Cargando…
Prediction of Drug Targets for Specific Diseases Leveraging Gene Perturbation Data: A Machine Learning Approach
Identification of the correct targets is a key element for successful drug development. However, there are limited approaches for predicting drug targets for specific diseases using omics data, and few have leveraged expression profiles from gene perturbations. We present a novel computational appro...
Autores principales: | Zhao, Kai, Shi, Yujia, So, Hon-Cheong |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8878225/ https://www.ncbi.nlm.nih.gov/pubmed/35213968 http://dx.doi.org/10.3390/pharmaceutics14020234 |
Ejemplares similares
-
Uncovering Clinical Risk Factors and Predicting Severe COVID-19 Cases Using UK Biobank Data: Machine Learning Approach
por: Wong, Kenneth Chi-Yin, et al.
Publicado: (2021) -
Leveraging Machine Learning Approaches for Predicting Antidepressant Treatment Response Using Electroencephalography (EEG) and Clinical Data
por: Jaworska, Natalia, et al.
Publicado: (2019) -
Causal reasoning over knowledge graphs leveraging drug-perturbed and disease-specific transcriptomic signatures for drug discovery
por: Domingo-Fernández, Daniel, et al.
Publicado: (2022) -
Possibilistic Estimation of Distributions to Leverage Sparse Data in Machine Learning
por: Tettamanzi, Andrea G. B., et al.
Publicado: (2020) -
Leveraging machine learning essentiality predictions and chemogenomic interactions to identify antifungal targets
por: Fu, Ci, et al.
Publicado: (2021)