Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Approximation spaces and information granulation
Transactions on Rough Sets III
Low-complexity fuzzy relational clustering algorithms for Web mining
IEEE Transactions on Fuzzy Systems
Rough fuzzy MLP: knowledge encoding and classification
IEEE Transactions on Neural Networks
Bio-basis function neural network for prediction of protease cleavage sites in proteins
IEEE Transactions on Neural Networks
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To recognize functional sites within a protein sequence, the non-numerical attributes of the sequence need encoding prior to using a pattern recognition algorithm. The success of recognition depends on the efficient coding of the biological information contained in the sequence. In this regard, a bio-basis function maps a non-numerical sequence space to a numerical feature space, based on an amino acid mutation matrix. In effect, the biological content in a sequence can be maximally utilized for analysis. One of the important issues for the bio-basis function is how to select a minimum set of bio-bases with maximum information. In this paper, we present two relational soft clustering algorithms, named rough c-medoids and fuzzy-possibilistic c-medoids, to select the most informative bio-bases. While both fuzzy and possibilistic memberships of fuzzy-possibilistic c-medoids avoid the noise sensitivity defect of fuzzy c-medoids and the coincident clusters problem of possibilistic c-medoids, the concept of lower and upper boundaries of rough c-medoids deals with uncertainty, vagueness, and incompleteness in class definition of biological data. The concept of 'degree of resemblance', based on non-gapped pairwise homology alignment score, circumvents the initialization and local minima problems of both c-medoids algorithms. In effect, it enables efficient selection of a minimum set of most informative bio-bases. The effectiveness of the algorithms, along with a comparison with other algorithms, has been demonstrated on HIV (human immunodeficiency virus) protein datasets.