Cargando…
Prediction of Drug–Target Interactions From Multi-Molecular Network Based on Deep Walk Embedding Model
Predicting drug–target interactions (DTIs) is crucial in innovative drug discovery, drug repositioning and other fields. However, there are many shortcomings for predicting DTIs using traditional biological experimental methods, such as the high-cost, time-consumption, low efficiency, and so on, whi...
Autores principales: | Chen, Zhan-Heng, You, Zhu-Hong, Guo, Zhen-Hao, Yi, Hai-Cheng, Luo, Gong-Xu, Wang, Yan-Bin |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7283956/ https://www.ncbi.nlm.nih.gov/pubmed/32582646 http://dx.doi.org/10.3389/fbioe.2020.00338 |
Ejemplares similares
-
Predicting the Internal Knee Abduction Impulse During Walking Using Deep Learning
por: Boukhennoufa, Issam, et al.
Publicado: (2022) -
Combining 3D skeleton data and deep convolutional neural network for balance assessment during walking
por: Ma, Xiangyuan, et al.
Publicado: (2023) -
DWPPI: A Deep Learning Approach for Predicting Protein–Protein Interactions in Plants Based on Multi-Source Information With a Large-Scale Biological Network
por: Pan, Jie, et al.
Publicado: (2022) -
Prediction of drug sensitivity based on multi-omics data using deep learning and similarity network fusion approaches
por: Liu, Xiao-Ying, et al.
Publicado: (2023) -
Multi-planar instability, laxity and reduced knee flexion during the support phase of walking are determinants of return to sports
por: Zhou, Tianping, et al.
Publicado: (2022)