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Deciphering the Genetic Links between Psychological Stress, Autophagy, and Dermatological Health: Insights from Bioinformatics, Single-Cell Analysis, and Machine Learning in Psoriasis and Anxiety Disorders.

The relationship between psychological stress, altered skin immunity, and autophagy-related genes (ATGs) is currently unclear. Psoriasis is a chronic skin inflammation of unclear etiology that is characterized by persistence and recurrence. Immune dysregulation and emotional disturbances are recognized as significant risk factors. Emerging clinical evidence suggests a possible connection between a...

AI-Assisted Detection of Interproximal, Occlusal, and Secondary Caries on Bite-Wing Radiographs: A Single-Shot Deep Learning Approach.

Tooth decay is a common oral disease worldwide, but errors in diagnosis can often be made in dental clinics, which can lead to a delay in treatment. This study aims to use artificial intelligence (AI) for the automated detection and localization of secondary, occlusal, and interproximal (D1, D2, D3) caries types on bite-wing radiographs. The eight hundred and sixty bite-wing radiographs were colle...

CKG-IMC: An inductive matrix completion method enhanced by CKG and GNN for Alzheimer's disease compound-protein interactions prediction.

Alzheimer's disease (AD) is one of the most prevalent chronic neurodegenerative disorders globally, with a rapidly growing population of AD patients a...

Machine Learning Models Identify Inhibitors of New Delhi Metallo-β-lactamase.

The worldwide spread of the metallo-β-lactamases (MBL), especially New Delhi metallo-β-lactamase-1 (NDM-1), is threatening the efficacy of β-lactams, ...

Advances in computational and experimental approaches for deciphering transcriptional regulatory networks: Understanding the roles of cis-regulatory elements is essential, and recent research utilizing MPRAs, STARR-seq, CRISPR-Cas9, and machine learning has yielded valuable insights.

Understanding the influence of cis-regulatory elements on gene regulation poses numerous challenges given complexities stemming from variations in tra...

MLm5C: A high-precision human RNA 5-methylcytosine sites predictor based on a combination of hybrid machine learning models.

RNA modification serves as a pivotal component in numerous biological processes. Among the prevalent modifications, 5-methylcytosine (m5C) significant...

Deep learning based digital pathology for predicting treatment response to first-line PD-1 blockade in advanced gastric cancer.

BACKGROUND: Advanced unresectable gastric cancer (GC) patients were previously treated with chemotherapy alone as the first-line therapy. However, wit...

Novel dosimetric validation of a commercial CT scanner based deep learning automated contour solution for prostate radiotherapy.

PURPOSE: OAR delineation accuracy influences: (i) a patient's optimised dose distribution (PD), (ii) the reported doses (RD) presented at approval, wh...

ML3CNet: Non-local means-assisted automatic framework for lung cancer subtypes classification using histopathological images.

BACKGROUND AND OBJECTIVE: Lung cancer (LC) has a high fatality rate that continuously affects human lives all over the world. Early detection of LC pr...

Opening the Black Box: Spatial Transcriptomics and the Relevance of Artificial Intelligence-Detected Prognostic Regions in High-Grade Serous Carcinoma.

Image-based deep learning models are used to extract new information from standard hematoxylin and eosin pathology slides; however, biological interpr...

Breaking the Barriers: Machine-Learning-Based c-RASAR Approach for Accurate Blood-Brain Barrier Permeability Prediction.

The intricate nature of the blood-brain barrier (BBB) poses a significant challenge in predicting drug permeability, which is crucial for assessing ce...

Machine Teaching Allows for Rapid Development of Automated Systems for Retinal Lesion Detection From Small Image Datasets.

Machine teaching, a machine learning subfield, may allow for rapid development of artificial intelligence systems able to automatically identify emerg...

Timing Matters: A Machine Learning Method for the Prioritization of Drug-Drug Interactions Through Signal Detection in the FDA Adverse Event Reporting System and Their Relationship with Time of Co-exposure.

INTRODUCTION: Current drug-drug interaction (DDI) detection methods often miss the aspect of temporal plausibility, leading to false-positive dispropo...

Artificial Intelligence in Orthodontics: Critical Review.

With increasing digitalization in orthodontics, certain orthodontic manufacturing processes such as the fabrication of indirect bonding trays, aligner...

Should the Use of Adaptive Machine Learning Systems in Medicine be Classified as Research?

A novel advantage of the use of machine learning (ML) systems in medicine is their potential to continue learning from new data after implementation i...

Machine-learning prediction of a novel diagnostic model using mitochondria-related genes for patients with bladder cancer.

Bladder cancer (BC) is the ninth most-common cancer worldwide and it is associated with high morbidity and mortality. Mitochondrial Dysfunction is inv...

Part I: prostate cancer detection, artificial intelligence for prostate cancer and how we measure diagnostic performance: a comprehensive review.

MRI has firmly established itself as a mainstay for the detection, staging and surveillance of prostate cancer. Despite its success, prostate MRI cont...

Assessment of atherosclerotic plaque burden: comparison of AI-QCT versus SIS, CAC, visual and CAD-RADS stenosis categories.

This study assesses the agreement of Artificial Intelligence-Quantitative Computed Tomography (AI-QCT) with qualitative approaches to atherosclerotic ...

Immune Regulation Patterns in Response to Environmental Pollutant Chromate Exposure-Related Genetic Damage: A Cross-Sectional Study Applying Machine Learning Methods.

Exposure to hexavalent chromium damages genetic materials like DNA and chromosomes, further elevating cancer risk, yet research rarely focuses on rela...

Predicting treatment plan approval probability for high-dose-rate brachytherapy of cervical cancer using adversarial deep learning.

Predicting the probability of having the plan approved by the physician is important for automatic treatment planning. Driven by the mathematical foun...