Dissimilarity spaces, along with feature reduction/ selection techniques, are among the mainstream approaches when dealing with pattern recognition problems in structured (and possibly non-metric) domains. In this work, we aim at investigating dissimilarity space representations in a biology-related application, namely protein function classification, as proteins are a seminal example of structured data given their primary and tertiary structures. Specifically, we propose two different analyses relying on both the complete dissimilarity matrix and a dimensionally-reduced version of the complete dissimilarity matrix, thereby casting the pattern recognition problem from structured domains towards real-valued feature vectors, for which any standard classification algorithm can be used. A third, hybrid, analysis uses a clustering-based one-class classifier exploiting different representations. First results conducted on a subset of the Escherichia coli proteome are promising and some of the analyses presented in this work may also dually suit field-experts, further bridging the gap between natural sciences and computational intelligence techniques.

Dissimilarity space representations and automatic feature selection for protein function prediction / De Santis, Enrico; Martino, Alessio; Rizzi, Antonello; Mascioli, Fabio Massimo Frattale. - (2018), pp. 1-8. (Intervento presentato al convegno International Joint Conference on Neural Networks (IJCNN) 2018 tenutosi a Rio de Janeiro; Brazil) [10.1109/IJCNN.2018.8489115].

Dissimilarity space representations and automatic feature selection for protein function prediction

De Santis, Enrico;Martino, Alessio;Rizzi, Antonello;Mascioli, Fabio Massimo Frattale
2018

Abstract

Dissimilarity spaces, along with feature reduction/ selection techniques, are among the mainstream approaches when dealing with pattern recognition problems in structured (and possibly non-metric) domains. In this work, we aim at investigating dissimilarity space representations in a biology-related application, namely protein function classification, as proteins are a seminal example of structured data given their primary and tertiary structures. Specifically, we propose two different analyses relying on both the complete dissimilarity matrix and a dimensionally-reduced version of the complete dissimilarity matrix, thereby casting the pattern recognition problem from structured domains towards real-valued feature vectors, for which any standard classification algorithm can be used. A third, hybrid, analysis uses a clustering-based one-class classifier exploiting different representations. First results conducted on a subset of the Escherichia coli proteome are promising and some of the analyses presented in this work may also dually suit field-experts, further bridging the gap between natural sciences and computational intelligence techniques.
2018
International Joint Conference on Neural Networks (IJCNN) 2018
protein function prediction; dissimilarity space representations; automatic feature selection; one class classification; genetic algorithms, support vector machines
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
Dissimilarity space representations and automatic feature selection for protein function prediction / De Santis, Enrico; Martino, Alessio; Rizzi, Antonello; Mascioli, Fabio Massimo Frattale. - (2018), pp. 1-8. (Intervento presentato al convegno International Joint Conference on Neural Networks (IJCNN) 2018 tenutosi a Rio de Janeiro; Brazil) [10.1109/IJCNN.2018.8489115].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1202075
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