AI Medical Compendium

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

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AFTGAN: prediction of multi-type PPI based on attention free transformer and graph attention network.

Bioinformatics (Oxford, England)
MOTIVATION: Protein-protein interaction (PPI) networks and transcriptional regulatory networks are critical in regulating cells and their signaling. A thorough understanding of PPIs can provide more insights into cellular physiology at normal and dis...

CAPLA: improved prediction of protein-ligand binding affinity by a deep learning approach based on a cross-attention mechanism.

Bioinformatics (Oxford, England)
MOTIVATION: Accurate and rapid prediction of protein-ligand binding affinity is a great challenge currently encountered in drug discovery. Recent advances have manifested a promising alternative in applying deep learning-based computational approache...

Adversarial dense graph convolutional networks for single-cell classification.

Bioinformatics (Oxford, England)
MOTIVATION: In single-cell transcriptomics applications, effective identification of cell types in multicellular organisms and in-depth study of the relationships between genes has become one of the main goals of bioinformatics research. However, dat...

Accurately modeling biased random walks on weighted networks using node2vec.

Bioinformatics (Oxford, England)
MOTIVATION: Accurately representing biological networks in a low-dimensional space, also known as network embedding, is a critical step in network-based machine learning and is carried out widely using node2vec, an unsupervised method based on biased...

HelixGAN a deep-learning methodology for conditional de novo design of α-helix structures.

Bioinformatics (Oxford, England)
MOTIVATION: Protein and peptide engineering has become an essential field in biomedicine with therapeutics, diagnostics and synthetic biology applications. Helices are both abundant structural feature in proteins and comprise a major portion of bioac...

3D-equivariant graph neural networks for protein model quality assessment.

Bioinformatics (Oxford, England)
MOTIVATION: Quality assessment (QA) of predicted protein tertiary structure models plays an important role in ranking and using them. With the recent development of deep learning end-to-end protein structure prediction techniques for generating highl...

DFinder: a novel end-to-end graph embedding-based method to identify drug-food interactions.

Bioinformatics (Oxford, England)
MOTIVATION: Drug-food interactions (DFIs) occur when some constituents of food affect the bioaccessibility or efficacy of the drug by involving in drug pharmacodynamic and/or pharmacokinetic processes. Many computational methods have achieved remarka...

Sc2Mol: a scaffold-based two-step molecule generator with variational autoencoder and transformer.

Bioinformatics (Oxford, England)
MOTIVATION: Finding molecules with desired pharmaceutical properties is crucial in drug discovery. Generative models can be an efficient tool to find desired molecules through the distribution learned by the model to approximate given training data. ...

Discovering misannotated lncRNAs using deep learning training dynamics.

Bioinformatics (Oxford, England)
MOTIVATION: Recent experimental evidence has shown that some long non-coding RNAs (lncRNAs) contain small open reading frames (sORFs) that are translated into functional micropeptides, suggesting that these lncRNAs are misannotated as non-coding. Cur...

A benchmark for automatic medical consultation system: frameworks, tasks and datasets.

Bioinformatics (Oxford, England)
MOTIVATION: In recent years, interest has arisen in using machine learning to improve the efficiency of automatic medical consultation and enhance patient experience. In this article, we propose two frameworks to support automatic medical consultatio...