AIMC Topic: Drosophila melanogaster
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Implementing artificial neural networks through bionic construction.
PloS one
Feb 22, 2019
It is evident through biology research that, biological neural network could be implemented through two means: by congenital heredity, or by posteriority learning. However, traditionally, artificial neural network, especially the Deep learning Neural...
Fast animal pose estimation using deep neural networks.
Nature methods
Dec 20, 2018
The need for automated and efficient systems for tracking full animal pose has increased with the complexity of behavioral data and analyses. Here we introduce LEAP (LEAP estimates animal pose), a deep-learning-based method for predicting the positio...
DeepLabCut: markerless pose estimation of user-defined body parts with deep learning.
Nature neuroscience
Aug 20, 2018
Quantifying behavior is crucial for many applications in neuroscience. Videography provides easy methods for the observation and recording of animal behavior in diverse settings, yet extracting particular aspects of a behavior for further analysis ca...
MAPLE (modular automated platform for large-scale experiments), a robot for integrated organism-handling and phenotyping.
eLife
Aug 17, 2018
Lab organisms are valuable in part because of large-scale experiments like screens, but performing such experiments over long time periods by hand is arduous and error-prone. Organism-handling robots could revolutionize large-scale experiments in the...
Segmentation of Drosophila heart in optical coherence microscopy images using convolutional neural networks.
Journal of biophotonics
Aug 6, 2018
Convolutional neural networks (CNNs) are powerful tools for image segmentation and classification. Here, we use this method to identify and mark the heart region of Drosophila at different developmental stages in the cross-sectional images acquired b...
Deep(er) Learning.
The Journal of neuroscience : the official journal of the Society for Neuroscience
Jul 13, 2018
Animals successfully thrive in noisy environments with finite resources. The necessity to function with resource constraints has led evolution to design animal brains (and bodies) to be optimal in their use of computational power while being adaptabl...
Mushroom Bodies
Olfactory Receptor Neurons
Neuropil
Association Learning
Signal Detection, Psychological
Conditioning, Operant
Afferent Pathways
Signal-To-Noise Ratio
Drosophila melanogaster
Olfactory Perception
Nerve Net
Environment
Reinforcement, Psychology
Models, Neurological
Conditioning, Classical
Animals
Reward
Avoidance Learning
Deep Learning
Odorants
Synapses
Consistent prediction of GO protein localization.
Scientific reports
May 17, 2018
The GO-Cellular Component (GO-CC) ontology provides a controlled vocabulary for the consistent description of the subcellular compartments or macromolecular complexes where proteins may act. Current machine learning-based methods used for the automat...
A machine learning based framework to identify and classify long terminal repeat retrotransposons.
PLoS computational biology
Apr 23, 2018
Transposable elements (TEs) are repetitive nucleotide sequences that make up a large portion of eukaryotic genomes. They can move and duplicate within a genome, increasing genome size and contributing to genetic diversity within and across species. A...
Protective effect of aspirin against mitomycin C-induced carcinogenicity, assessed by the test for detection of epithelial tumor clones (warts) in Drosophila melanogaster.
Drug and chemical toxicology
Dec 27, 2017
The present study assessed the protective effect of aspirin against carcinogenicity induced by mitomycin C (MMC) by the test for detection of warts/epithelial tumor clones in Drosophila melanogaster. Larvae were treated with different concentrations ...
McEnhancer: predicting gene expression via semi-supervised assignment of enhancers to target genes.
Genome biology
Oct 26, 2017
Transcriptional enhancers regulate spatio-temporal gene expression. While genomic assays can identify putative enhancers en masse, assigning target genes is a complex challenge. We devised a machine learning approach, McEnhancer, which links target g...
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