AIMC Topic: Machine Learning

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An active machine learning discovery platform for membrane-disrupting and pore-forming peptides.

Physical chemistry chemical physics : PCCP
Membrane-disrupting and pore-forming peptides (PFPs) play a substantial role in bionanotechnology and can determine the life and death of cells. The control of chemical and ion transport through cell membranes is essential to maintaining concentratio...

Advancements in computer vision and pathology: Unraveling the potential of artificial intelligence for precision diagnosis and beyond.

Advances in cancer research
The integration of computer vision into pathology through slide digitalization represents a transformative leap in the field's evolution. Traditional pathology methods, while reliable, are often time-consuming and susceptible to intra- and interobser...

The circadian syndrome is a better predictor for psoriasis than the metabolic syndrome via an explainable machine learning method - the NHANES survey during 2005-2006 and 2009-2014.

Frontiers in endocrinology
OBJECTIVE: To explore the association between circadian syndrome (CircS) and Metabolic Syndrome (MetS) with psoriasis. Compare the performance of MetS and CircS in predicting psoriasis.

A new hybrid machine learning model for predicting the renewal life of patents.

PloS one
In almost every country, patents need to be renewed multiple times after they are granted. A patentee assesses the value of the patent and then pays a renewal fee to keep it active for another stipulated period. The factors that characterize the valu...

Characterizing Disease Progression in Parkinson's Disease from Videos of the Finger Tapping Test.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
INTRODUCTION: Parkinson's disease (PD) is characterized by motor symptoms whose progression is typically assessed using clinical scales, namely the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS). Despite its reliabilit...

Can Machine Learning Models Based on Computed Tomography Radiomics and Clinical Characteristics Provide Diagnostic Value for Epstein-Barr Virus-Associated Gastric Cancer?

Journal of computer assisted tomography
OBJECTIVE: The aim of this study was to explore whether machine learning model based on computed tomography (CT) radiomics and clinical characteristics can differentiate Epstein-Barr virus-associated gastric cancer (EBVaGC) from non-EBVaGC.

Predictive modelling of stress, anxiety and depression: A network analysis and machine learning study.

The British journal of clinical psychology
OBJECTIVE: This study assessed predictors of stress, anxiety and depression during the COVID-19 pandemic using a large number of demographic, COVID-19 context and psychological variables.

Machine learning in soil nutrient dynamics of alpine grasslands.

The Science of the total environment
As a terrestrial ecosystem, alpine grasslands feature diverse vegetation types and play key roles in regulating water resources and carbon storage, thus shaping global climate. The dynamics of soil nutrients in this ecosystem, responding to regional ...

Advancing toxicity studies of per- and poly-fluoroalkyl substances (pfass) through machine learning: Models, mechanisms, and future directions.

The Science of the total environment
Perfluorinated and perfluoroalkyl substances (PFASs), encompassing a vast array of isomeric chemicals, are recognized as typical emerging contaminants with direct or potential impacts on human health and the ecological environment. With the complex a...

Harnessing artificial intelligence for predicting and managing postoperative pain: a narrative literature review.

Current opinion in anaesthesiology
PURPOSE OF REVIEW: This review examines recent research on artificial intelligence focusing on machine learning (ML) models for predicting postoperative pain outcomes. We also identify technical, ethical, and practical hurdles that demand continued i...