AI Medical Compendium Journal:
Communications biology

Showing 1 to 10 of 154 articles

Predicting bacterial phenotypic traits through improved machine learning using high-quality, curated datasets.

Communications biology
Predicting prokaryotic phenotypes-observable traits that govern functionality, adaptability, and interactions-holds significant potential for fields such as biotechnology, environmental sciences, and evolutionary biology. In this study, we leverage m...

Development of an AI model for DILI-level prediction using liver organoid brightfield images.

Communications biology
AI image processing techniques hold promise for clinical applications by enabling analysis of complex status information from cells. Importantly, real-time brightfield imaging has advantages of informativeness, non-destructive nature, and low cost ov...

Detecting genetic interactions with visible neural networks.

Communications biology
Non-linear interactions among single nucleotide polymorphisms (SNPs), genes, and pathways play an important role in human diseases, but identifying these interactions is a challenging task. Neural networks are state-of-the-art predictors in many doma...

Building molecular model series from heterogeneous CryoEM structures using Gaussian mixture models and deep neural networks.

Communications biology
Cryogenic electron microscopy (CryoEM) produces structures of macromolecules at near-atomic resolution. However, building molecular models with good stereochemical geometry from those structures can be challenging and time-consuming, especially when ...

A self-supervised learning approach for high throughput and high content cell segmentation.

Communications biology
In principle, ML/AI-based algorithms should enable rapid and accurate cell segmentation in high-throughput settings. However, reliance on large training datasets, human input, computational expertise, and limited generalizability has prevented this g...

AI-guided laser purification of human iPSC-derived cardiomyocytes for next-generation cardiac cell manufacturing.

Communications biology
Current methods for producing cardiomyocytes from human induced pluripotent stem cells (hiPSCs) using 2D monolayer differentiation are often hampered by batch-to-batch variability and inefficient purification processes. Here, we introduce CM-AI, a no...

Global intraspecific diversity of marine forests of brown macroalgae predicted by past climate conditions.

Communications biology
Global patterns of intraspecific genetic diversity are key to understanding evolutionary and ecological processes. However, insights into the distribution and drivers of genetic diversity remain limited, particularly for marine species. Here, we expl...

Machine learning-led semi-automated medium optimization reveals salt as key for flaviolin production in Pseudomonas putida.

Communications biology
Although synthetic biology can produce valuable chemicals in a renewable manner, its progress is still hindered by a lack of predictive capabilities. Media optimization is a critical, and often overlooked, process which is essential to obtain the tit...

Transformer-based deep learning for accurate detection of multiple base modifications using single molecule real-time sequencing.

Communications biology
We had previously reported a convolutional neural network (CNN) based approach, called the holistic kinetic model (HK model 1), for detecting 5-methylcytosine (5mC) by single molecule real-time sequencing (Pacific Biosciences). In this study, we cons...

Neural models for detection and classification of brain states and transitions.

Communications biology
Exploring natural or pharmacologically induced brain dynamics, such as sleep, wakefulness, or anesthesia, provides rich functional models for studying brain states. These models allow detailed examination of unique spatiotemporal neural activity patt...