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In Silico Drug Repurposing

André Câmara Rafael Ferreira Péricles Miranda Thiago Batista Andressa Luana Arthur Lima Tássia Bastos

AI powered Drug Discovery

Machine learning in pharmacological networks covers a wide range of applications, with the potential to boost projects that focus on the development of drugs and treatments for neglected diseases.

In this project, our aim is to investigate machine learning methods to predict drug-target interactions, through the integration of heterogeneous data (Multiple Kernel Learning).

Relevant publications:

Nascimento, André C. A.; PRUDÊNCIO, RICARDO B. C. ; Costa, Ivan G. . A multiple kernel learning algorithm for drug-target interaction prediction. BMC Bioinformatics, v. 17, p. 46, 2016.

Nascimento, André C. A.; PRUDÊNCIO, RICARDO B. C. ; Costa, Ivan G. . A Drug-Target Network-Based Supervised Machine Learning Repurposing Method Allowing the Use of Multiple Heterogeneous Information Sources. In: Quentin Vanhaelen. (Org.). Methods in Molecular Biology. 01ed.New York: Springer New York, 2019, v. 01, p. 281-289..

Nascimento, André C. A.; PRUDENCIO, R. B. C. ; de Souto, Marcilio C. P. ; Costa, Ivan G. . Mining Rules for the Automatic Selection Process of Clustering Methods Applied to Cancer Gene Expression Data. In: International Conference on Artificial Neural Networks, 2009, Limassol, Cyprus. Proc. of the International Conference on Artificial Neural Networks. Berlin: Springer-Verlag, 2009.