AIMC Topic: Machine Learning

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Generating realistic single-cell images from CellProfiler representations.

Medical image analysis
High-throughput imaging techniques acquire large amounts of images efficiently. These images contain rich biological information including cellular processes. A common method to analyse them is to encode them into quantitative representation vectors....

Preclinical evidence construction for epigallocatechin-3-gallate against non-alcoholic fatty liver disease: a meta-analysis and machine learning study.

Phytomedicine : international journal of phytotherapy and phytopharmacology
BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) has emerged as a significant health concern worldwide, exhibiting an increasing incidence that necessitates immediate intervention. Epigallocatechin-3-gallate (EGCG) has shown significant pharmaco...

Machine-Learning-Based Computed Tomography Radiomics Regression Model for Predicting Pulmonary Function.

Academic radiology
RATIONALE AND OBJECTIVES: Chest computed tomography (CT) radiomics can be utilized for categorical predictions; however, models predicting pulmonary function indices directly are lacking. This study aimed to develop machine-learning-based regression ...

Interpretable Machine Learning Radiomics Model Predicts 5-year Recurrence-Free Survival in Non-metastatic Clear Cell Renal Cell Carcinoma: A Multicenter and Retrospective Cohort Study.

Academic radiology
RATIONALE AND OBJECTIVES: To develop and validate a computed tomography (CT) radiomics-based interpretable machine learning (ML) model for predicting 5-year recurrence-free survival (RFS) in non-metastatic clear cell renal cell carcinoma (ccRCC).

Enhancing Ophthalmic Anesthesia Optimization with Predictive Embedding Models.

SLAS technology
Ophthalmic anesthesia the crucial factors in success and safety of ophthalmic surgery, which involves the delicate aspects of pain control, sedation, and patient response. Advances in ophthalmic surgery cause a need for exact and individualized anest...

Children on wheels: Identifying crash determinants using cluster correspondence analysis.

Accident; analysis and prevention
Child bicyclists (14 years old and younger) are among the most vulnerable road users, facing significant risks of crashes that often result in severe injuries or fatalities. This study aims to identify key factors influencing child bicyclist crashes ...

First report on Quantitative Structure-Toxicity Relationship modeling approaches for the prediction of acute toxicity of various organic chemicals against rotifer species.

The Science of the total environment
Nowadays, organic chemicals are crucial components in virtually every aspect of daily life, serving as indispensable elements for modern society. The ongoing synthesis of chemicals and the various potential harmful effects on living organisms are pro...

Artificial intelligence for the detection of interictal epileptiform discharges in EEG signals.

Revue neurologique
INTRODUCTION: Over the past decades, the integration of modern technologies - such as electronic health records, cloud computing, and artificial intelligence (AI) - has revolutionized the collection, storage, and analysis of medical data in neurology...

Enhancing the understandings on SARS-CoV-2 main protease (M) mutants from molecular dynamics and machine learning.

International journal of biological macromolecules
While star drugs like Paxlovid have shown remarkable performance in combating SARS-CoV-2, we still face serious challenges such as viral mutants and resistance. In this study, we employ a computational framework combining molecular dynamics (MD) simu...