AIMC Topic: Models, Biological

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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...

Modeling in-vivo protein-DNA binding by combining multiple-instance learning with a hybrid deep neural network.

Scientific reports
Modeling in-vivo protein-DNA binding is not only fundamental for further understanding of the regulatory mechanisms, but also a challenging task in computational biology. Deep-learning based methods have succeed in modeling in-vivo protein-DNA bindin...

Novel epidemic models on PSO-based networks.

Journal of theoretical biology
This paper proposes two spatio-temporal epidemic network models based on popularity and similarity optimization (PSO), called r-SI and r-SIS, respectively, in which new connections take both popularity and similarity into account. In the spatial dime...

Improving the repeatability of two-rate model parameter estimations by using autoencoder networks.

Progress in brain research
The adaptive changes elicited in visuomotor adaptation experiments are usually well explained at group level by two-rate models (Smith et al., 2006), but parameters fitted to individuals show considerable variance. Data cleaning can mitigate this pro...

Machine Learning Applied to Predicting Microorganism Growth Temperatures and Enzyme Catalytic Optima.

ACS synthetic biology
Enzymes that catalyze chemical reactions at high temperatures are used for industrial biocatalysis, applications in molecular biology, and as highly evolvable starting points for protein engineering. The optimal growth temperature (OGT) of organisms ...

Learning and Tracking the 3D Body Shape of Freely Moving Infants from RGB-D sequences.

IEEE transactions on pattern analysis and machine intelligence
Statistical models of the human body surface are generally learned from thousands of high-quality 3D scans in predefined poses to cover the wide variety of human body shapes and articulations. Acquisition of such data requires expensive equipment, ca...