AIMC Topic: Models, Biological

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Comparative efficacy of machine-learning models in prediction of reducing uncertainties in biosurfactant production.

Bioprocess and biosystems engineering
An accurate and reliable forecast of biosurfactant production with minimum error is useful in any bioprocess engineering. Bacterial isolate FKOD36 capable of producing biosurfactant was isolated in this study and pre-inoculums was prepared from the a...

Local Interpretable Model-Agnostic Explanations for Classification of Lymph Node Metastases.

Sensors (Basel, Switzerland)
An application of explainable artificial intelligence on medical data is presented. There is an increasing demand in machine learning literature for such explainable models in health-related applications. This work aims to generate explanations on ho...

Deep multiphysics: Coupling discrete multiphysics with machine learning to attain self-learning in-silico models replicating human physiology.

Artificial intelligence in medicine
OBJECTIVES: The objective of this study is to devise a modelling strategy for attaining in-silico models replicating human physiology and, in particular, the activity of the autonomic nervous system.

Machine learning prediction of cyanobacterial toxin (microcystin) toxicodynamics in humans.

ALTEX
Microcystins (MC) represent a family of cyclic peptides with approx. 250 congeners presumed harmful to human health due to their ability to inhibit ser/thr-proteinphosphatases (PPP), albeit all hazard and risk assessments (RA) are based on data of on...

Gene Expression Data Based Deep Learning Model for Accurate Prediction of Drug-Induced Liver Injury in Advance.

Journal of chemical information and modeling
Drug-induced liver injury (DILI), one of the most common adverse effects, leads to drug development failure or withdrawal from the market in most cases, showing an emerging challenge that is to accurately predict DILI in the early stage. Recently, th...

A visual encoding model based on deep neural networks and transfer learning for brain activity measured by functional magnetic resonance imaging.

Journal of neuroscience methods
BACKGROUND: Building visual encoding models to accurately predict visual responses is a central challenge for current vision-based brain-machine interface techniques. To achieve high prediction accuracy on neural signals, visual encoding models shoul...

Identifying pre-disease signals before metabolic syndrome in mice by dynamical network biomarkers.

Scientific reports
The establishment of new therapeutic strategies for metabolic syndrome is urgently needed because metabolic syndrome, which is characterized by several disorders, such as hypertension, increases the risk of lifestyle-related diseases. One approach is...

A neural network model predicts community-level signaling states in a diverse microbial community.

PLoS computational biology
Signal crosstalk within biological communication networks is common, and such crosstalk can have unexpected consequences for decision making in heterogeneous communities of cells. Here we examined crosstalk within a bacterial community composed of fi...

Single-Particle Diffusion Characterization by Deep Learning.

Biophysical journal
Diffusion plays a crucial role in many biological processes including signaling, cellular organization, transport mechanisms, and more. Direct observation of molecular movement by single-particle-tracking experiments has contributed to a growing body...

Termite population size estimation based on termite tunnel patterns using a convolutional neural network.

Mathematical biosciences
Subterranean termites live in large colonies under the ground where they build an elaborate network of tunnels for foraging. In this study, we explored how the termite population size can be estimated using partial information on tunnel patterns. To ...