AIMC Topic: Models, Biological

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Using neural ordinary differential equations to predict complex ecological dynamics from population density data.

Journal of the Royal Society, Interface
Simple models have been used to describe ecological processes for over a century. However, the complexity of ecological systems makes simple models subject to modelling bias due to simplifying assumptions or unaccounted factors, limiting their predic...

Predicting the clinical subcutaneous absorption rate constant of monoclonal antibodies using only the primary sequence: a machine learning approach.

mAbs
Subcutaneous injections are an increasingly prevalent route of administration for delivering biological therapies including monoclonal antibodies (mAbs). Compared with intravenous delivery, subcutaneous injections reduce administration costs, shorten...

The Impact of Drop Test Conditions on Brain Strain Location and Severity: A Novel Approach Using a Deep Learning Model.

Annals of biomedical engineering
In contact sports such as rugby, players are at risk of sustaining traumatic brain injuries (TBI) due to high-intensity head impacts that generate high linear and rotational accelerations of the head. Previous studies have established a clear link be...

A gray box framework that optimizes a white box logical model using a black box optimizer for simulating cellular responses to perturbations.

Cell reports methods
Predicting cellular responses to perturbations requires interpretable insights into molecular regulatory dynamics to perform reliable cell fate control, despite the confounding non-linearity of the underlying interactions. There is a growing interest...

Another string to your bow: machine learning prediction of the pharmacokinetic properties of small molecules.

Expert opinion on drug discovery
INTRODUCTION: Prediction of pharmacokinetic (PK) properties is crucial for drug discovery and development. Machine-learning (ML) models, which use statistical pattern recognition to learn correlations between input features (such as chemical structur...

Asymmetric fin shape changes swimming dynamics of ancient marine reptiles' soft robophysical models.

Bioinspiration & biomimetics
Animals have evolved highly effective locomotion capabilities in terrestrial, aerial, and aquatic environments. Over life's history, mass extinctions have wiped out unique animal species with specialized adaptations, leaving paleontologists to recons...

A toe-inspired rigid-flexible coupling wheel design method for improving the terrain adaptability of a sewer robot.

Bioinspiration & biomimetics
The human toe, characterized by its rigid-flexible structure comprising hard bones and flexible joints, facilitates adaptive and stable movement across varied terrains. In this paper, we utilized a motion capture system to study the adaptive adjustme...

Genome-scale metabolic models in translational medicine: the current status and potential of machine learning in improving the effectiveness of the models.

Molecular omics
The genome-scale metabolic model (GEM) has emerged as one of the leading modeling approaches for systems-level metabolic studies and has been widely explored for a broad range of organisms and applications. Owing to the development of genome sequenci...

Comparing the advantages and disadvantages of physics-based and neural network-based modelling for predicting cycling power.

Journal of biomechanics
Models of physical phenomena can be developed using two distinct approaches: using expert knowledge of the underlying physical principles or using experimental data to train a neural network. Here, our aim was to better understand the advantages and ...