AIMC Topic: Machine Learning

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Exploring the spatial effects influencing the EGFR/ERK pathway dynamics with machine learning surrogate models.

Bio Systems
The fate of cells is regulated by biochemical reactions taking place inside of them, known as intracellular pathways. Cells display a variety of characteristics related to their shape, structure and contained fluid, which influences the diffusion of ...

Graph-learning-based machine learning improves prediction and cultivation of commercial-grade marine microalgae Porphyridium.

Bioresource technology
A graph learning [Binarized Attributed Network Embedding (BANE)] model enhances the single-target and multi-target prediction performances of random forest and eXtreme Gradient Boosting (XGBoost) by learning complex interrelationships between cultiva...

Synergistic biophysics and machine learning modeling to rapidly predict cardiac growth probability.

Computers in biology and medicine
Computational models that can predict growth and remodeling of the heart could have important clinical applications. However, the time it takes to calibrate and run current models while considering data uncertainty and variability makes them impracti...

Carcinogenic and non-carcinogenic risks caused by rice contamination with heavy metals and their effect on the prevalence of cardiovascular disease (Using machine learning).

Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association
INTRODUCTION: The safety and health of food products are essential in the food industry, and the risk of contamination from various contaminants must be evaluated. Exposure to HMs from the environment (especially food) causes various adverse effects ...

Small molecule inhibitors of IL-1R1/IL-1β interaction identified via transfer machine learning QSAR modelling.

International journal of biological macromolecules
The human interleukin-1 receptor I (IL-1R1) is a cytokine receptor recognized by interleukin 1β (IL-1β), among other cytokines. Over activation of IL-1R1 has been implicated in various inflammatory conditions. This research aims to identify small-mol...

Machine learning integration with response surface methodology to enhance the removal efficacy of arsenate (V) through sulfur-functionalized mxene coated QPPO/PVA AEM.

Journal of environmental management
Arsenic, a poisonous and carcinogenic heavy metal in drinking water, presents severe health risks to humans, including skin lesions, neurological damage, and circulatory disorders. Despite extensive research efforts have been carried out on removing ...

Assessing polyomic risk to predict Alzheimer's disease using a machine learning model.

Alzheimer's & dementia : the journal of the Alzheimer's Association
INTRODUCTION: Alzheimer's disease (AD) is the most common form of dementia in the elderly. Given that AD neuropathology begins decades before symptoms, there is a dire need for effective screening tools for early detection of AD to facilitate early i...

Challenges and Prospects of DNA-Encoded Library Data Interpretation.

Chemical reviews
DNA-encoded library (DEL) technology is a powerful platform for the efficient identification of novel chemical matter in the early drug discovery process enabled by parallel screening of vast libraries of encoded small molecules through affinity sele...

Microfluidic Optical Aptasensor for Small Molecules Based on Analyte-Tuned Growth of Gold Nanoseeds and Machine Learning-Enhanced Spectrum Analysis: Rapid Detection of Mycotoxins.

ACS sensors
Natural toxins, mainly small molecules, are a category of chemical hazards in agri-food systems that pose threats to both public health and food security. Current standard methods for monitoring these toxins, predominantly based on liquid chromatogra...

Predictions of the Optical Properties of Brown Carbon Aerosol by Machine Learning with Typical Chromophores.

Environmental science & technology
The linkages between BrC optical properties and chemical composition remain inadequately understood, with quantified chromophores explaining less than 25% of ambient aerosol light absorption. This study characterized 38 typical chromophores in aeroso...