AIMC Topic: Models, Biological
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A bio-lattice deep learning framework for modeling discrete biological materials.
Journal of the mechanical behavior of biomedical materials
Jan 25, 2025
Biological tissues dynamically adapt their mechanical properties at the microscale in response to stimuli, often governed by discrete interacting mechanisms that dictate the material's behavior at the macroscopic scale. An approach to model the discr...
I2Bot: an open-source tool for multi-modal and embodied simulation of insect navigation.
Journal of the Royal Society, Interface
Jan 22, 2025
Achieving a comprehensive understanding of animal intelligence demands an integrative approach that acknowledges the interplay between an organism's brain, body and environment. Insects, despite their limited computational resources, demonstrate rema...
Automated model discovery for tensional homeostasis: Constitutive machine learning in growth and remodeling.
Computers in biology and medicine
Jan 21, 2025
We present a built-in physics neural network architecture, known as inelastic Constitutive Artificial Neural Network (iCANN), to discover the inelastic phenomenon of tensional homeostasis. In this course, identifying the optimal model and material pa...
Covariate Model Selection Approaches for Population Pharmacokinetics: A Systematic Review of Existing Methods, From SCM to AI.
CPT: pharmacometrics & systems pharmacology
Jan 20, 2025
A growing number of covariate modeling methods have been proposed in the field of popPK modeling, but limited information exists on how they all compare. The objective of this study was to perform a systematic review of all popPK covariate modeling m...
Addressing data uncertainty of Caulobacter crescentus cell cycles using hybrid Petri nets with fuzzy kinetics.
Computers in biology and medicine
Jan 18, 2025
Studying and analysing the various phases and key proteins of cell cycles is essential for the understanding of cell development and differentiation. To this end, mechanistic models play an important role towards a system level understanding of the i...
Neural network surrogate and projected gradient descent for fast and reliable finite element model calibration: A case study on an intervertebral disc.
Computers in biology and medicine
Jan 8, 2025
Accurate calibration of finite element (FE) models is essential across various biomechanical applications, including human intervertebral discs (IVDs), to ensure their reliability and use in diagnosing and planning treatments. However, traditional ca...
Research on the movement pattern and kinematic model of the hindlegs of the water boatman.
Bioinspiration & biomimetics
Jan 6, 2025
The special hindleg structure and swimming setae of a water boatman give it a high degree of maneuverability, which plays an important role in swimming. This paper used a high-speed photography platform to extract key points from videos, obtaining th...
Deep learning predicts DNA methylation regulatory variants in specific brain cell types and enhances fine mapping for brain disorders.
Science advances
Jan 1, 2025
DNA methylation (DNAm) is essential for brain development and function and potentially mediates the effects of genetic risk variants underlying brain disorders. We present INTERACT, a transformer-based deep learning model to predict regulatory varian...
An in-depth examination of the fuzzy fractional cancer tumor model and its numerical solution by implicit finite difference method.
PloS one
Dec 20, 2024
The cancer tumor model serves a s a crucial instrument for understanding the behavior of different cancer tumors. Researchers have employed fractional differential equations to describe these models. In the context of time fractional cancer tumor mod...
Metabolic Fluxes Using Deep Learning Based on Enzyme Variations: .
International journal of molecular sciences
Dec 13, 2024
Metabolic pathway modeling, essential for understanding organism metabolism, is pivotal in predicting genetic mutation effects, drug design, and biofuel development. Enhancing these modeling techniques is crucial for achieving greater prediction accu...
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