AIMC Topic: Drosophila
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A library of lineage-specific driver lines connects developing neuronal circuits to behavior in the ventral nerve cord.
eLife
Jun 10, 2025
Understanding developmental changes in neuronal lineages is crucial to elucidate how they assemble into functional neural networks. Studies investigating nervous system development in model systems have only focused on select regions of the CNS due t...
DeepGFT: identifying spatial domains in spatial transcriptomics of complex and 3D tissue using deep learning and graph Fourier transform.
Genome biology
Jun 3, 2025
The rapid advancements in spatially resolved transcriptomics (SRT) enable the characterization of gene expressions while preserving spatial information. However, high dropout rates and noise hinder accurate spatial domain identification for understan...
Anti-drift pose tracker (ADPT), a transformer-based network for robust animal pose estimation cross-species.
eLife
May 6, 2025
Deep learning-based methods have advanced animal pose estimation, enhancing accuracy, and efficiency in quantifying animal behavior. However, these methods frequently experience tracking drift, where noise-induced jumps in body point estimates compro...
Integrating genetic variation with deep learning provides context for variants impacting transcription factor binding during embryogenesis.
Genome research
May 2, 2025
Understanding how genetic variation impacts transcription factor (TF) binding remains a major challenge, limiting our ability to model disease-associated variants. Here, we used a highly controlled system of F crosses with extensive genetic diversity...
Transfer learning of multicellular organization via single-cell and spatial transcriptomics.
PLoS computational biology
Apr 21, 2025
Biological tissues exhibit complex gene expression and multicellular patterns that are valuable to dissect. Single-cell RNA sequencing (scRNA-seq) provides full coverages of genes, but lacks spatial information, whereas spatial transcriptomics (ST) m...
FlyVISTA, an integrated machine learning platform for deep phenotyping of sleep in .
Science advances
Mar 12, 2025
There is great interest in using genetically tractable organisms such as to gain insights into the regulation and function of sleep. However, sleep phenotyping in has largely relied on simple measures of locomotor inactivity. Here, we present FlyVI...
Comparing statistical learning methods for complex trait prediction from gene expression.
PloS one
Feb 11, 2025
Accurate prediction of complex traits is an important task in quantitative genetics. Genotypes have been used for trait prediction using a variety of methods such as mixed models, Bayesian methods, penalized regression methods, dimension reduction me...
Deep learning for rapid analysis of cell divisions in vivo during epithelial morphogenesis and repair.
eLife
Sep 23, 2024
Cell division is fundamental to all healthy tissue growth, as well as being rate-limiting in the tissue repair response to wounding and during cancer progression. However, the role that cell divisions play in tissue growth is a collective one, requir...
The impacts of positive selection on genomic variation in Drosophila serrata: Insights from a deep learning approach.
Molecular ecology
Aug 26, 2024
This study explores the impact of positive selection on the genetic composition of a Drosophila serrata population in eastern Australia through a comprehensive analysis of 110 whole genome sequences. Utilizing an advanced deep learning algorithm (par...
A machine learning based method for tracking of simultaneously imaged neural activity and body posture of freely moving maggot.
Biochemical and biophysical research communications
Jun 24, 2024
To understand neural basis of animal behavior, it is necessary to monitor neural activity and behavior in freely moving animal before building relationship between them. Here we use light sheet fluorescence microscope (LSFM) combined with microfluidi...
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