AIMC Topic: Species Specificity
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Deep learning the cis-regulatory code for gene expression in selected model plants.
Nature communications
Apr 25, 2024
Elucidating the relationship between non-coding regulatory element sequences and gene expression is crucial for understanding gene regulation and genetic variation. We explored this link with the training of interpretable deep learning models predict...
Unsupervised machine learning for species delimitation, integrative taxonomy, and biodiversity conservation.
Molecular phylogenetics and evolution
Oct 5, 2023
Integrative taxonomy, combining data from multiple axes of biologically relevant variation, is a major goal of systematics. Ideally, such taxonomies will derive from similarly integrative species-delimitation analyses. Yet, most current methods rely ...
Marine Robotics for Deep-Sea Specimen Collection: A Taxonomy of Underwater Manipulative Actions.
Sensors (Basel, Switzerland)
Feb 14, 2022
In order to develop a gripping system or control strategy that improves scientific sampling procedures, knowledge of the process and the consequent definition of requirements is fundamental. Nevertheless, factors influencing sampling procedures have ...
A low-cost, long-term underwater camera trap network coupled with deep residual learning image analysis.
PloS one
Feb 2, 2022
Understanding long-term trends in marine ecosystems requires accurate and repeatable counts of fishes and other aquatic organisms on spatial and temporal scales that are difficult or impossible to achieve with diver-based surveys. Long-term, spatiall...
Coalescent-based species delimitation meets deep learning: Insights from a highly fragmented cactus system.
Molecular ecology resources
Oct 31, 2021
Delimiting species boundaries is a major goal in evolutionary biology. An increasing volume of literature has focused on the challenges of investigating cryptic diversity within complex evolutionary scenarios of speciation, including gene flow and de...
Rapid identification of the resistance of urinary tract pathogenic bacteria using deep learning-based spectroscopic analysis.
Analytical and bioanalytical chemistry
Oct 21, 2021
The resistance of urinary tract pathogenic bacteria to various antibiotics is increasing, which requires the rapid detection of infectious pathogens for accurate and timely antibiotic treatment. Here, we propose a rapid diagnosis strategy for the ant...
Evolutionarily informed machine learning enhances the power of predictive gene-to-phenotype relationships.
Nature communications
Sep 24, 2021
Inferring phenotypic outcomes from genomic features is both a promise and challenge for systems biology. Using gene expression data to predict phenotypic outcomes, and functionally validating the genes with predictive powers are two challenges we add...
Cross-species behavior analysis with attention-based domain-adversarial deep neural networks.
Nature communications
Sep 17, 2021
Since the variables inherent to various diseases cannot be controlled directly in humans, behavioral dysfunctions have been examined in model organisms, leading to better understanding their underlying mechanisms. However, because the spatial and tem...
Misic, a general deep learning-based method for the high-throughput cell segmentation of complex bacterial communities.
eLife
Sep 9, 2021
Studies of bacterial communities, biofilms and microbiomes, are multiplying due to their impact on health and ecology. Live imaging of microbial communities requires new tools for the robust identification of bacterial cells in dense and often inter-...
Organization of areal connectivity in the monkey frontoparietal network.
NeuroImage
Jul 20, 2021
Activity observed in biological neural networks is determined by anatomical connectivity between cortical areas. The monkey frontoparietal network facilitates cognitive functions, but the organization of its connectivity is unknown. Here, a new conne...
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