title: A three-way approach for protein function classification.
abstract: The knowledge of protein functions plays an essential role in understanding biological cells and has a significant impact on human life in areas such as personalized medicine, better crops and improved therapeutic interventions. Due to expense and inherent difficulty of biological experiments, intelligent methods are generally relied upon for automatic assignment of functions to proteins. The technological advancements in the field of biology are improving our understanding of biological processes and are regularly resulting in new features and characteristics that better describe the role of proteins. It is inevitable to neglect and overlook these anticipated features in designing more effective classification techniques. A key issue in this context, that is not being sufficiently addressed, is how to build effective classification models and approaches for protein function prediction by incorporating and taking advantage from the ever evolving biological information. In this article, we propose a three-way decision making approach which provides provisions for seeking and incorporating future information. We considered probabilistic rough sets based models such as Game-Theoretic Rough Sets (GTRS) and Information-Theoretic Rough Sets (ITRS) for inducing three-way decisions. An architecture of protein functions classification with probabilistic rough sets based three-way decisions is proposed and explained. Experiments are carried out on Saccharomyces cerevisiae species dataset obtained from Uniprot database with the corresponding functional classes extracted from the Gene Ontology (GO) database. The results indicate that as the level of biological information increases, the number of deferred cases are reduced while maintaining similar level of accuracy.
authors.affiliation: Department of Computer Science, National University of Computer and Emerging Sciences, Peshawar Pakistan.
authors.forename: Hafeez
authors.identifier: http://orcid.org/0000-0002-1386-5287
authors.initials: H
authors.lastname: Ur Rehman
authors.affiliation: Department of Computer Science, National University of Computer and Emerging Sciences, Peshawar Pakistan.
authors.forename: Nouman
authors.initials: N
authors.lastname: Azam
authors.affiliation: Department of Computer Science, University of Regina, Regina, SK S4S 0A2, Canada.
authors.forename: JingTao
authors.initials: J
authors.lastname: Yao
authors.affiliation: Department of Computer & Control Engineering, Politecnico di Torino, I-10129, Torino, Italy.
authors.forename: Alfredo
authors.initials: A
authors.lastname: Benso
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D030562:Databases, Protein; D015870:Gene Expression; D063990:Gene Ontology; D015233:Models, Statistical; D054730:Protein Interaction Domains and Motifs; D025941:Protein Interaction Mapping; D012441:Saccharomyces cerevisiae / Q000235:genetics / Q000378:metabolism*; D029701:Saccharomyces cerevisiae Proteins / Q000737:chemistry / Q000502:physiology*
chemical_list: D029701:Saccharomyces cerevisiae Proteins