AIMC Topic: Drosophila melanogaster
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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...
Hybrid neural networks in the mushroom body drive olfactory preference in .
Science advances
May 30, 2025
In , olfactory encoding in the mushroom body (MB) involves thousands of Kenyon cells (KCs) processing inputs from hundreds of projection neurons (PNs). Recent data challenge the notion of random PN-to-KC connectivity, revealing preferential connectio...
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...
Neural network conditioned to produce thermophilic protein sequences can increase thermal stability.
Scientific reports
Apr 23, 2025
This work presents Neural Optimization for Melting-temperature Enabled by Leveraging Translation (NOMELT), a novel approach for designing and ranking high-temperature stable proteins using neural machine translation. The model, trained on over 4 mill...
The fruit fly, , as a microrobotics platform.
Proceedings of the National Academy of Sciences of the United States of America
Apr 8, 2025
Engineering small autonomous agents capable of operating in the microscale environment remains a key challenge, with current systems still evolving. Our study explores the fruit fly, , a classic model system in biology and a species adept at microsca...
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...
Deep learning-driven behavioral analysis reveals adaptive responses in Drosophila offspring after long-term parental microplastic exposure.
Journal of environmental management
Feb 15, 2025
Microplastics are widely distributed in the environment and pose potential hazards to organisms. However, our understanding of the transgenerational effects of microplastics on terrestrial organisms remains limited. In this study, we focused on the m...
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...
Ense-i6mA: Identification of DNA N-Methyladenine Sites Using XGB-RFE Feature Selection and Ensemble Machine Learning.
IEEE/ACM transactions on computational biology and bioinformatics
Dec 10, 2024
DNA N-methyladenine (6mA) is an important epigenetic modification that plays a vital role in various cellular processes. Accurate identification of the 6mA sites is fundamental to elucidate the biological functions and mechanisms of modification. How...
Sex dimorphism and hormesis response to polystyrene microplastic exposure in kinematics and metabolism of Drosophila model based on deep learning.
Journal of environmental management
Oct 11, 2024
The emergence of microplastics (MPs) has become a significant focus of environmental pollution, prompting widespread concern regarding its potential toxicity and impact on the environment and organisms. Recent research indicates notable alterations i...
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