Drug–target interaction prediction with bipartite local models and hubness-aware regression
Computational prediction of drug–target interactions is an essential task with various
applications in the pharmaceutical industry, such as adverse effect prediction or drug …
applications in the pharmaceutical industry, such as adverse effect prediction or drug …
Revealing drug-target interactions with computational models and algorithms
Background: Identifying possible drug-target interactions (DTIs) has become an important
task in drug research and development. Although high-throughput screening is becoming …
task in drug research and development. Although high-throughput screening is becoming …
A machine learning approach for drug‐target interaction prediction using wrapper feature selection and class balancing
Drug‐Target interaction (DTI) plays a crucial role in drug discovery, drug repositioning and
understanding the drug side effects which helps to identify new therapeutic profiles for …
understanding the drug side effects which helps to identify new therapeutic profiles for …
A robust drug–target interaction prediction framework with capsule network and transfer learning
Drug–target interactions (DTIs) are considered a crucial component of drug design and drug
discovery. To date, many computational methods were developed for drug–target …
discovery. To date, many computational methods were developed for drug–target …
Drug–target interaction prediction from PSSM based evolutionary information
The labor-intensive and expensive experimental process of drug–target interaction
prediction has motivated many researchers to focus on in silico prediction, which leads to …
prediction has motivated many researchers to focus on in silico prediction, which leads to …
Drug–target interaction prediction through domain-tuned network-based inference
Motivation: The identification of drug–target interaction (DTI) represents a costly and time-
consuming step in drug discovery and design. Computational methods capable of predicting …
consuming step in drug discovery and design. Computational methods capable of predicting …
Predicting drug-target interactions for new drug compounds using a weighted nearest neighbor profile
T Van Laarhoven, E Marchiori - PloS one, 2013 - journals.plos.org
In silico discovery of interactions between drug compounds and target proteins is of core
importance for improving the efficiency of the laborious and costly experimental …
importance for improving the efficiency of the laborious and costly experimental …
Prediction of drug-target interaction by integrating diverse heterogeneous information source with multiple kernel learning and clustering methods
XY Yan, SW Zhang, CR He - Computational biology and chemistry, 2019 - Elsevier
Background Identification of potential drug-target interaction pairs is very important for
pharmaceutical innovation and drug discovery. Numerous machine learning-based and …
pharmaceutical innovation and drug discovery. Numerous machine learning-based and …
Positive-unlabeled learning for inferring drug interactions based on heterogeneous attributes
Background Investigating and understanding drug-drug interactions (DDIs) is important in
improving the effectiveness of clinical care. DDIs can occur when two or more drugs are …
improving the effectiveness of clinical care. DDIs can occur when two or more drugs are …
Predicting drug-target interactions via FM-DNN learning
J Wang, H Wang, X Wang, H Chang - Current Bioinformatics, 2020 - ingentaconnect.com
Background: Identifying Drug-Target Interactions (DTIs) is a major challenge for current drug
discovery and drug repositioning. Compared to traditional experimental approaches, in …
discovery and drug repositioning. Compared to traditional experimental approaches, in …