AI Medical Compendium

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

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Hierarchical deep learning for predicting GO annotations by integrating protein knowledge.

Bioinformatics (Oxford, England)
MOTIVATION: Experimental testing and manual curation are the most precise ways for assigning Gene Ontology (GO) terms describing protein functions. However, they are expensive, time-consuming and cannot cope with the exponential growth of data genera...

Insights into performance evaluation of compound-protein interaction prediction methods.

Bioinformatics (Oxford, England)
MOTIVATION: Machine-learning-based prediction of compound-protein interactions (CPIs) is important for drug design, screening and repurposing. Despite numerous recent publication with increasing methodological sophistication claiming consistent impro...

GNN-SubNet: disease subnetwork detection with explainable graph neural networks.

Bioinformatics (Oxford, England)
MOTIVATION: The tremendous success of graphical neural networks (GNNs) already had a major impact on systems biology research. For example, GNNs are currently being used for drug target recognition in protein-drug interaction networks, as well as for...

DistilProtBert: a distilled protein language model used to distinguish between real proteins and their randomly shuffled counterparts.

Bioinformatics (Oxford, England)
SUMMARY: Recently, deep learning models, initially developed in the field of natural language processing (NLP), were applied successfully to analyze protein sequences. A major drawback of these models is their size in terms of the number of parameter...

Multidrug representation learning based on pretraining model and molecular graph for drug interaction and combination prediction.

Bioinformatics (Oxford, England)
MOTIVATION: Approaches for the diagnosis and treatment of diseases often adopt the multidrug therapy method because it can increase the efficacy or reduce the toxic side effects of drugs. Using different drugs simultaneously may trigger unexpected ph...

Human mitochondrial protein complexes revealed by large-scale coevolution analysis and deep learning-based structure modeling.

Bioinformatics (Oxford, England)
MOTIVATION: Recent development of deep-learning methods has led to a breakthrough in the prediction accuracy of 3D protein structures. Extending these methods to protein pairs is expected to allow large-scale detection of protein-protein interactions...

Prediction of HIV sensitivity to monoclonal antibodies using aminoacid sequences and deep learning.

Bioinformatics (Oxford, England)
MOTIVATION: Knowing the sensitivity of a viral strain versus a monoclonal antibody is of interest for HIV vaccine development and therapy. The HIV strains vary in their resistance to antibodies, and the accurate prediction of virus-antibody sensitivi...

BioADAPT-MRC: adversarial learning-based domain adaptation improves biomedical machine reading comprehension task.

Bioinformatics (Oxford, England)
MOTIVATION: Biomedical machine reading comprehension (biomedical-MRC) aims to comprehend complex biomedical narratives and assist healthcare professionals in retrieving information from them. The high performance of modern neural network-based MRC sy...

LanceOtron: a deep learning peak caller for genome sequencing experiments.

Bioinformatics (Oxford, England)
MOTIVATION: Genome sequencing experiments have revolutionized molecular biology by allowing researchers to identify important DNA-encoded elements genome wide. Regions where these elements are found appear as peaks in the analog signal of an assay's ...

Predicting and explaining the impact of genetic disruptions and interactions on organismal viability.

Bioinformatics (Oxford, England)
MOTIVATION: Existing computational models can predict single- and double-mutant fitness but they do have limitations. First, they are often tested via evaluation metrics that are inappropriate for imbalanced datasets. Second, all of them only predict...