AIMC Topic: Models, Biological

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DeepCt: Predicting Pharmacokinetic Concentration-Time Curves and Compartmental Models from Chemical Structure Using Deep Learning.

Molecular pharmaceutics
After initial triaging using in vitro absorption, distribution, metabolism, and excretion (ADME) assays, pharmacokinetic (PK) studies are the first application of promising drug candidates in living mammals. Preclinical PK studies characterize the ev...

Automated real-world data integration improves cancer outcome prediction.

Nature
The digitization of health records and growing availability of tumour DNA sequencing provide an opportunity to study the determinants of cancer outcomes with unprecedented richness. Patient data are often stored in unstructured text and siloed datase...

Cooperative control of environmental extremes by artificial intelligent agents.

Journal of the Royal Society, Interface
Humans have been able to tackle biosphere complexities by acting as ecosystem engineers, profoundly changing the flows of matter, energy and information. This includes major innovations that allowed to reduce and control the impact of extreme events....

Robotic feet modeled after ungulates improve locomotion on soft wet grounds.

Bioinspiration & biomimetics
Locomotion on soft yielding grounds is more complicated and energetically demanding than on hard ground. Wet soft ground (such as mud or snow) is a particularly difficult substance because it dissipates energy when stepping and resists extrusion of t...

Physiological model-based machine learning for classifying patients with binge-eating disorder (BED) from the Oral Glucose Tolerance Test (OGTT) curve.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Binge eating disorder (BED) is the most frequent eating disorder, often confused with obesity, with which it shares several characteristics. Early identification could enable targeted therapeutic interventions. In this study...

Predicting egg production rate and egg weight of broiler breeders based on machine learning and Shapley additive explanations.

Poultry science
Egg production rate and egg weight are core indicators for evaluating the production performance of broiler breeders. The accurate prediction of these indicators can significantly enhance farm economic efficiency and can provide a basis for future pr...

PREPRINT Machine Learning for the Sensitivity Analysis of a Model of the Cellular Uptake of Nanoparticles for the Treatment of Cancer.

International journal for numerical methods in biomedical engineering
Experimental studies on the cellular uptake of nanoparticles (NPs), useful for the investigation of NP-based drug delivery systems, are often difficult to interpret due to the large number of parameters that can contribute to the phenomenon. It is th...

The state-of-the-art machine learning model for plasma protein binding prediction: Computational modeling with OCHEM and experimental validation.

European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences
Plasma protein binding (PPB) is closely related to pharmacokinetics, pharmacodynamics and drug toxicity. Existing models for predicting PPB often suffer from low prediction accuracy and poor interpretability, especially for high PPB compounds, and ar...

AI-based automated construction of high-precision Geobacillus thermoglucosidasius enzyme constraint model.

Metabolic engineering
Geobacillus thermoglucosidasius NCIMB 11955 possesses advantages, such as high-temperature tolerance, rapid growth rate, and low contamination risk. Additionally, it features efficient gene editing tools, making it one of the most promising next-gene...

Microbial-Guided prediction of methane and sulfide production in Sewers: Integrating mechanistic models with Machine learning.

Bioresource technology
Accurate modeling of methane (CH) and sulfide (HS) production in sewer systems was constrained by insufficient consideration of microbial processes under dynamic environmental conditions. This study introduces a microbial-guided machine learning (ML)...