AI Medical Compendium

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

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MotGen: a closed-loop bacterial motility control framework using generative adversarial networks.

Bioinformatics (Oxford, England)
MOTIVATION: Many organisms' survival and behavior hinge on their responses to environmental signals. While research on bacteria-directed therapeutic agents has increased, systematic exploration of real-time modulation of bacterial motility remains li...

Clustering single-cell multi-omics data via graph regularized multi-view ensemble learning.

Bioinformatics (Oxford, England)
MOTIVATION: Single-cell clustering plays a crucial role in distinguishing between cell types, facilitating the analysis of cell heterogeneity mechanisms. While many existing clustering methods rely solely on gene expression data obtained from single-...

Integrating physics in deep learning algorithms: a force field as a PyTorch module.

Bioinformatics (Oxford, England)
MOTIVATION: Deep learning algorithms applied to structural biology often struggle to converge to meaningful solutions when limited data is available, since they are required to learn complex physical rules from examples. State-of-the-art force-fields...

Advancing entity recognition in biomedicine via instruction tuning of large language models.

Bioinformatics (Oxford, England)
MOTIVATION: Large Language Models (LLMs) have the potential to revolutionize the field of Natural Language Processing, excelling not only in text generation and reasoning tasks but also in their ability for zero/few-shot learning, swiftly adapting to...

Contrastive pre-training and 3D convolution neural network for RNA and small molecule binding affinity prediction.

Bioinformatics (Oxford, England)
MOTIVATION: The diverse structures and functions inherent in RNAs present a wealth of potential drug targets. Some small molecules are anticipated to serve as leading compounds, providing guidance for the development of novel RNA-targeted therapeutic...

TemStaPro: protein thermostability prediction using sequence representations from protein language models.

Bioinformatics (Oxford, England)
MOTIVATION: Reliable prediction of protein thermostability from its sequence is valuable for both academic and industrial research. This prediction problem can be tackled using machine learning and by taking advantage of the recent blossoming of deep...

MINDG: a drug-target interaction prediction method based on an integrated learning algorithm.

Bioinformatics (Oxford, England)
MOTIVATION: Drug-target interaction (DTI) prediction refers to the prediction of whether a given drug molecule will bind to a specific target and thus exert a targeted therapeutic effect. Although intelligent computational approaches for drug target ...

AACFlow: an end-to-end model based on attention augmented convolutional neural network and flow-attention mechanism for identification of anticancer peptides.

Bioinformatics (Oxford, England)
MOTIVATION: Anticancer peptides (ACPs) have natural cationic properties and can act on the anionic cell membrane of cancer cells to kill cancer cells. Therefore, ACPs have become a potential anticancer drug with good research value and prospect.

iNGNN-DTI: prediction of drug-target interaction with interpretable nested graph neural network and pretrained molecule models.

Bioinformatics (Oxford, England)
MOTIVATION: Drug-target interaction (DTI) prediction aims to identify interactions between drugs and protein targets. Deep learning can automatically learn discriminative features from drug and protein target representations for DTI prediction, but c...

pyM2aia: Python interface for mass spectrometry imaging with focus on deep learning.

Bioinformatics (Oxford, England)
SUMMARY: Python is the most commonly used language for deep learning (DL). Existing Python packages for mass spectrometry imaging (MSI) data are not optimized for DL tasks. We, therefore, introduce pyM2aia, a Python package for MSI data analysis with...