AI Medical Compendium

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

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AutoDC: an automatic machine learning framework for disease classification.

Bioinformatics (Oxford, England)
MOTIVATION: The emergence of next-generation sequencing techniques opens up tremendous opportunities for researchers to uncover the basic mechanisms of disease at the molecular level. Recently, automatic machine learning (AutoML) frameworks have been...

A graph neural network approach for molecule carcinogenicity prediction.

Bioinformatics (Oxford, England)
MOTIVATION: Molecular carcinogenicity is a preventable cause of cancer, but systematically identifying carcinogenic compounds, which involves performing experiments on animal models, is expensive, time consuming and low throughput. As a result, carci...

Scaling multi-instance support vector machine to breast cancer detection on the BreaKHis dataset.

Bioinformatics (Oxford, England)
MOTIVATION: Breast cancer is a type of cancer that develops in breast tissues, and, after skin cancer, it is the most commonly diagnosed cancer in women in the United States. Given that an early diagnosis is imperative to prevent breast cancer progre...

SPARSE: a sparse hypergraph neural network for learning multiple types of latent combinations to accurately predict drug-drug interactions.

Bioinformatics (Oxford, England)
MOTIVATION: Predicting side effects of drug-drug interactions (DDIs) is an important task in pharmacology. The state-of-the-art methods for DDI prediction use hypergraph neural networks to learn latent representations of drugs and side effects to exp...

MLGL-MP: a Multi-Label Graph Learning framework enhanced by pathway interdependence for Metabolic Pathway prediction.

Bioinformatics (Oxford, England)
MOTIVATION: During lead compound optimization, it is crucial to identify pathways where a drug-like compound is metabolized. Recently, machine learning-based methods have achieved inspiring progress to predict potential metabolic pathways for drug-li...

An approachable, flexible and practical machine learning workshop for biologists.

Bioinformatics (Oxford, England)
SUMMARY: The increasing prevalence and importance of machine learning in biological research have created a need for machine learning training resources tailored towards biological researchers. However, existing resources are often inaccessible, infe...

BITES: balanced individual treatment effect for survival data.

Bioinformatics (Oxford, England)
MOTIVATION: Estimating the effects of interventions on patient outcome is one of the key aspects of personalized medicine. Their inference is often challenged by the fact that the training data comprises only the outcome for the administered treatmen...

A LASSO-based approach to sample sites for phylogenetic tree search.

Bioinformatics (Oxford, England)
MOTIVATION: In recent years, full-genome sequences have become increasingly available and as a result many modern phylogenetic analyses are based on very long sequences, often with over 100 000 sites. Phylogenetic reconstructions of large-scale align...

InterPepScore: a deep learning score for improving the FlexPepDock refinement protocol.

Bioinformatics (Oxford, England)
MOTIVATION: Interactions between peptide fragments and protein receptors are vital to cell function yet difficult to experimentally determine in structural details of. As such, many computational methods have been developed to aid in peptide-protein ...

Small molecule generation via disentangled representation learning.

Bioinformatics (Oxford, England)
MOTIVATION: Expanding our knowledge of small molecules beyond what is known in nature or designed in wet laboratories promises to significantly advance cheminformatics, drug discovery, biotechnology and material science. In silico molecular design re...