Arrhythmia detection from ECG is an important area of computational ECG analysis. However, although a large number of public ECG recordings are available, most research uses only few datasets, making it difficult to estimate the generalizability of t...
Journal of chemical information and modeling
Jul 17, 2023
Drug-drug interactions (DDI) are a critical aspect of drug research that can have adverse effects on patients and can lead to serious consequences. Predicting these events accurately can significantly improve clinicians' ability to make better decisi...
BACKGROUND: Paediatric appendicitis may be challenging to diagnose, and outcomes difficult to predict. While diagnostic and prognostic scores exist, artificial intelligence (AI) may be able to assist with these tasks.
Advancements in AI have resulted in the development of sophisticated language models like ChatGPT, which can generate human-like text. While ChatGPT is useful for clarifying concepts and providing basic guidance, it has limitations. It lacks the abil...
Each year, publicly available databases are updated with new compounds from different research institutions. Positive experimental outcomes are more likely to be reported; therefore, they account for a considerable fraction of these entries. Establis...
International journal of molecular sciences
Jul 4, 2023
The prediction of drug metabolism is attracting great interest for the possibility of discarding molecules with unfavorable ADME/Tox profile at the early stage of the drug discovery process. In this context, artificial intelligence methods can genera...
We describe a freely available web server called Retention Index Predictor (RIpred) (https://ripred.ca) that rapidly and accurately predicts Gas Chromatographic Kováts Retention Indices (RI) using SMILES strings as chemical structure input. RIpred pe...
There are currently no abstract classifiers, which can be used for new diagnostic test accuracy (DTA) systematic reviews to select primary DTA study abstracts from database searches. Our goal was to develop machine-learning-based abstract classifiers...
Machine-learning-based scoring functions (MLSFs) have gained attention for their potential to improve accuracy in binding affinity prediction and structure-based virtual screening (SBVS) compared to classical SFs. Developing accurate MLSFs for SBVS r...
INTRODUCTION: Time- and resource-demanding activities related to processing individual case safety reports (ICSRs) include manual procedures to evaluate individual causality with the final goal of dismissing false-positive safety signals. Eminent exp...
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