Machine learning approaches and databases for prediction of drug–target interaction: a survey paper
The task of predicting the interactions between drugs and targets plays a key role in the
process of drug discovery. There is a need to develop novel and efficient prediction …
process of drug discovery. There is a need to develop novel and efficient prediction …
Deep learning in the biomedical applications: Recent and future status
Deep neural networks represent, nowadays, the most effective machine learning technology
in biomedical domain. In this domain, the different areas of interest concern the Omics (study …
in biomedical domain. In this domain, the different areas of interest concern the Omics (study …
Affinity2Vec: drug-target binding affinity prediction through representation learning, graph mining, and machine learning
Drug-target interaction (DTI) prediction plays a crucial role in drug repositioning and virtual
drug screening. Most DTI prediction methods cast the problem as a binary classification task …
drug screening. Most DTI prediction methods cast the problem as a binary classification task …
Drug targets for COVID-19 therapeutics: Ongoing global efforts
A Saxena - Journal of biosciences, 2020 - Springer
The current global pandemic COVID-19 caused by the SARS-CoV-2 virus has already
inflicted insurmountable damage both to the human lives and global economy. There is an …
inflicted insurmountable damage both to the human lives and global economy. There is an …
DTi2Vec: Drug–target interaction prediction using network embedding and ensemble learning
Drug–target interaction (DTI) prediction is a crucial step in drug discovery and repositioning
as it reduces experimental validation costs if done right. Thus, developing in-silico methods …
as it reduces experimental validation costs if done right. Thus, developing in-silico methods …
DTiGEMS+: drug–target interaction prediction using graph embedding, graph mining, and similarity-based techniques
In silico prediction of drug–target interactions is a critical phase in the sustainable drug
development process, especially when the research focus is to capitalize on the …
development process, especially when the research focus is to capitalize on the …
Drug–target interaction prediction based on protein features, using wrapper feature selection
Drug–target interaction prediction is a vital stage in drug development, involving lots of
methods. Experimental methods that identify these relationships on the basis of clinical …
methods. Experimental methods that identify these relationships on the basis of clinical …
MCL-DTI: using drug multimodal information and bi-directional cross-attention learning method for predicting drug–target interaction
Background Prediction of drug–target interaction (DTI) is an essential step for drug discovery
and drug reposition. Traditional methods are mostly time-consuming and labor-intensive …
and drug reposition. Traditional methods are mostly time-consuming and labor-intensive …
BE-DTI': Ensemble framework for drug target interaction prediction using dimensionality reduction and active learning
Background and objective Drug-target interaction prediction plays an intrinsic role in the
drug discovery process. Prediction of novel drugs and targets helps in identifying optimal …
drug discovery process. Prediction of novel drugs and targets helps in identifying optimal …
[PDF][PDF] Aplicaciones de las redes neuronales y el deep learning a la ingeniería biomédica
JL Sarmiento-Ramos - Revista UIS Ingenierías, 2020 - redalyc.org
Hoy en día, las redes neuronales artificiales y el deep learning, son dos de las herramientas
más poderosas del aprendizaje de máquina, que tienen por objetivo desarrollar sistemas …
más poderosas del aprendizaje de máquina, que tienen por objetivo desarrollar sistemas …