AIMC Topic: Caenorhabditis elegans

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Artificial intelligence-driven drug repositioning uncovers efavirenz as a modulator of α-synuclein propagation: Implications in Parkinson's disease.

Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie
Parkinson's disease (PD) is a complex neurodegenerative disorder with an unclear etiology. Despite significant research efforts, developing disease-modifying treatments for PD remains a major unmet medical need. Notably, drug repositioning is becomin...

GraphsformerCPI: Graph Transformer for Compound-Protein Interaction Prediction.

Interdisciplinary sciences, computational life sciences
Accurately predicting compound-protein interactions (CPI) is a critical task in computer-aided drug design. In recent years, the exponential growth of compound activity and biomedical data has highlighted the need for efficient and interpretable pred...

Mechanical intelligence simplifies control in terrestrial limbless locomotion.

Science robotics
Limbless locomotors, from microscopic worms to macroscopic snakes, traverse complex, heterogeneous natural environments typically using undulatory body wave propagation. Theoretical and robophysical models typically emphasize body kinematics and acti...

Automated neuron tracking inside moving and deforming C. elegans using deep learning and targeted augmentation.

Nature methods
Reading out neuronal activity from three-dimensional (3D) functional imaging requires segmenting and tracking individual neurons. This is challenging in behaving animals if the brain moves and deforms. The traditional approach is to train a convoluti...

DensePPI: A Novel Image-Based Deep Learning Method for Prediction of Protein-Protein Interactions.

IEEE transactions on nanobioscience
Protein-protein interactions (PPI) are crucial for understanding the behaviour of living organisms and identifying disease associations. This paper proposes DensePPI, a novel deep convolution strategy applied to the 2D image map generated from the in...

Predicting lifespan-extending chemical compounds for with machine learning and biologically interpretable features.

Aging
Recently, there has been a growing interest in the development of pharmacological interventions targeting ageing, as well as in the use of machine learning for analysing ageing-related data. In this work, we use machine learning methods to analyse da...

The million-molecule challenge: a moonshot project to rapidly advance longevity intervention discovery.

GeroScience
Targeting aging is the future of twenty-first century preventative medicine. Small molecule interventions that promote healthy longevity are known, but few are well-developed and discovery of novel, robust interventions has stagnated. To accelerate l...

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 ...