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.
Supervised approaches for function prediction of proteins contact networks from topological structure information / Martino, Alessio; Maiorino, Enrico; Giuliani, Alessandro; Giampieri, Mauro; Rizzi, Antonello. - 10269:(2017), pp. 285-296. [10.1007/978-3-319-59126-1_24]
Supervised approaches for function prediction of proteins contact networks from topological structure information
MARTINO, ALESSIO
;
2017
Abstract
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.File | Dimensione | Formato | |
---|---|---|---|
Martino_Post-print_Supervised_2017.pdf
Solo gestori archivio
Tipologia:
Versione dell'editore
Licenza:
DRM (Digital rights management) non definiti
Dimensione
389.99 kB
Formato
Adobe PDF
|
389.99 kB | Adobe PDF | Visualizza/Apri |
Pubblicazioni consigliate
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.