AI Medical Compendium

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

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DeepTraSynergy: drug combinations using multimodal deep learning with transformers.

Bioinformatics (Oxford, England)
MOTIVATION: Screening bioactive compounds in cancer cell lines receive more attention. Multidisciplinary drugs or drug combinations have a more effective role in treatments and selectively inhibit the growth of cancer cells.

SBOannotator: a Python tool for the automated assignment of systems biology ontology terms.

Bioinformatics (Oxford, England)
MOTIVATION: The number and size of computational models in biology have drastically increased over the past years and continue to grow. Modeled networks are becoming more complex, and reconstructing them from the beginning in an exchangeable and repr...

MOViDA: multiomics visible drug activity prediction with a biologically informed neural network model.

Bioinformatics (Oxford, England)
MOTIVATION: The process of drug development is inherently complex, marked by extended intervals from the inception of a pharmaceutical agent to its eventual launch in the market. Additionally, each phase in this process is associated with a significa...

KG-Hub-building and exchanging biological knowledge graphs.

Bioinformatics (Oxford, England)
MOTIVATION: Knowledge graphs (KGs) are a powerful approach for integrating heterogeneous data and making inferences in biology and many other domains, but a coherent solution for constructing, exchanging, and facilitating the downstream use of KGs is...

AttOmics: attention-based architecture for diagnosis and prognosis from omics data.

Bioinformatics (Oxford, England)
MOTIVATION: The increasing availability of high-throughput omics data allows for considering a new medicine centered on individual patients. Precision medicine relies on exploiting these high-throughput data with machine-learning models, especially t...

CProMG: controllable protein-oriented molecule generation with desired binding affinity and drug-like properties.

Bioinformatics (Oxford, England)
MOTIVATION: Deep learning-based molecule generation becomes a new paradigm of de novo molecule design since it enables fast and directional exploration in the vast chemical space. However, it is still an open issue to generate molecules, which bind t...

Transfer learning for drug-target interaction prediction.

Bioinformatics (Oxford, England)
MOTIVATION: Utilizing AI-driven approaches for drug-target interaction (DTI) prediction require large volumes of training data which are not available for the majority of target proteins. In this study, we investigate the use of deep transfer learnin...

COmic: convolutional kernel networks for interpretable end-to-end learning on (multi-)omics data.

Bioinformatics (Oxford, England)
MOTIVATION: The size of available omics datasets is steadily increasing with technological advancement in recent years. While this increase in sample size can be used to improve the performance of relevant prediction tasks in healthcare, models that ...

PPAD: a deep learning architecture to predict progression of Alzheimer's disease.

Bioinformatics (Oxford, England)
MOTIVATION: Alzheimer's disease (AD) is a neurodegenerative disease that affects millions of people worldwide. Mild cognitive impairment (MCI) is an intermediary stage between cognitively normal state and AD. Not all people who have MCI convert to AD...

Genome-wide scans for selective sweeps using convolutional neural networks.

Bioinformatics (Oxford, England)
MOTIVATION: Recent methods for selective sweep detection cast the problem as a classification task and use summary statistics as features to capture region characteristics that are indicative of a selective sweep, thereby being sensitive to confoundi...