PDC-SGB: Prediction of effective drug combinations using a stochastic gradient boosting algorithm.
J Theor Biol
; 417: 1-7, 2017 03 21.
Article
em En
| MEDLINE
| ID: mdl-28099868
ABSTRACT
Combinatorial therapy is a promising strategy for combating complex diseases by improving the efficacy and reducing the side effects. To facilitate the identification of drug combinations in pharmacology, we proposed a new computational model, termed PDC-SGB, to predict effective drug combinations by integrating biological, chemical and pharmacological information based on a stochastic gradient boosting algorithm. To begin with, a set of 352 golden positive samples were collected from the public drug combination database. Then, a set of 732 dimensional feature vector involving biological, chemical and pharmaceutical information was constructed for each drug combination to describe its properties. To avoid overfitting, the maximum relevance & minimum redundancy (mRMR) method was performed to extract useful ones by removing redundant subsets. Based on the selected features, the three different type of classification algorithms were employed to build the drug combination prediction models. Our results demonstrated that the model based on the stochastic gradient boosting algorithm yield out the best performance. Furthermore, it is indicated that the feature patterns of therapy had powerful ability to discriminate effective drug combinations from non-effective ones. By analyzing various features, it is shown that the enriched features occurred frequently in golden positive samples can help predict novel drug combinations.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Algoritmos
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Processos Estocásticos
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Combinação de Medicamentos
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Bases de Dados de Produtos Farmacêuticos
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Modelos Teóricos
Tipo de estudo:
Prognostic_studies
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Risk_factors_studies
Idioma:
En
Revista:
J Theor Biol
Ano de publicação:
2017
Tipo de documento:
Article
País de afiliação:
China
País de publicação:
ENGLAND
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ESCOCIA
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GB
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GREAT BRITAIN
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INGLATERRA
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REINO UNIDO
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SCOTLAND
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UK
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UNITED KINGDOM