Updated review of advances in microRNAs and complex diseases: taxonomy, trends and challenges of computational models
Since the problem proposed in late 2000s, microRNA–disease association (MDA)
predictions have been implemented based on the data fusion paradigm. Integrating diverse …
predictions have been implemented based on the data fusion paradigm. Integrating diverse …
Biomedical data and computational models for drug repositioning: a comprehensive review
Drug repositioning can drastically decrease the cost and duration taken by traditional drug
research and development while avoiding the occurrence of unforeseen adverse events …
research and development while avoiding the occurrence of unforeseen adverse events …
Prediction of synthesis of 2D metal carbides and nitrides (MXenes) and their precursors with positive and unlabeled machine learning
Growing interest in the potential applications of two-dimensional (2D) materials has fueled
advancement in the identification of 2D systems with exotic properties. Increasingly, the …
advancement in the identification of 2D systems with exotic properties. Increasingly, the …
[HTML][HTML] DTITR: End-to-end drug–target binding affinity prediction with transformers
The accurate identification of Drug–Target Interactions (DTIs) remains a critical turning point
in drug discovery and understanding of the binding process. Despite recent advances in …
in drug discovery and understanding of the binding process. Despite recent advances in …
DTI-MLCD: predicting drug-target interactions using multi-label learning with community detection method
Identifying drug-target interactions (DTIs) is an important step for drug discovery and drug
repositioning. To reduce the experimental cost, a large number of computational …
repositioning. To reduce the experimental cost, a large number of computational …
OPAL: prediction of MoRF regions in intrinsically disordered protein sequences
Motivation Intrinsically disordered proteins lack stable 3-dimensional structure and play a
crucial role in performing various biological functions. Key to their biological function are the …
crucial role in performing various biological functions. Key to their biological function are the …
[HTML][HTML] TAG-DTA: Binding-region-guided strategy to predict drug-target affinity using transformers
The proper assessment of target-specific compound selectivity is paramount in the drug
discovery context, promoting the identification of drug-target interactions (DTIs) and the …
discovery context, promoting the identification of drug-target interactions (DTIs) and the …
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 …
Recent advances in the machine learning-based drug-target interaction prediction
W Zhang, W Lin, D Zhang, S Wang… - Current drug …, 2019 - ingentaconnect.com
Background: The identification of drug-target interactions is a crucial issue in drug discovery.
In recent years, researchers have made great efforts on the drug-target interaction …
In recent years, researchers have made great efforts on the drug-target interaction …
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 …