Utilize este identificador para referenciar este registo:
http://hdl.handle.net/10071/17465
Autoria: | Ruano-Ordás, D. Yevseyeva, I. Basto-Fernandes, V. Méndez, J. R. Emmerichd, M. T. M. |
Data: | 2019 |
Título próprio: | Improving the drug discovery process by using multiple classifier systems |
Volume: | 121 |
Paginação: | 292 - 303 |
ISSN: | 0957-4174 |
DOI (Digital Object Identifier): | 10.1016/j.eswa.2018.12.032 |
Palavras-chave: | Drug discovery Machine learning algorithms Feature clustering Multiple classifier systems |
Resumo: | Machine learning methods have become an indispensable tool for utilizing large knowledge and data repositories in science and technology. In the context of the pharmaceutical domain, the amount of acquired knowledge about the design and synthesis of pharmaceutical agents and bioactive molecules (drugs) is enormous. The primary challenge for automatically discovering new drugs from molecular screening information is related to the high dimensionality of datasets, where a wide range of features is included for each candidate drug. Thus, the implementation of improved techniques to ensure an adequate manipulation and interpretation of data becomes mandatory. To mitigate this problem, our tool (called D2-MCS) can split homogeneously the dataset into several groups (the subset of features) and subsequently, determine the most suitable classifier for each group. Finally, the tool allows determining the biological activity of each molecule by a voting scheme. The application of the D2-MCS tool was tested on a standardized, high quality dataset gathered from ChEMBL and have shown outperformance of our tool when compare to well-known single classification models. |
Arbitragem científica: | yes |
Acesso: | Acesso Aberto |
Aparece nas coleções: | ISTAR-RI - Artigos em revistas científicas internacionais com arbitragem científica |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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Manuscript_REV2.pdf | Pós-print | 1,98 MB | Adobe PDF | Ver/Abrir |
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