Abstract
The essentiality of proteins is a valuable characteristic in research related to the development of new drugs. Neglected diseases profit from this characteristic in their research due to the lack of investments in the search for new drugs. Among the neglected diseases, we can highlight the schistosomiasis caused by the Schistosoma mansoni organism. This organism is a major cause of infections in humans and only one drug for its treatment is recommended by the World Health Organization. This fact raises a concern about the development of drug resistance by this organism. In this context, the present work aims to identify S. mansoni essential protein candidates. The methodology uses a machine learning approach and makes use of the knowledge of protein essentiality characteristics of model organisms. Experimental results show the Random Forest algorithm achieved the best performance in predicting the protein essentiality characteristic of S. mansoni compared to the other evaluated algorithms.
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Garcia, F.P., Guedes, G.P., Belloze, K.T. (2020). Identifying Schistosoma mansoni Essential Protein Candidates Based on Machine Learning. In: Kowada, L., de Oliveira, D. (eds) Advances in Bioinformatics and Computational Biology. BSB 2019. Lecture Notes in Computer Science(), vol 11347. Springer, Cham. https://doi.org/10.1007/978-3-030-46417-2_12
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