AI Medical Compendium

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

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ADMET-AI: a machine learning ADMET platform for evaluation of large-scale chemical libraries.

Bioinformatics (Oxford, England)
MOTIVATION: The emergence of large chemical repositories and combinatorial chemical spaces, coupled with high-throughput docking and generative AI, have greatly expanded the chemical diversity of small molecules for drug discovery. Selecting compound...

FastHPOCR: pragmatic, fast, and accurate concept recognition using the human phenotype ontology.

Bioinformatics (Oxford, England)
MOTIVATION: Human Phenotype Ontology (HPO)-based phenotype concept recognition (CR) underpins a faster and more effective mechanism to create patient phenotype profiles or to document novel phenotype-centred knowledge statements. While the increasing...

A deep learning method to predict bacterial ADP-ribosyltransferase toxins.

Bioinformatics (Oxford, England)
MOTIVATION: ADP-ribosylation is a critical modification involved in regulating diverse cellular processes, including chromatin structure regulation, RNA transcription, and cell death. Bacterial ADP-ribosyltransferase toxins (bARTTs) serve as potent v...

Integration of background knowledge for automatic detection of inconsistencies in gene ontology annotation.

Bioinformatics (Oxford, England)
MOTIVATION: Biological background knowledge plays an important role in the manual quality assurance (QA) of biological database records. One such QA task is the detection of inconsistencies in literature-based Gene Ontology Annotation (GOA). This man...

Adaptive digital tissue deconvolution.

Bioinformatics (Oxford, England)
MOTIVATION: The inference of cellular compositions from bulk and spatial transcriptomics data increasingly complements data analyses. Multiple computational approaches were suggested and recently, machine learning techniques were developed to systema...

TA-RNN: an attention-based time-aware recurrent neural network architecture for electronic health records.

Bioinformatics (Oxford, England)
MOTIVATION: Electronic health records (EHRs) represent a comprehensive resource of a patient's medical history. EHRs are essential for utilizing advanced technologies such as deep learning (DL), enabling healthcare providers to analyze extensive data...

MolLM: a unified language model for integrating biomedical text with 2D and 3D molecular representations.

Bioinformatics (Oxford, England)
MOTIVATION: The current paradigm of deep learning models for the joint representation of molecules and text primarily relies on 1D or 2D molecular formats, neglecting significant 3D structural information that offers valuable physical insight. This n...

Scalable de novo classification of antibiotic resistance of Mycobacterium tuberculosis.

Bioinformatics (Oxford, England)
MOTIVATION: World Health Organization estimates that there were over 10 million cases of tuberculosis (TB) worldwide in 2019, resulting in over 1.4 million deaths, with a worrisome increasing trend yearly. The disease is caused by Mycobacterium tuber...

CODEX: COunterfactual Deep learning for the in silico EXploration of cancer cell line perturbations.

Bioinformatics (Oxford, England)
MOTIVATION: High-throughput screens (HTS) provide a powerful tool to decipher the causal effects of chemical and genetic perturbations on cancer cell lines. Their ability to evaluate a wide spectrum of interventions, from single drugs to intricate dr...

Unveil cis-acting combinatorial mRNA motifs by interpreting deep neural network.

Bioinformatics (Oxford, England)
SUMMARY: Cis-acting mRNA elements play a key role in the regulation of mRNA stability and translation efficiency. Revealing the interactions of these elements and their impact plays a crucial role in understanding the regulation of the mRNA translati...