AI Medical Compendium

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

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AFsample: improving multimer prediction with AlphaFold using massive sampling.

Bioinformatics (Oxford, England)
SUMMARY: The AlphaFold2 neural network model has revolutionized structural biology with unprecedented performance. We demonstrate that by stochastically perturbing the neural network by enabling dropout at inference combined with massive sampling, it...

ActivePPI: quantifying protein-protein interaction network activity with Markov random fields.

Bioinformatics (Oxford, England)
MOTIVATION: Protein-protein interactions (PPI) are crucial components of the biomolecular networks that enable cells to function. Biological experiments have identified a large number of PPI, and these interactions are stored in knowledge bases. Howe...

IsoFrog: a reversible jump Markov Chain Monte Carlo feature selection-based method for predicting isoform functions.

Bioinformatics (Oxford, England)
MOTIVATION: A single gene may yield several isoforms with different functions through alternative splicing. Continuous efforts are devoted to developing machine-learning methods to predict isoform functions. However, existing methods do not consider ...

MSDRP: a deep learning model based on multisource data for predicting drug response.

Bioinformatics (Oxford, England)
MOTIVATION: Cancer heterogeneity drastically affects cancer therapeutic outcomes. Predicting drug response in vitro is expected to help formulate personalized therapy regimens. In recent years, several computational models based on machine learning a...

iDeLUCS: a deep learning interactive tool for alignment-free clustering of DNA sequences.

Bioinformatics (Oxford, England)
SUMMARY: We present an interactive Deep Learning-based software tool for Unsupervised Clustering of DNA Sequences (iDeLUCS), that detects genomic signatures and uses them to cluster DNA sequences, without the need for sequence alignment or taxonomic ...

The Deep Generative Decoder: MAP estimation of representations improves modelling of single-cell RNA data.

Bioinformatics (Oxford, England)
MOTIVATION: Learning low-dimensional representations of single-cell transcriptomics has become instrumental to its downstream analysis. The state of the art is currently represented by neural network models, such as variational autoencoders, which us...

Ionmob: a Python package for prediction of peptide collisional cross-section values.

Bioinformatics (Oxford, England)
MOTIVATION: Including ion mobility separation (IMS) into mass spectrometry proteomics experiments is useful to improve coverage and throughput. Many IMS devices enable linking experimentally derived mobility of an ion to its collisional cross-section...

dRFEtools: dynamic recursive feature elimination for omics.

Bioinformatics (Oxford, England)
MOTIVATION: Advances in technology have generated larger omics datasets with potential applications for machine learning. In many datasets, however, cost and limited sample availability result in an excessively higher number of features as compared t...

Protein-ligand binding affinity prediction exploiting sequence constituent homology.

Bioinformatics (Oxford, England)
MOTIVATION: Molecular docking is a commonly used approach for estimating binding conformations and their resultant binding affinities. Machine learning has been successfully deployed to enhance such affinity estimations. Many methods of varying compl...

CMMS-GCL: cross-modality metabolic stability prediction with graph contrastive learning.

Bioinformatics (Oxford, England)
MOTIVATION: Metabolic stability plays a crucial role in the early stages of drug discovery and development. Accurately modeling and predicting molecular metabolic stability has great potential for the efficient screening of drug candidates as well as...