AI Medical Compendium Journal:
Communications biology

Showing 101 to 110 of 154 articles

PremPLI: a machine learning model for predicting the effects of missense mutations on protein-ligand interactions.

Communications biology
Resistance to small-molecule drugs is the main cause of the failure of therapeutic drugs in clinical practice. Missense mutations altering the binding of ligands to proteins are one of the critical mechanisms that result in genetic disease and drug r...

Manifold learning analysis suggests strategies to align single-cell multimodal data of neuronal electrophysiology and transcriptomics.

Communications biology
Recent single-cell multimodal data reveal multi-scale characteristics of single cells, such as transcriptomics, morphology, and electrophysiology. However, integrating and analyzing such multimodal data to deeper understand functional genomics and ge...

Radiomics-guided deep neural networks stratify lung adenocarcinoma prognosis from CT scans.

Communications biology
Deep learning (DL) is a breakthrough technology for medical imaging with high sample size requirements and interpretability issues. Using a pretrained DL model through a radiomics-guided approach, we propose a methodology for stratifying the prognosi...

Development of deep learning-based detecting systems for pathologic myopia using retinal fundus images.

Communications biology
Globally, cases of myopia have reached epidemic levels. High myopia and pathological myopia (PM) are the leading cause of visual impairment and blindness in China, demanding a large volume of myopia screening tasks to control the rapid growing myopic...

Connecting MHC-I-binding motifs with HLA alleles via deep learning.

Communications biology
The selection of peptides presented by MHC molecules is crucial for antigen discovery. Previously, several predictors have shown impressive performance on binding affinity. However, the decisive MHC residues and their relation to the selection of bin...

GenNet framework: interpretable deep learning for predicting phenotypes from genetic data.

Communications biology
Applying deep learning in population genomics is challenging because of computational issues and lack of interpretable models. Here, we propose GenNet, a novel open-source deep learning framework for predicting phenotypes from genetic variants. In th...

Explainable artificial intelligence based analysis for interpreting infant fNIRS data in developmental cognitive neuroscience.

Communications biology
In the last decades, non-invasive and portable neuroimaging techniques, such as functional near infrared spectroscopy (fNIRS), have allowed researchers to study the mechanisms underlying the functional cognitive development of the human brain, thus f...

NetTCR-2.0 enables accurate prediction of TCR-peptide binding by using paired TCRα and β sequence data.

Communications biology
Prediction of T-cell receptor (TCR) interactions with MHC-peptide complexes remains highly challenging. This challenge is primarily due to three dominant factors: data accuracy, data scarceness, and problem complexity. Here, we showcase that "shallow...

Accelerating antibiotic discovery through artificial intelligence.

Communications biology
By targeting invasive organisms, antibiotics insert themselves into the ancient struggle of the host-pathogen evolutionary arms race. As pathogens evolve tactics for evading antibiotics, therapies decline in efficacy and must be replaced, distinguish...

Machine learning-based real-time object locator/evaluator for cryo-EM data collection.

Communications biology
In cryo-electron microscopy (cryo-EM) data collection, locating a target object is error-prone. Here, we present a machine learning-based approach with a real-time object locator named yoneoLocr using YOLO, a well-known object detection system. Imple...