AIMC Topic: Models, Biological

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A network model of affective odor perception.

PloS one
The affective appraisal of odors is known to depend on their intensity (I), familiarity (F), detection threshold (T), and on the baseline affective state of the observer. However, the exact nature of these relations is still largely unknown. We there...

Moonlighting Proteins in the Fuzzy Logic of Cellular Metabolism.

Molecules (Basel, Switzerland)
The numerous interconnected biochemical pathways that make up the metabolism of a living cell comprise a fuzzy logic system because of its high level of complexity and our inability to fully understand, predict, and model the many activities, how the...

Viscoelastic legs for open-loop control of gram-scale robots.

Bioinspiration & biomimetics
Gram-scale insects, such as cockroaches, take advantage of the mechanical properties of the musculoskeletal system to enable rapid and robust running. Engineering gram-scale robots, much like their biological counterparts, comes with inherent constra...

In-plane gait planning for earthworm-like metameric robots using genetic algorithm.

Bioinspiration & biomimetics
Locomotion of earthworm-like metameric robots results from shape changes of deformable segments. Morphologically, the segments could stretch, contract or bend by changing their states. Periodic shape changes are recognized as gaits of the robots. Rob...

Novel application of an automated-machine learning development tool for predicting burn sepsis: proof of concept.

Scientific reports
Sepsis is the primary cause of burn-related mortality and morbidity. Traditional indicators of sepsis exhibit poor performance when used in this unique population due to their underlying hypermetabolic and inflammatory response following burn injury....

Capsule networks as recurrent models of grouping and segmentation.

PLoS computational biology
Classically, visual processing is described as a cascade of local feedforward computations. Feedforward Convolutional Neural Networks (ffCNNs) have shown how powerful such models can be. However, using visual crowding as a well-controlled challenge, ...

Early detection of type 2 diabetes mellitus using machine learning-based prediction models.

Scientific reports
Most screening tests for T2DM in use today were developed using multivariate regression methods that are often further simplified to allow transformation into a scoring formula. The increasing volume of electronically collected data opened the opport...

Developing a neurally informed ontology of creativity measurement.

NeuroImage
A central challenge for creativity research-as for all areas of experimental psychology and cognitive neuroscience-is to establish a mapping between constructs and measures (i.e., identifying a set of tasks that best captures a set of creative abilit...

A mechanism-aware and multiomic machine-learning pipeline characterizes yeast cell growth.

Proceedings of the National Academy of Sciences of the United States of America
Metabolic modeling and machine learning are key components in the emerging next generation of systems and synthetic biology tools, targeting the genotype-phenotype-environment relationship. Rather than being used in isolation, it is becoming clear th...

Deep Physiological Model for Blood Glucose Prediction in T1DM Patients.

Sensors (Basel, Switzerland)
Accurate estimations for the near future levels of blood glucose are crucial for Type 1 Diabetes Mellitus (T1DM) patients in order to be able to react on time and avoid hypo and hyper-glycemic episodes. Accurate predictions for blood glucose are the ...