AIMC Topic: Drosophila

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DANCE provides an open-source and low-cost approach to quantify aggression and courtship in .

eLife
Quantifying animal behavior is pivotal for identifying the neuronal and genetic mechanisms involved. Computational approaches have enabled automated analysis of complex behaviors such as aggression and courtship in . However, existing approaches rely...

VacQuant: a tool to quantify neurodegeneration and associated vacuolation in brain tissue.

Fly
Neurodegenerative diseases are devastating conditions characterized by progressive cognitive decline with few available treatments. Neurodegeneration can be quantified in vertebrate and invertebrate models of disease by analysis of vacuolation - the ...

A library of lineage-specific driver lines connects developing neuronal circuits to behavior in the ventral nerve cord.

eLife
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
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
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
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
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
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
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
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...