AIMC Topic: Pharmacology

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Drug Repositioning by Integrating Known Disease-Gene and Drug-Target Associations in a Semi-supervised Learning Model.

Acta biotheoretica
Computational drug repositioning has been proven as a promising and efficient strategy for discovering new uses from existing drugs. To achieve this goal, a number of computational methods have been proposed, which are based on different data sources...

Identifying predictive features in drug response using machine learning: opportunities and challenges.

Annual review of pharmacology and toxicology
This article reviews several techniques from machine learning that can be used to study the problem of identifying a small number of features, from among tens of thousands of measured features, that can accurately predict a drug response. Prediction ...

Impact of pharmacology perception and learning strategies on academic achievement in undergraduate pharmacy students.

Scientific reports
Pharmacology is a cornerstone of pharmacy education, bridging biomedical sciences with clinical application. Understanding students' perceptions of pharmacology's relevance can influence their learning strategies and academic performance. Despite its...

Large-scale single-molecule imaging aided by artificial intelligence.

Microscopy (Oxford, England)
Single-molecule imaging analysis has been applied to study the dynamics and kinetics of molecular behaviors and interactions in living cells. In spite of its high potential as a technique to investigate the molecular mechanisms of cellular phenomena,...

The 18th FRAME Annual Lecture, October 2019: Human Trials in Pharmacology.

Alternatives to laboratory animals : ATLA
Safety and efficacy testing is a crucial part of the drug development process, and several different methods are used to obtain the necessary data (e.g. testing, animal trials and clinical trials). Our group has been investigating the potential of m...

Deep mining heterogeneous networks of biomedical linked data to predict novel drug-target associations.

Bioinformatics (Oxford, England)
MOTIVATION: A heterogeneous network topology possessing abundant interactions between biomedical entities has yet to be utilized in similarity-based methods for predicting drug-target associations based on the array of varying features of drugs and t...