AI Medical Compendium

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

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Deep learning of protein sequence design of protein-protein interactions.

Bioinformatics (Oxford, England)
MOTIVATION: As more data of experimentally determined protein structures are becoming available, data-driven models to describe protein sequence-structure relationships become more feasible. Within this space, the amino acid sequence design of protei...

Digitally predicting protein localization and manipulating protein activity in fluorescence images using 4D reslicing GAN.

Bioinformatics (Oxford, England)
MOTIVATION: While multi-channel fluorescence microscopy is a vital imaging method in biological studies, the number of channels that can be imaged simultaneously is limited by technical and hardware limitations such as emission spectra cross-talk. On...

Paragraph-antibody paratope prediction using graph neural networks with minimal feature vectors.

Bioinformatics (Oxford, England)
SUMMARY: The development of new vaccines and antibody therapeutics typically takes several years and requires over $1bn in investment. Accurate knowledge of the paratope (antibody binding site) can speed up and reduce the cost of this process by impr...

Neuron tracing from light microscopy images: automation, deep learning and bench testing.

Bioinformatics (Oxford, England)
MOTIVATION: Large-scale neuronal morphologies are essential to neuronal typing, connectivity characterization and brain modeling. It is widely accepted that automation is critical to the production of neuronal morphology. Despite previous survey pape...

GlycoEnzOnto: a GlycoEnzyme pathway and molecular function ontology.

Bioinformatics (Oxford, England)
MOTIVATION: The 'glycoEnzymes' include a set of proteins having related enzymatic, metabolic, transport, structural and cofactor functions. Currently, there is no established ontology to describe glycoEnzyme properties and to relate them to glycan bi...

EcoTransLearn: an R-package to easily use transfer learning for ecological studies-a plankton case study.

Bioinformatics (Oxford, England)
SUMMARY: In recent years, Deep Learning (DL) has been increasingly used in many fields, in particular in image recognition, due to its ability to solve problems where traditional machine learning algorithms fail. However, building an appropriate DL m...

DeepPerVar: a multi-modal deep learning framework for functional interpretation of genetic variants in personal genome.

Bioinformatics (Oxford, England)
MOTIVATION: Understanding the functional consequence of genetic variants, especially the non-coding ones, is important but particularly challenging. Genome-wide association studies (GWAS) or quantitative trait locus analyses may be subject to limited...

Defining the extent of gene function using ROC curvature.

Bioinformatics (Oxford, England)
MOTIVATION: Interactions between proteins help us understand how genes are functionally related and how they contribute to phenotypes. Experiments provide imperfect 'ground truth' information about a small subset of potential interactions in a specif...

Improving and evaluating deep learning models of cellular organization.

Bioinformatics (Oxford, England)
MOTIVATION: Cells contain dozens of major organelles and thousands of other structures, many of which vary extensively in their number, size, shape and spatial distribution. This complexity and variation dramatically complicates the use of both tradi...

E-SNPs&GO: embedding of protein sequence and function improves the annotation of human pathogenic variants.

Bioinformatics (Oxford, England)
MOTIVATION: The advent of massive DNA sequencing technologies is producing a huge number of human single-nucleotide polymorphisms occurring in protein-coding regions and possibly changing their sequences. Discriminating harmful protein variations fro...