AIMC Topic: Zebrafish
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A database and deep learning toolbox for noise-optimized, generalized spike inference from calcium imaging.
Nature neuroscience
Aug 2, 2021
Inference of action potentials ('spikes') from neuronal calcium signals is complicated by the scarcity of simultaneous measurements of action potentials and calcium signals ('ground truth'). In this study, we compiled a large, diverse ground truth da...
Artificial intelligence-driven phenotyping of zebrafish psychoactive drug responses.
Progress in neuro-psychopharmacology & biological psychiatry
Jul 25, 2021
Zebrafish (Danio rerio) are rapidly emerging in biomedicine as promising tools for disease modelling and drug discovery. The use of zebrafish for neuroscience research is also growing rapidly, necessitating novel reliable and unbiased methods of neur...
Leveraging high-throughput screening data, deep neural networks, and conditional generative adversarial networks to advance predictive toxicology.
PLoS computational biology
Jul 2, 2021
There are currently 85,000 chemicals registered with the Environmental Protection Agency (EPA) under the Toxic Substances Control Act, but only a small fraction have measured toxicological data. To address this gap, high-throughput screening (HTS) an...
Zebrafish behavior feature recognition using three-dimensional tracking and machine learning.
Scientific reports
Jun 29, 2021
In this work, we aim to construct a new behavior analysis method by using machine learning. We used two cameras to capture three-dimensional (3D) tracking data of zebrafish, which were analyzed using fuzzy adaptive resonance theory (FuzzyART), a type...
Deep learning-based framework for cardiac function assessment in embryonic zebrafish from heart beating videos.
Computers in biology and medicine
Jun 11, 2021
Zebrafish is a powerful and widely-used model system for a host of biological investigations, including cardiovascular studies and genetic screening. Zebrafish are readily assessable during developmental stages; however, the current methods for quant...
The CPGs for Limbed Locomotion-Facts and Fiction.
International journal of molecular sciences
May 30, 2021
The neuronal networks that generate locomotion are well understood in swimming animals such as the lamprey, zebrafish and tadpole. The networks controlling locomotion in tetrapods remain, however, still enigmatic with an intricate motor pattern requi...
Deep learning-enhanced light-field imaging with continuous validation.
Nature methods
May 7, 2021
Visualizing dynamic processes over large, three-dimensional fields of view at high speed is essential for many applications in the life sciences. Light-field microscopy (LFM) has emerged as a tool for fast volumetric image acquisition, but its effect...
Modeling transcriptional regulation of model species with deep learning.
Genome research
Apr 22, 2021
To enable large-scale analyses of transcription regulation in model species, we developed DeepArk, a set of deep learning models of the -regulatory activities for four widely studied species: , , , and DeepArk accurately predicts the presence of tho...
3DeeCellTracker, a deep learning-based pipeline for segmenting and tracking cells in 3D time lapse images.
eLife
Mar 30, 2021
Despite recent improvements in microscope technologies, segmenting and tracking cells in three-dimensional time-lapse images (3D + T images) to extract their dynamic positions and activities remains a considerable bottleneck in the field. We develope...
Machine learning predictions of concentration-specific aggregate hazard scores of inorganic nanomaterials in embryonic zebrafish.
Nanotoxicology
Feb 15, 2021
The possibility of employing computational approaches like nano-QSAR or nano-read-across to predict nanomaterial hazard is attractive from both a financial, and most importantly, where in vivo tests are required, ethical perspective. In the present w...
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