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 …

Microorganisms are ubiquitous and closely related to people's daily lives. Since they were
first discovered in the 19th century, researchers have shown great interest in …

Aims: The study aims to find a way to reduce the dimensionality of the dataset. Background:
Dimensionality reduction is the key issue of the machine learning process. It does not only …

Abstract Detection of Drug–Target Interactions (DTIs) is the time-consuming and laborious
experiment via biochemical approaches. Machine learning based methods have been …

The identification of therapeutic biomarkers predictive of drug response is crucial in
personalized medicine. A number of computational models to predict response of anti …

In medicine research, drug discovery aims to develop a drug to patients who will benefit from
it and try to avoid some side effects. However, the tradition experiment is time consuming …

Key message We proposed an ensemble convolutional neural network model to identify
sgRNA high on-target activity in four crops and we used one-hot encoding and k-mers for …

The identification of drug-target interactions has great significance for pharmaceutical
scientific research. Since traditional experimental methods identifying drug-target …

Drug–target interactions (DTIs) play a crucial role in target-based drug discovery and
development. Computational prediction of DTIs can effectively complement experimental …

Targeted drugs have been applied to the treatment of cancer on a large scale, and some
patients have certain therapeutic effects. It is a time-consuming task to detect drug–target …