzy paradigm
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fast nonnegative matrix factorization and its application for protein fold recognition
EURASIP Journal on Applied Signal Processing
Neural, Parallel & Scientific Computations
Letters: Fusion of classifiers for protein fold recognition
Neurocomputing
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part II
Fast non-negative dimensionality reduction for protein fold recognition
ECML'05 Proceedings of the 16th European conference on Machine Learning
Protein fold recognition with combined SVM-RDA classifier
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I
Finding short structural motifs for re-construction of proteins 3D structure
Applied Soft Computing
FRAN and RBF-PSO as two components of a hyper framework to recognize protein folds
Computers in Biology and Medicine
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In this paper we propose several sets of new features for protein fold prediction. The first feature set consisting of 47 features uses only the sequence information. We also define four different sets of features based on hydrophobicity of amino acids. Each such set has 400 features which are motivated by folding energy modeling. To define these features we have considered pair-wise amino acids (AA) interaction potential. The effectiveness of the proposed feature sets is tested using multilayer perceptron and radial basis function networks to solve the 4 class (level 1) and 27 class (level 2) prediction problems as defined in the context of SCOP classification. Our investigation shows that such features have good discriminating powers in predicting protein folds.