AIMC Topic: Machine Learning

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Discovering periodontitis biomarkers and therapeutic targets through bioinformatics and ensemble learning analysis.

Scientific reports
Periodontitis, a prevalent inflammatory disease, leads to the progressive destruction of periodontal tissues and poses significant systemic health risks. Despite its widespread impact, the molecular mechanisms driving periodontitis remain poorly unde...

DenPAR: Annotated Intra-Oral Periapical Radiographs Dataset for Machine Learning.

Scientific data
Dental diseases are one of the most common diseases that affect humans. Clinicians employ several techniques for diagnosing and monitoring dental diseases, with intra-oral periapical (IOPA) radiographs being among the most commonly utilized methods. ...

Automated assessment and detection of third molar and inferior alveolar nerve relations using UNet and transfer learning models.

Scientific reports
Panoramic radiographs (PRs) are widely used in assessing the relationship between the mandibular third molar (MM3) and the inferior alveolar nerve (IAN). The relationship of MM3 and IAN is a critical consideration in oral and maxillofacial surgery du...

Diagnostics of diabetic retinopathy based on fundus photos using machine learning methods with advanced feature engineering algorithms.

Scientific reports
Diabetes is one of the main diseases posing a threat to healthcare systems. One of the complications of diabetes is diabetic retinopathy, which, if left untreated, can lead to serious consequences such as blindness. Early detection of this disease is...

Activities of Daily Living Detection through Energy Consumption Data and Machine Learning to Support Independent Aging.

Journal of medical systems
The aging population presents significant challenges for healthcare and social services, emphasizing the need for innovative solutions that support independent living. This study explores the feasibility of identifying Instrumental Activities of Dail...

Retention time prediction of forensic compounds using ensemble machine learning and molecular descriptors.

Journal of chromatography. B, Analytical technologies in the biomedical and life sciences
Retention time (RT) prediction can greatly improve the efficiency of chromatographic workflows in forensic toxicology, especially in high-throughput or non-targeted analytical workflows. In the present study, we compare the performance of four ensemb...

A Three-Module Machine Learning Framework for Protein Sequence- and Temperature-Dependent / Prediction in β-Glucosidases.

ACS synthetic biology
The catalytic activity of enzymes is intricately determined by their amino acid sequences and assay conditions, particularly temperature. Navigating the complex interplay among sequence, temperature, and catalytic function is crucial for unlocking a ...

Machine Learning-Assisted Terahertz Metagrating Biosensor for Label-Free Bacterial Identification Based on Spectral Fingerprinting.

Analytical chemistry
Rapid and accurate identification of pathogenic bacteria is pivotal for enabling early clinical diagnosis and precision antimicrobial therapy. In this study, we developed a label-free bacterial detection platform that integrates terahertz (THz) metag...

Meta-Analysis and Machine Learning Prediction of Protein Corona Composition across Nanoparticle Systems in Biological Media.

ACS nano
A comprehensive understanding of protein corona (PC) composition is critical for engineering nanoparticles (NPs) with optimal safety and therapeutic performance, because the PC governs NP pharmacokinetics, biodistribution, and cellular interactions. ...

The Black Hole Strategy: Gravity-Based Representative Sampling for Frugal Graph Learning on Metal-Organic Framework Networks.

Journal of chemical information and modeling
The expansion of large-scale materials databases has facilitated the development of graph-based representations, encoding structural and functional similarities as edges in data-driven networks. These enable machine learning models to leverage both l...