Neural networks for pattern recognition
Neural networks for pattern recognition
Learning optimization in simplifying fuzzy rules
Fuzzy Sets and Systems
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Improving Performance of Similarity-Based Clustering by Feature Weight Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computers in Biology and Medicine
A comparative study on heuristic algorithms for generating fuzzydecision trees
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Improving learning accuracy of fuzzy decision trees by hybrid neural networks
IEEE Transactions on Fuzzy Systems
Maximum Ambiguity-Based Sample Selection in Fuzzy Decision Tree Induction
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Neural Networks
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Conventional machine learning methods can be used to identify protein-protein interaction sites and study the gene regulatory networks and functions. However, when applied to large datasets, the computational complexities of these methods become a major drawback. With a significantly reduced computational complexity, the Extreme Learning Machines provide an attractive balance between computational time and generalization performance. In the method proposed in this paper, after searching for interfacial residues using a dynamic strategy and extracting spatially neighboring residue profiles for a set of 563 non-redundant protein chains, we implement the interface prediction either on multi-chain sets or on single-chain sets, using the two methods Extreme Learning Machines and support vector machines for a comparable study. As a consequence, in both multi-chain and single-chain cases Extreme Learning Machines tend to obtain higher Recall values than support vector machines, and in the multi-chain case Extreme Learning Machines as well show a remarkable advantage in the computational speed.