AI Medical Compendium Topic:
Models, Biological

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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 ...

Discrimination of bursts and tonic activity in multifunctional sensorimotor neural network using the extended hill-valley method.

Journal of neurophysiology
Individual neurons can exhibit a wide range of activity, including spontaneous spiking, tonic spiking, bursting, or spike-frequency adaptation, and can also transition between these activity types. Manual identification of these activity patterns can...

Reconstruction of long-distance bird migration routes using advanced machine learning techniques on geolocator data.

Journal of the Royal Society, Interface
Geolocators are a well-established technology to reconstruct migration routes of animals that are too small to carry satellite tags (e.g. passerine birds). These devices record environmental light-level data that enable the reconstruction of daily po...

Domain transformation on biological event extraction by learning methods.

Journal of biomedical informatics
Event extraction and annotation has become a significant focus of recent efforts in biological text mining and information extraction (IE). However, event extraction, event annotation methods, and resources have so far focused almost exclusively on a...

Intelligent prediction of kinetic parameters during cutting manoeuvres.

Medical & biological engineering & computing
Due to its capabilities in analysing injury risk, the ability to analyse an athlete's ground reaction force and joint moments is of high interest in sports biomechanics. However, using force plates for the kinetic measurements influences the athlete'...

An influent responsive control strategy with machine learning: Q-learning based optimization method for a biological phosphorus removal system.

Chemosphere
Biological phosphorus removal (BPR) is an economical and sustainable processes for the removal of phosphorus (P) from wastewater, achieved by recirculating activated sludge through anaerobic and aerobic (An/Ae) processes. However, few studies have sy...