AIMC Topic: Machine Learning

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Performance and hypothetical clinical impact of an mNGS-based machine learning model for antimicrobial susceptibility prediction of five ESKAPEE bacteria.

Microbiology spectrum
UNLABELLED: Antimicrobial resistance is an escalating global health crisis, underscoring the urgent need for timely and targeted therapies to ensure effective clinical treatment. We developed a machine learning model based on metagenomic next-generat...

Decoding the quantitative structure-activity relationship and astringency formation mechanism of oxygenated aromatic compounds.

Food research international (Ottawa, Ont.)
Astringency is a common sensory experience in the mouth, characterized by dryness, roughness, and puckering. Due to the inefficiency and expense of conventional astringency evaluation methods, the quantitative structure-activity relationship (QSAR) m...

Assessing the association of multi-environmental chemical exposures on metabolic syndrome: A machine learning approach.

Environment international
Metabolic syndrome (MetS) is a major global public health concern due to its rising prevalence and association with increased risks of cardiovascular disease and type 2 diabetes. Emerging evidence suggests that environmental chemical exposures may pl...

Advances in artificial intelligence-envisioned technologies for protein and nucleic acid research.

Drug discovery today
Artificial intelligence (AI) and machine learning (ML) have revolutionized pharmaceutical research, particularly in protein and nucleic acid studies. This review summarizes the current status of AI and ML applications in the pharmaceutical sector, fo...

Artificial intelligence in preclinical research: enhancing digital twins and organ-on-chip to reduce animal testing.

Drug discovery today
Artificial intelligence (AI) is reshaping preclinical drug research offering innovative alternatives to traditional animal testing. Advanced techniques, including machine learning (ML), deep learning (DL), AI-powered digital twins (DTs), and AI-enhan...

BL-FlowSOM: Consistent and Highly Accelerated FlowSOM Based on Parallelized Batch Learning.

Cytometry. Part A : the journal of the International Society for Analytical Cytology
The recent increase in the dimensionality of cytometry data has led to the development of various computational analysis methods. FlowSOM is one of the best-performing clustering methods but has room for improvement in terms of the consistency and sp...

Enhancing the Predictive Performance of Molecularly Imprinted Polymer-Based Electrochemical Sensors Using a Stacking Regressor Ensemble of Machine Learning Models.

ACS sensors
The performance of electrochemical sensors is influenced by various factors. To enhance the effectiveness of these sensors, it is crucial to find the right balance among these factors. Researchers and engineers continually explore innovative approach...

Smartphone-Based SPAD Value Estimation for Jujube Leaves Using Machine Learning: A Study on RGB Feature Extraction and Hybrid Modeling.

Sensors (Basel, Switzerland)
Chlorophyll content in date leaves is critical for fruit quality and yield. Traditional detection methods are usually complex and expensive. This study proposes a rapid detection method for chlorophyll content using smartphone images and machine lear...

Identification of key therapeutic targets in nicotine-induced intracranial aneurysm through integrated bioinformatics and machine learning approaches.

BMC pharmacology & toxicology
BACKGROUND: Intracranial aneurysm (IA) is a critical cerebrovascular condition, and nicotine exposure is a known risk factor. This study delves into the toxicological mechanisms of nicotine in IA, aiming to identify key biomarkers and therapeutic tar...