AI Medical Compendium

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

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Multi-instance learning of graph neural networks for aqueous pKa prediction.

Bioinformatics (Oxford, England)
MOTIVATION: The acid dissociation constant (pKa) is a critical parameter to reflect the ionization ability of chemical compounds and is widely applied in a variety of industries. However, the experimental determination of pKa is intricate and time-co...

scMRA: a robust deep learning method to annotate scRNA-seq data with multiple reference datasets.

Bioinformatics (Oxford, England)
MOTIVATION: Single-cell RNA-seq (scRNA-seq) has been widely used to resolve cellular heterogeneity. After collecting scRNA-seq data, the natural next step is to integrate the accumulated data to achieve a common ontology of cell types and states. Thu...

Tiara: deep learning-based classification system for eukaryotic sequences.

Bioinformatics (Oxford, England)
MOTIVATION: With a large number of metagenomic datasets becoming available, eukaryotic metagenomics emerged as a new challenge. The proper classification of eukaryotic nuclear and organellar genomes is an essential step toward a better understanding ...

Prediction of whole-cell transcriptional response with machine learning.

Bioinformatics (Oxford, England)
MOTIVATION: Applications in synthetic and systems biology can benefit from measuring whole-cell response to biochemical perturbations. Execution of experiments to cover all possible combinations of perturbations is infeasible. In this paper, we prese...

TGSA: protein-protein association-based twin graph neural networks for drug response prediction with similarity augmentation.

Bioinformatics (Oxford, England)
MOTIVATION: Drug response prediction (DRP) plays an important role in precision medicine (e.g. for cancer analysis and treatment). Recent advances in deep learning algorithms make it possible to predict drug responses accurately based on genetic prof...

DLAB: deep learning methods for structure-based virtual screening of antibodies.

Bioinformatics (Oxford, England)
MOTIVATION: Antibodies are one of the most important classes of pharmaceuticals, with over 80 approved molecules currently in use against a wide variety of diseases. The drug discovery process for antibody therapeutic candidates however is time- and ...

Structure-aware protein-protein interaction site prediction using deep graph convolutional network.

Bioinformatics (Oxford, England)
MOTIVATION: Protein-protein interactions (PPI) play crucial roles in many biological processes, and identifying PPI sites is an important step for mechanistic understanding of diseases and design of novel drugs. Since experimental approaches for PPI ...

Precise estimation of residue relative solvent accessible area from Cα atom distance matrix using a deep learning method.

Bioinformatics (Oxford, England)
MOTIVATION: The solvent accessible surface is an essential structural property measure related to the protein structure and protein function. Relative solvent accessible area (RSA) is a standard measure to describe the degree of residue exposure in t...

DeepAc4C: a convolutional neural network model with hybrid features composed of physicochemical patterns and distributed representation information for identification of N4-acetylcytidine in mRNA.

Bioinformatics (Oxford, England)
MOTIVATION: N4-acetylcytidine (ac4C) is the only acetylation modification that has been characterized in eukaryotic RNA, and is correlated with various human diseases. Laboratory identification of ac4C is complicated by factors, such as sample hydrol...

DeepSec: a deep learning framework for secreted protein discovery in human body fluids.

Bioinformatics (Oxford, England)
MOTIVATION: Human proteins that are secreted into different body fluids from various cells and tissues can be promising disease indicators. Modern proteomics research empowered by both qualitative and quantitative profiling techniques has made great ...