AI Medical Compendium

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

Showing 121 to 130 of 837 articles

Clear Filters

Improving dictionary-based named entity recognition with deep learning.

Bioinformatics (Oxford, England)
MOTIVATION: Dictionary-based named entity recognition (NER) allows terms to be detected in a corpus and normalized to biomedical databases and ontologies. However, adaptation to different entity types requires new high-quality dictionaries and associ...

GORetriever: reranking protein-description-based GO candidates by literature-driven deep information retrieval for protein function annotation.

Bioinformatics (Oxford, England)
SUMMARY: The vast majority of proteins still lack experimentally validated functional annotations, which highlights the importance of developing high-performance automated protein function prediction/annotation (AFP) methods. While existing approache...

MolMVC: Enhancing molecular representations for drug-related tasks through multi-view contrastive learning.

Bioinformatics (Oxford, England)
MOTIVATION: Effective molecular representation is critical in drug development. The complex nature of molecules demands comprehensive multi-view representations, considering 1D, 2D, and 3D aspects, to capture diverse perspectives. Obtaining represent...

Predicting spatially resolved gene expression via tissue morphology using adaptive spatial GNNs.

Bioinformatics (Oxford, England)
MOTIVATION: Spatial transcriptomics technologies, which generate a spatial map of gene activity, can deepen the understanding of tissue architecture and its molecular underpinnings in health and disease. However, the high cost makes these technologie...

PEPerMINT: peptide abundance imputation in mass spectrometry-based proteomics using graph neural networks.

Bioinformatics (Oxford, England)
MOTIVATION: Accurate quantitative information about protein abundance is crucial for understanding a biological system and its dynamics. Protein abundance is commonly estimated using label-free, bottom-up mass spectrometry (MS) protocols. Here, prote...

Learning meaningful representation of single-neuron morphology via large-scale pre-training.

Bioinformatics (Oxford, England)
SUMMARY: Single-neuron morphology, the study of the structure, form, and shape of a group of specialized cells in the nervous system, is of vital importance to define the type of neurons, assess changes in neuronal development and aging and determine...

Multi-task deep latent spaces for cancer survival and drug sensitivity prediction.

Bioinformatics (Oxford, England)
MOTIVATION: Cancer is a very heterogeneous disease that can be difficult to treat without addressing the specific mechanisms driving tumour progression in a given patient. High-throughput screening and sequencing data from cancer cell-lines has drive...

Scalable CNN-based classification of selective sweeps using derived allele frequencies.

Bioinformatics (Oxford, England)
MOTIVATION: Selective sweeps can successfully be distinguished from neutral genetic data using summary statistics and likelihood-based methods that analyze single nucleotide polymorphisms (SNPs). However, these methods are sensitive to confounding fa...

HCS-hierarchical algorithm for simulation of omics datasets.

Bioinformatics (Oxford, England)
MOTIVATION: Analysis of the omics data with the help of machine learning (ML) methods is limited by small sample sizes and a large number of variables. One possible approach to deal with such data is using algorithms for feature selection and reducin...

An Ensemble Spectral Prediction (ESP) model for metabolite annotation.

Bioinformatics (Oxford, England)
MOTIVATION: A key challenge in metabolomics is annotating measured spectra from a biological sample with chemical identities. Currently, only a small fraction of measurements can be assigned identities. Two complementary computational approaches have...