AI Medical Compendium

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

Showing 401 to 410 of 837 articles

Clear Filters

SPEQ: quality assessment of peptide tandem mass spectra with deep learning.

Bioinformatics (Oxford, England)
MOTIVATION: In proteomics, database search programs are routinely used for peptide identification from tandem mass spectrometry data. However, many low-quality spectra cannot be interpreted by any programs. Meanwhile, certain high-quality spectra may...

DeepSVP: integration of genotype and phenotype for structural variant prioritization using deep learning.

Bioinformatics (Oxford, England)
MOTIVATION: Structural genomic variants account for much of human variability and are involved in several diseases. Structural variants are complex and may affect coding regions of multiple genes, or affect the functions of genomic regions in differe...

InDeep: 3D fully convolutional neural networks to assist in silico drug design on protein-protein interactions.

Bioinformatics (Oxford, England)
MOTIVATION: Protein-protein interactions (PPIs) are key elements in numerous biological pathways and the subject of a growing number of drug discovery projects including against infectious diseases. Designing drugs on PPI targets remains a difficult ...

Virtifier: a deep learning-based identifier for viral sequences from metagenomes.

Bioinformatics (Oxford, England)
MOTIVATION: Viruses, the most abundant biological entities on earth, are important components of microbial communities, and as major human pathogens, they are responsible for human mortality and morbidity. The identification of viral sequences from m...

Back translation for molecule generation.

Bioinformatics (Oxford, England)
MOTIVATION: Molecule generation, which is to generate new molecules, is an important problem in bioinformatics. Typical tasks include generating molecules with given properties, molecular property improvement (i.e. improving specific properties of an...

Automated classification of cytogenetic abnormalities in hematolymphoid neoplasms.

Bioinformatics (Oxford, England)
MOTIVATION: Algorithms for classifying chromosomes, like convolutional deep neural networks (CNNs), show promise to augment cytogeneticists' workflows; however, a critical limitation is their inability to accurately classify various structural chromo...

HDMC: a novel deep learning-based framework for removing batch effects in single-cell RNA-seq data.

Bioinformatics (Oxford, England)
MOTIVATION: With the development of single-cell RNA sequencing (scRNA-seq) techniques, increasingly more large-scale gene expression datasets become available. However, to analyze datasets produced by different experiments, batch effects among differ...

Detecting spatially co-expressed gene clusters with functional coherence by graph-regularized convolutional neural network.

Bioinformatics (Oxford, England)
MOTIVATION: Clustering spatial-resolved gene expression is an essential analysis to reveal gene activities in the underlying morphological context by their functional roles. However, conventional clustering analysis does not consider gene expression ...

DeepKG: an end-to-end deep learning-based workflow for biomedical knowledge graph extraction, optimization and applications.

Bioinformatics (Oxford, England)
SUMMARY: DeepKG is an end-to-end deep learning-based workflow that helps researchers automatically mine valuable knowledge in biomedical literature. Users can utilize it to establish customized knowledge graphs in specified domains, thus facilitating...

DeepMP: a deep learning tool to detect DNA base modifications on Nanopore sequencing data.

Bioinformatics (Oxford, England)
MOTIVATION: DNA methylation plays a key role in a variety of biological processes. Recently, Nanopore long-read sequencing has enabled direct detection of these modifications. As a consequence, a range of computational methods have been developed to ...