AIMC Topic: Models, Biological

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Continuous attractors for dynamic memories.

eLife
Episodic memory has a dynamic nature: when we recall past episodes, we retrieve not only their content, but also their temporal structure. The phenomenon of replay, in the hippocampus of mammals, offers a remarkable example of this temporal dynamics....

Misic, a general deep learning-based method for the high-throughput cell segmentation of complex bacterial communities.

eLife
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-...

Predictive constitutive modelling of arteries by deep learning.

Journal of the Royal Society, Interface
The constitutive modelling of soft biological tissues has rapidly gained attention over the last 20 years. Current constitutive models can describe the mechanical properties of arterial tissue. Predicting these properties from microstructural informa...

Machine learning approaches to predict gestational age in normal and complicated pregnancies via urinary metabolomics analysis.

Scientific reports
The elucidation of dynamic metabolomic changes during gestation is particularly important for the development of methods to evaluate pregnancy status or achieve earlier detection of pregnancy-related complications. Some studies have constructed model...

Synergisms of machine learning and constraint-based modeling of metabolism for analysis and optimization of fermentation parameters.

Biotechnology journal
Recent noteworthy advances in developing high-performing microbial and mammalian strains have enabled the sustainable production of bio-economically valuable substances such as bio-compounds, biofuels, and biopharmaceuticals. However, to obtain an in...

Leveraging advances in immunopathology and artificial intelligence to analyze in vitro tumor models in composition and space.

Advanced drug delivery reviews
Cancer is the leading cause of death worldwide. Unfortunately, efforts to understand this disease are confounded by the complex, heterogenous tumor microenvironment (TME). Better understanding of the TME could lead to novel diagnostic, prognostic, an...

Uncovering cancer vulnerabilities by machine learning prediction of synthetic lethality.

Molecular cancer
BACKGROUND: Synthetic lethality describes a genetic interaction between two perturbations, leading to cell death, whereas neither event alone has a significant effect on cell viability. This concept can be exploited to specifically target tumor cells...

Classification for avian malaria parasite Plasmodium gallinaceum blood stages by using deep convolutional neural networks.

Scientific reports
The infection of an avian malaria parasite (Plasmodium gallinaceum) in domestic chickens presents a major threat to the poultry industry because it causes economic loss in both the quality and quantity of meat and egg production. Computer-aided diagn...

COVID-19 sentiment analysis via deep learning during the rise of novel cases.

PloS one
Social scientists and psychologists take interest in understanding how people express emotions and sentiments when dealing with catastrophic events such as natural disasters, political unrest, and terrorism. The COVID-19 pandemic is a catastrophic ev...

Comparative mapping of crawling-cell morphodynamics in deep learning-based feature space.

PLoS computational biology
Navigation of fast migrating cells such as amoeba Dictyostelium and immune cells are tightly associated with their morphologies that range from steady polarized forms that support high directionality to those more complex and variable when making fre...