AI Medical Compendium Journal:
Communications biology

Showing 71 to 80 of 154 articles

Deep learning-based image enhancement in optical coherence tomography by exploiting interference fringe.

Communications biology
Optical coherence tomography (OCT), an interferometric imaging technique, provides non-invasive, high-speed, high-sensitive volumetric biological imaging in vivo. However, systemic features inherent in the basic operating principle of OCT limit its i...

Harmonizing across datasets to improve the transferability of drug combination prediction.

Communications biology
Combination treatment has multiple advantages over traditional monotherapy in clinics, thus becoming a target of interest for many high-throughput screening (HTS) studies, which enables the development of machine learning models predicting the respon...

Machine-learning-powered extraction of molecular diffusivity from single-molecule images for super-resolution mapping.

Communications biology
While critical to biological processes, molecular diffusion is difficult to quantify, and spatial mapping of local diffusivity is even more challenging. Here we report a machine-learning-enabled approach, pixels-to-diffusivity (Pix2D), to directly ex...

Cross-platform normalization enables machine learning model training on microarray and RNA-seq data simultaneously.

Communications biology
Large compendia of gene expression data have proven valuable for the discovery of novel biological relationships. Historically, most available RNA assays were run on microarray, while RNA-seq is now the platform of choice for many new experiments. Th...

Learning the protein language of proteome-wide protein-protein binding sites via explainable ensemble deep learning.

Communications biology
Protein-protein interactions (PPIs) govern cellular pathways and processes, by significantly influencing the functional expression of proteins. Therefore, accurate identification of protein-protein interaction binding sites has become a key step in t...

Volumetric imaging of fast cellular dynamics with deep learning enhanced bioluminescence microscopy.

Communications biology
Bioluminescence microscopy is an appealing alternative to fluorescence microscopy, because it does not depend on external illumination, and consequently does neither produce spurious background autofluorescence, nor perturb intrinsically photosensiti...

DrugnomeAI is an ensemble machine-learning framework for predicting druggability of candidate drug targets.

Communications biology
The druggability of targets is a crucial consideration in drug target selection. Here, we adopt a stochastic semi-supervised ML framework to develop DrugnomeAI, which estimates the druggability likelihood for every protein-coding gene in the human ex...

UnMICST: Deep learning with real augmentation for robust segmentation of highly multiplexed images of human tissues.

Communications biology
Upcoming technologies enable routine collection of highly multiplexed (20-60 channel), subcellular resolution images of mammalian tissues for research and diagnosis. Extracting single cell data from such images requires accurate image segmentation, a...

Gene-gene interaction detection with deep learning.

Communications biology
The extent to which genetic interactions affect observed phenotypes is generally unknown because current interaction detection approaches only consider simple interactions between top SNPs of genes. We introduce an open-source framework for increasin...

Self-supervised machine learning for live cell imagery segmentation.

Communications biology
Segmenting single cells is a necessary process for extracting quantitative data from biological microscopy imagery. The past decade has seen the advent of machine learning (ML) methods to aid in this process, the overwhelming majority of which fall u...