Synergistic drug combination prediction in multi-input neural network / 生物医学工程学杂志
Journal of Biomedical Engineering
; (6): 676-682, 2020.
Article
de Zh
| WPRIM
| ID: wpr-828119
Bibliothèque responsable:
WPRO
ABSTRACT
Synergistic effects of drug combinations are very important in improving drug efficacy or reducing drug toxicity. However, due to the complex mechanism of action between drugs, it is expensive to screen new drug combinations through trials. It is well known that virtual screening of computational models can effectively reduce the test cost. Recently, foreign scholars successfully predicted the synergistic value of new drug combinations on cancer cell lines by using deep learning model DeepSynergy. However, DeepSynergy is a two-stage method and uses only one kind of feature as input. In this study, we proposed a new end-to-end deep learning model, MulinputSynergy which predicted the synergistic value of drug combinations by integrating gene expression, gene mutation, gene copy number characteristics of cancer cells and anticancer drug chemistry characteristics. In order to solve the problem of high dimension of features, we used convolutional neural network to reduce the dimension of gene features. Experimental results showed that the proposed model was superior to DeepSynergy deep learning model, with the mean square error decreasing from 197 to 176, the mean absolute error decreasing from 9.48 to 8.77, and the decision coefficient increasing from 0.53 to 0.58. This model could learn the potential relationship between anticancer drugs and cell lines from a variety of characteristics and locate the effective drug combinations quickly and accurately.
Mots clés
Texte intégral:
1
Indice:
WPRIM
Sujet Principal:
29935
/
Biologie informatique
/
Association médicamenteuse
/
Tumeurs
/
Antinéoplasiques
Type d'étude:
Prognostic_studies
Limites du sujet:
Humans
langue:
Zh
Texte intégral:
Journal of Biomedical Engineering
Année:
2020
Type:
Article