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Models, Biological

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Bridging the gap between mechanistic biological models and machine learning surrogates.

PLoS computational biology
Mechanistic models have been used for centuries to describe complex interconnected processes, including biological ones. As the scope of these models has widened, so have their computational demands. This complexity can limit their suitability when r...

Coupling machine learning and epidemiological modelling to characterise optimal fungicide doses when fungicide resistance is partial or quantitative.

Journal of the Royal Society, Interface
Increasing fungicide dose tends to lead to better short-term control of plant diseases. However, high doses select more rapidly for fungicide resistant strains, reducing long-term disease control. When resistance is qualitative and complete-i.e. resi...

Artificial Intelligence-Based Quantitative Structure-Property Relationship Model for Predicting Human Intestinal Absorption of Compounds with Serotonergic Activity.

Molecular pharmaceutics
Oral medicines represent the largest pharmaceutical market area. To achieve a therapeutic effect, a drug must penetrate the intestinal walls, the main absorption site for orally delivered active pharmaceutical ingredients (APIs). Indeed, predicting d...

Constrained disorder principle-based variability is fundamental for biological processes: Beyond biological relativity and physiological regulatory networks.

Progress in biophysics and molecular biology
The constrained disorder principle (CDP) defines systems based on their degree of disorder bounded by dynamic boundaries. The principle explains stochasticity in living and non-living systems. Denis Noble described the importance of stochasticity in ...

Gait-CNN-ViT: Multi-Model Gait Recognition with Convolutional Neural Networks and Vision Transformer.

Sensors (Basel, Switzerland)
Gait recognition, the task of identifying an individual based on their unique walking style, can be difficult because walking styles can be influenced by external factors such as clothing, viewing angle, and carrying conditions. To address these chal...

Dynamic turning and running of a hexapod robot using a separated and laterally arranged two-leg model.

Bioinspiration & biomimetics
We report on the development of separated and laterally arranged two-leg (SLTL) models with/without differentiated leg properties and their use as the dynamic running and turning templates for a hexapod robot. The laterally arranged two-leg morpholog...

A pharmacokinetic-pharmacodynamic model based on the SSA-1DCNN-Attention network and the semicompartment method.

Biotechnology & genetic engineering reviews
To solve the problem of inaccurate prediction caused by the lack of representativeness of samples due to the small sample size of the collected clinical data when using machine learning methods to predict drug concentration in plasma and describe the...

A CPG-Based Versatile Control Framework for Metameric Earthworm-Like Robotic Locomotion.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Annelids such as earthworms are considered to have central pattern generators (CPGs) that generate rhythms in neural circuits to coordinate the deformation of body segments for effective locomotion. At present, the states of earthworm-like robot segm...

Exploring the expressiveness of abstract metabolic networks.

PloS one
Metabolism is characterised by chemical reactions linked to each other, creating a complex network structure. The whole metabolic network is divided into pathways of chemical reactions, such that every pathway is a metabolic function. A simplified re...

Artificial Intelligence and Machine Learning Approaches to Facilitate Therapeutic Drug Management and Model-Informed Precision Dosing.

Therapeutic drug monitoring
BACKGROUND: Therapeutic drug monitoring (TDM) and model-informed precision dosing (MIPD) have greatly benefitted from computational and mathematical advances over the past 60 years. Furthermore, the use of artificial intelligence (AI) and machine lea...