Paper Info
Title
Modelling and Recognition of Protein Contact Networks by Multiple Kernel Learning and Dissimilarity Representations.
Abstract
Multiple kernel learning is a paradigm which employs a properly constructed chain of kernel functions able to simultaneously analyse different data or different representations of the same data. In this paper, we propose an hybrid classification system based on a linear combination of multiple kernels defined over multiple dissimilarity spaces. The core of the training procedure is the joint optimisation of kernel weights and representatives selection in the dissimilarity spaces. This equips the system with a two-fold knowledge discovery phase: by analysing the weights, it is possible to check which representations are more suitable for solving the classification problem, whereas the pivotal patterns selected as representatives can give further insights on the modelled system, possibly with the help of field-experts. The proposed classification system is tested on real proteomic data in order to predict proteins' functional role starting from their folded structure: specifically, a set of eight representations are drawn from the graph-based protein folded description. The proposed multiple kernel-based system has also been benchmarked against a clustering-based classification system also able to exploit multiple dissimilarities simultaneously. Computational results show remarkable classification capabilities and the knowledge discovery analysis is in line with current biological knowledge, suggesting the reliability of the proposed system.
Year
DOI
Venue
2020
10.3390/e22070794
ENTROPY
Keywords
DocType
Volume
dissimilarity spaces,support vector machines,kernel methods,computational biology,systems biology,protein contact networks
Journal
22
Issue
ISSN
Citations 
7
1099-4300
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Alessio Martino143.48
Enrico De Santis2505.92
Alessandro Giuliani317025.21
Antonello Rizzi436341.68