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Predicting protein functions via interaction prediction

Grant number: 17/13218-5
Support Opportunities:Scholarships abroad - Research Internship - Scientific Initiation
Effective date (Start): September 01, 2017
Effective date (End): December 31, 2017
Field of knowledge:Physical Sciences and Mathematics - Computer Science
Principal Investigator:Ricardo Cerri
Grantee:Bruna Zamith Santos
Supervisor: Celine Vens
Host Institution: Centro de Ciências Exatas e de Tecnologia (CCET). Universidade Federal de São Carlos (UFSCAR). São Carlos , SP, Brazil
Research place: University of Leuven, Kulak Kortrijk (KU Leuven), Belgium  
Associated to the scholarship:16/25078-0 - Hierarchical classification of transposable elements and protein functions making use of machine learning, BP.IC


Proteins are macro-molecules responsible for virtually every task necessary for the maintenance of cells, having a fundamental role in the behavior and regulation of organisms. Advances in the area of Molecular Biology have allowed an almost complete listing of the proteins that make up the organisms. However, there are a large number of proteins whose function is still unknown, opening space for a new research focus in Molecular Biology. Usually, protein function prediction is performed using homology-based Bioinformatic tools, comparing a sequence with a database with many sequences belonging to previously known functions. This is a limited strategy, since it ignores the sequences' biochemical properties, and also the hierarchical relationships that may exist between the different classes. In the literature, the use of Machine Learning for the protein function prediction has shown to be promising, obtaining significant advances regarding the use of homology and other methods. Making use of Machine Learning, it is possible to model the protein function prediction problem a Hierarchical Multi-Label Classification problem, due to the fact that protein functions are hierarchically organized. This project proposes modeling the protein function prediction problem as a hierarchical multi-label classification problem through interaction data. Interaction data are characterized by two sets of objects, each described by their own set of features, which makes it possible to predict the interactions between two instances. More precisely, we will extend a decision tree learner, developed for interaction prediction, to the hierarchical multi-label classification context. (AU)

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Scientific publications
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
SANTOS, BRUNA Z.; NAKANO, FELIPE K.; CERRI, RICARDO; VENS, CELINE; HUTTER, F; KERSTING, K; LIJFFIJT, J; VALERA, I. Predictive Bi-clustering Trees for Hierarchical Multi-label Classification. MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES, ECML PKDD 2020, PT III, v. 12459, p. 18-pg., . (16/25078-0, 17/13218-5)

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