TY - GEN
T1 - Supervised approaches for function prediction of proteins contact networks from topological structure information
AU - Martino, Alessio
AU - Maiorino, Enrico
AU - Giuliani, Alessandro
AU - Giampieri, Mauro
AU - Rizzi, Antonello
PY - 2017/1/1
Y1 - 2017/1/1
N2 - The role performed by a protein is directly connected to its physico-chemical structure. How the latter affects the behaviour of these molecules is still an open research topic. In this paper we consider a subset of the Escherichia Coli proteome where each protein is represented through the spectral characteristics of its residue contact network and its physiological function is encoded by a suitable class label. By casting this problem as a machine learning task, we aim at assessing whether a relation exists between such spectral properties and the protein’s function. To this end we adopted a set of supervised learning techniques, possibly optimised by means of genetic algorithms. First results are promising and they show that such high-level spectral representation contains enough information in order to discriminate among functional classes. Our experiments pave the way for further research and analysis.
AB - The role performed by a protein is directly connected to its physico-chemical structure. How the latter affects the behaviour of these molecules is still an open research topic. In this paper we consider a subset of the Escherichia Coli proteome where each protein is represented through the spectral characteristics of its residue contact network and its physiological function is encoded by a suitable class label. By casting this problem as a machine learning task, we aim at assessing whether a relation exists between such spectral properties and the protein’s function. To this end we adopted a set of supervised learning techniques, possibly optimised by means of genetic algorithms. First results are promising and they show that such high-level spectral representation contains enough information in order to discriminate among functional classes. Our experiments pave the way for further research and analysis.
KW - Normalised laplacian matrix
KW - Pattern recognition
KW - Protein contact networks
KW - Supervised learning
KW - Support vector machines
UR - http://www.scopus.com/inward/record.url?scp=85020468027&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85020468027&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-59126-1_24
DO - 10.1007/978-3-319-59126-1_24
M3 - Conference contribution
AN - SCOPUS:85020468027
SN - 9783319591254
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 285
EP - 296
BT - Image Analysis - 20th Scandinavian Conference, SCIA 2017, Proceedings
PB - Springer Verlag
T2 - 20th Scandinavian Conference on Image Analysis, SCIA 2017
Y2 - 12 June 2017 through 14 June 2017
ER -