AIMC Topic: Machine Learning

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Multidimensional strategy for discovering saltiness-enhancing peptides in shrimp heads integrating ultra-high pressure hydrolysis and machine learning.

Food chemistry
This study aims to develop a comprehensive strategy to investigate whether the integration of ultra-high pressure (UHP)-assisted enzymatic hydrolysis with machine learning and molecular docking can effectively identify salty peptides (SPs) from Litop...

Machine learning-based prediction of deep soil metal(loid) contamination in industrial areas: Role of surface environmental factors.

Environmental pollution (Barking, Essex : 1987)
Predicting the distribution of soil contamination is crucial for targeted remediation efforts and risk prevention, especially considering the high costs associated with in-situ contamination surveys. This study proposes a random forest (RF)-based app...

Polymer-Functionalized Carbon Nanotube Sensors for Volatile Organic Compound Signal Exchange and Bioinspired Molecular Communication.

ACS sensors
Conventional electromagnetic communication systems face limitations in dense environments, including high energy consumption, signal attenuation, and interference. To overcome these challenges, we present a bioinspired molecular communication (MC) pl...

Artificial Intelligence for Noninvasive Health Diagnostics.

ACS sensors
Noninvasive diagnostic approaches are essential for early detection, patient compliance, and reduction of healthcare burden, yet they often face limitations in sensitivity, specificity, and timely interpretation. Artificial intelligence (AI) and mach...

Predictive value of systemic inflammation response index for atherosclerotic cardiovascular disease risk in patients with hypercholesterolemia: a machine learning study with dual-cohort validation.

Lipids in health and disease
BACKGROUND: Residual cardiovascular risk persists in patients with hypercholesterolemia despite lipid-lowering therapy, underscoring the importance of inflammation in ASCVD development. This study evaluated the relationship between Systemic Inflammat...

Non-Hodgkin's lymphoma classification using 3D radiomics machine learning models for precision imaging in oncology.

BMC medical imaging
PURPOSE: To apply quantitative imaging analysis for noninvasive classification of the most frequent subtypes of Non-Hodgkin Lymphoma (NHL) as a basis for a clinical imaging genomic model to support therapeutic monitoring and clinical decision making.

A novel prediction method for protein-DNA binding sites based on protein language model fusion features with SE-connection pyramidal network and ensemble learning.

BMC genomics
Protein-DNA interactions are crucial in life processes such as gene expression and regulation. Therefore, the accurate prediction of DNA-binding sites on proteins is highly important for the advancement of scientific understanding in the field of bio...

Multistep Machine Learning Pipeline For Polymeric Nanoparticle Design.

AAPS PharmSciTech
Integrating machine learning (ML) into nanotechnology represents a promising strategy for rational design and accelerated development of drug delivery systems. However, studies in this field are scarce and face methodological and interpretative probl...

Multiple polygenic score approach in colorectal cancer risk prediction.

Scientific reports
Recent studies have demonstrated that for various diseases, incorporating polygenic risk scores (PRSs) for other traits and diseases into the PRS-based risk prediction model may improve predictive performance - known as Multiple Polygenic Score (MPS)...

Prediction of peptide cleavage sites using protein language models and graph neural networks.

Scientific reports
The growing interest in using peptide molecules as therapeutic agents, driven by their high selectivity and efficacy, has become a significant trend in the pharmaceutical industry. However, their oral administration remains challenging due to their l...