AI Medical Compendium

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

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BridgeDPI: a novel Graph Neural Network for predicting drug-protein interactions.

Bioinformatics (Oxford, England)
MOTIVATION: Exploring drug-protein interactions (DPIs) provides a rapid and precise approach to assist in laboratory experiments for discovering new drugs. Network-based methods usually utilize a drug-protein association network and predict DPIs by t...

GraphGONet: a self-explaining neural network encapsulating the Gene Ontology graph for phenotype prediction on gene expression.

Bioinformatics (Oxford, England)
MOTIVATION: Medical care is becoming more and more specific to patients' needs due to the increased availability of omics data. The application to these data of sophisticated machine learning models, in particular deep learning (DL), can improve the ...

fastISM: performant in silico saturation mutagenesis for convolutional neural networks.

Bioinformatics (Oxford, England)
MOTIVATION: Deep-learning models, such as convolutional neural networks, are able to accurately map biological sequences to associated functional readouts and properties by learning predictive de novo representations. In silico saturation mutagenesis...

Multi-scale deep learning for the imbalanced multi-label protein subcellular localization prediction based on immunohistochemistry images.

Bioinformatics (Oxford, England)
MOTIVATION: The development of microscopic imaging techniques enables us to study protein subcellular locations from the tissue level down to the cell level, contributing to the rapid development of image-based protein subcellular location prediction...

seqgra: principled selection of neural network architectures for genomics prediction tasks.

Bioinformatics (Oxford, England)
MOTIVATION: Sequence models based on deep neural networks have achieved state-of-the-art performance on regulatory genomics prediction tasks, such as chromatin accessibility and transcription factor binding. But despite their high accuracy, their con...

TransformerGO: predicting protein-protein interactions by modelling the attention between sets of gene ontology terms.

Bioinformatics (Oxford, England)
MOTIVATION: Protein-protein interactions (PPIs) play a key role in diverse biological processes but only a small subset of the interactions has been experimentally identified. Additionally, high-throughput experimental techniques that detect PPIs are...

Pre-training graph neural networks for link prediction in biomedical networks.

Bioinformatics (Oxford, England)
MOTIVATION: Graphs or networks are widely utilized to model the interactions between different entities (e.g. proteins, drugs, etc.) for biomedical applications. Predicting potential interactions/links in biomedical networks is important for understa...

GMNN2CD: identification of circRNA-disease associations based on variational inference and graph Markov neural networks.

Bioinformatics (Oxford, England)
MOTIVATION: With the analysis of the characteristic and function of circular RNAs (circRNAs), people have realized that they play a critical role in the diseases. Exploring the relationship between circRNAs and diseases is of far-reaching significanc...

EMBER: multi-label prediction of kinase-substrate phosphorylation events through deep learning.

Bioinformatics (Oxford, England)
MOTIVATION: Kinase-catalyzed phosphorylation of proteins forms the backbone of signal transduction within the cell, enabling the coordination of numerous processes such as the cell cycle, apoptosis, and differentiation. Although on the order of 105 p...

PST-PRNA: prediction of RNA-binding sites using protein surface topography and deep learning.

Bioinformatics (Oxford, England)
MOTIVATION: Protein-RNA interactions play essential roles in many biological processes, including pre-mRNA processing, post-transcriptional gene regulation and RNA degradation. Accurate identification of binding sites on RNA-binding proteins (RBPs) i...