AI Medical Compendium

Explore the latest research on artificial intelligence and machine learning in medicine.

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CLIN-X: pre-trained language models and a study on cross-task transfer for concept extraction in the clinical domain.

Bioinformatics (Oxford, England)
MOTIVATION: The field of natural language processing (NLP) has recently seen a large change toward using pre-trained language models for solving almost any task. Despite showing great improvements in benchmark datasets for various tasks, these models...

Scoring protein sequence alignments using deep learning.

Bioinformatics (Oxford, England)
MOTIVATION: A high-quality sequence alignment (SA) is the most important input feature for accurate protein structure prediction. For a protein sequence, there are many methods to generate a SA. However, when given a choice of more than one SA for a ...

Boost-RS: boosted embeddings for recommender systems and its application to enzyme-substrate interaction prediction.

Bioinformatics (Oxford, England)
MOTIVATION: Despite experimental and curation efforts, the extent of enzyme promiscuity on substrates continues to be largely unexplored and under documented. Providing computational tools for the exploration of the enzyme-substrate interaction space...

Relational graph convolutional networks for predicting blood-brain barrier penetration of drug molecules.

Bioinformatics (Oxford, England)
MOTIVATION: Evaluating the blood-brain barrier (BBB) permeability of drug molecules is a critical step in brain drug development. Traditional methods for the evaluation require complicated in vitro or in vivo testing. Alternatively, in silico predict...

Context-aware learning for cancer cell nucleus recognition in pathology images.

Bioinformatics (Oxford, England)
MOTIVATION: Nucleus identification supports many quantitative analysis studies that rely on nuclei positions or categories. Contextual information in pathology images refers to information near the to-be-recognized cell, which can be very helpful for...

A comprehensive evaluation of regression-based drug responsiveness prediction models, using cell viability inhibitory concentrations (IC50 values).

Bioinformatics (Oxford, England)
MOTIVATION: Predicting drug response is critical for precision medicine. Diverse methods have predicted drug responsiveness, as measured by the half-maximal drug inhibitory concentration (IC50), in cultured cells. Although IC50s are continuous, tradi...

Improving confidence in lipidomic annotations by incorporating empirical ion mobility regression analysis and chemical class prediction.

Bioinformatics (Oxford, England)
MOTIVATION: Mass spectrometry-based untargeted lipidomics aims to globally characterize the lipids and lipid-like molecules in biological systems. Ion mobility increases coverage and confidence by offering an additional dimension of separation and a ...

Interpretable-ADMET: a web service for ADMET prediction and optimization based on deep neural representation.

Bioinformatics (Oxford, England)
MOTIVATION: In the process of discovery and optimization of lead compounds, it is difficult for non-expert pharmacologists to intuitively determine the contribution of substructure to a particular property of a molecule.

Deep learning identifies and quantifies recombination hotspot determinants.

Bioinformatics (Oxford, England)
MOTIVATION: Recombination is one of the essential genetic processes for sexually reproducing organisms, which can happen more frequently in some regions, called recombination hotspots. Although several factors, such as PRDM9 binding motifs, are known...

DeepREAL: a deep learning powered multi-scale modeling framework for predicting out-of-distribution ligand-induced GPCR activity.

Bioinformatics (Oxford, England)
MOTIVATION: Drug discovery has witnessed intensive exploration of predictive modeling of drug-target physical interactions over two decades. However, a critical knowledge gap needs to be filled for correlating drug-target interactions with clinical o...