AI Medical Compendium Journal:
Communications biology

Showing 61 to 70 of 154 articles

COSMOS: a platform for real-time morphology-based, label-free cell sorting using deep learning.

Communications biology
Cells are the singular building blocks of life, and a comprehensive understanding of morphology, among other properties, is crucial to the assessment of underlying heterogeneity. We developed Computational Sorting and Mapping of Single Cells (COSMOS)...

Deep learning driven de novo drug design based on gastric proton pump structures.

Communications biology
Existing drugs often suffer in their effectiveness due to detrimental side effects, low binding affinity or pharmacokinetic problems. This may be overcome by the development of distinct compounds. Here, we exploit the rich structural basis of drug-bo...

A self-supervised deep learning method for data-efficient training in genomics.

Communications biology
Deep learning in bioinformatics is often limited to problems where extensive amounts of labeled data are available for supervised classification. By exploiting unlabeled data, self-supervised learning techniques can improve the performance of machine...

RNA contact prediction by data efficient deep learning.

Communications biology
On the path to full understanding of the structure-function relationship or even design of RNA, structure prediction would offer an intriguing complement to experimental efforts. Any deep learning on RNA structure, however, is hampered by the sparsit...

Integration of pre-trained protein language models into geometric deep learning networks.

Communications biology
Geometric deep learning has recently achieved great success in non-Euclidean domains, and learning on 3D structures of large biomolecules is emerging as a distinct research area. However, its efficacy is largely constrained due to the limited quantit...

Identifying the serious clinical outcomes of adverse reactions to drugs by a multi-task deep learning framework.

Communications biology
Adverse Drug Reactions (ADRs) have a direct impact on human health. As continuous pharmacovigilance and drug monitoring prove to be costly and time-consuming, computational methods have emerged as promising alternatives. However, most existing comput...

Intermediately synchronised brain states optimise trade-off between subject specificity and predictive capacity.

Communications biology
Functional connectivity (FC) refers to the statistical dependencies between activity of distinct brain areas. To study temporal fluctuations in FC within the duration of a functional magnetic resonance imaging (fMRI) scanning session, researchers hav...

Deep learning enables fast, gentle STED microscopy.

Communications biology
STED microscopy is widely used to image subcellular structures with super-resolution. Here, we report that restoring STED images with deep learning can mitigate photobleaching and photodamage by reducing the pixel dwell time by one or two orders of m...

ImmuneBuilder: Deep-Learning models for predicting the structures of immune proteins.

Communications biology
Immune receptor proteins play a key role in the immune system and have shown great promise as biotherapeutics. The structure of these proteins is critical for understanding their antigen binding properties. Here, we present ImmuneBuilder, a set of de...

Predicting 3D soft tissue dynamics from 2D imaging using physics informed neural networks.

Communications biology
Tissue dynamics play critical roles in many physiological functions and provide important metrics for clinical diagnosis. Capturing real-time high-resolution 3D images of tissue dynamics, however, remains a challenge. This study presents a hybrid phy...