AIMC Topic: Caenorhabditis elegans

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WormSwin: Instance segmentation of C. elegans using vision transformer.

Scientific reports
The possibility to extract motion of a single organism from video recordings at a large-scale provides means for the quantitative study of its behavior, both individual and collective. This task is particularly difficult for organisms that interact w...

An automated framework for evaluation of deep learning models for splice site predictions.

Scientific reports
A novel framework for the automated evaluation of various deep learning-based splice site detectors is presented. The framework eliminates time-consuming development and experimenting activities for different codebases, architectures, and configurati...

CPGL: Prediction of Compound-Protein Interaction by Integrating Graph Attention Network With Long Short-Term Memory Neural Network.

IEEE/ACM transactions on computational biology and bioinformatics
Recent advancements of artificial intelligence based on deep learning algorithms have made it possible to computationally predict compound-protein interaction (CPI) without conducting laboratory experiments. In this manuscript, we integrated a graph ...

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...

Fast, efficient, and accurate neuro-imaging denoising via supervised deep learning.

Nature communications
Volumetric functional imaging is widely used for recording neuron activities in vivo, but there exist tradeoffs between the quality of the extracted calcium traces, imaging speed, and laser power. While deep-learning methods have recently been applie...

A deep learning method for predicting molecular properties and compound-protein interactions.

Journal of molecular graphics & modelling
Predicting molecular properties and compound-protein interactions (CPIs) are two important areas of drug design and discovery. They are also an essential way to discover lead compounds in virtual screening. Recently, in silico methods based on deep l...

Gene function prediction in five model eukaryotes exclusively based on gene relative location through machine learning.

Scientific reports
The function of most genes is unknown. The best results in automated function prediction are obtained with machine learning-based methods that combine multiple data sources, typically sequence derived features, protein structure and interaction data....

Microrobotic Swarms for Intracellular Measurement with Enhanced Signal-to-Noise Ratio.

ACS nano
In cell biology, fluorescent dyes are routinely used for biochemical measurements. The traditional global dye treatment method suffers from low signal-to-noise ratios (SNR), especially when used for detecting a low concentration of ions, and increasi...

Nucleosome positioning based on DNA sequence embedding and deep learning.

BMC genomics
BACKGROUND: Nucleosome positioning is the precise determination of the location of nucleosomes on DNA sequence. With the continuous advancement of biotechnology and computer technology, biological data is showing explosive growth. It is of practical ...

Deep learning for robust and flexible tracking in behavioral studies for C. elegans.

PLoS computational biology
Robust and accurate behavioral tracking is essential for ethological studies. Common methods for tracking and extracting behavior rely on user adjusted heuristics that can significantly vary across different individuals, environments, and experimenta...