AIMC Topic: Machine Learning

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Oxidative Phosphorylation Pathway in Ankylosing Spondylitis: Multi-Omics Analysis and Machine Learning.

International journal of rheumatic diseases
INTRODUCTION: Ankylosing spondylitis (AS) is a chronic inflammatory disease affecting the axial skeleton, characterized by immune microenvironment dysregulation and elevated cytokines like TNF-α and IL-17. Mitochondrial oxidative phosphorylation (OXP...

Selection of neuroendocrine markers in diagnostic workup of neuroendocrine neoplasms: The real-world data and machine learning model algorithms.

Cancer cytopathology
BACKGROUND: Accurate diagnosis of neuroendocrine neoplasms (NENs) is challenging, especially in poorly differentiated neuroendocrine carcinomas (NECs). This study was aimed to search the best or best combination of neuroendocrine markers in the diagn...

Artificial intelligence and machine learning in ocular oncology, retinoblastoma (ArMOR).

Indian journal of ophthalmology
PURPOSE: To test the accuracy of a trained artificial intelligence and machine learning (AI/ML) model in the diagnosis and grouping of intraocular retinoblastoma (iRB) based on the International Classification of Retinoblastoma (ICRB) in a larger coh...

Harnessing artificial intelligence for advancements in Rice / wheat functional food Research and Development.

Food research international (Ottawa, Ont.)
In recent years, the research and development (R&D) of rice and wheat functional foods has attracted a widespread attention from food researchers, driven by the increasing global food consumption and growing consumer demand for healthier and safer fo...

Machine Learning Analysis of Single-Voxel Proton MR Spectroscopy for Differentiating Solitary Fibrous Tumors and Meningiomas.

NMR in biomedicine
Solitary fibrous tumor (SFT), formerly known as hemangiopericytoma, is an uncommon brain tumor often confused with meningioma on MRI. Unlike meningiomas, SFTs exhibit a myoinositol peak on magnetic resonance spectroscopy (MRS). This study aimed to de...

Applications and Considerations of Artificial Intelligence in Veterinary Sciences: A Narrative Review.

Veterinary medicine and science
In recent years, artificial intelligence (AI) has brought about a significant transformation in healthcare, streamlining manual tasks and allowing professionals to focus on critical responsibilities while AI handles complex procedures. This shift is ...

Integrating Artificial Intelligence in the Diagnosis and Management of Metabolic Syndrome: A Comprehensive Review.

Diabetes/metabolism research and reviews
BACKGROUND: Metabolic syndrome (MetS) is a progressive chronic pathophysiological state characterised by abdominal obesity, hypertension, hyperglycaemia, and dyslipidaemia. It is recognised as one of the major clinical syndromes affecting human healt...

Unraveling the three-dimensional genome structure using machine learning.

BMB reports
The study of chromatin interactions has advanced considerably with technologies such as high-throughput chromosome conformation capture (Hi-C) sequencing, providing a genome-wide view of physical interactions within the nucleus. These techniques have...

Large language models are less effective at clinical prediction tasks than locally trained machine learning models.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: To determine the extent to which current large language models (LLMs) can serve as substitutes for traditional machine learning (ML) as clinical predictors using data from electronic health records (EHRs), we investigated various factors ...

Predicting Milk Flow Behavior in Human Lactating Breast: An Integrated Machine Learning and Computational Fluid Dynamics Approach.

Journal of biomechanical engineering
This study develops a comprehensive framework that integrates computational fluid dynamics (CFD) and machine learning (ML) to predict milk flow behavior in lactating breasts. Utilizing CFD and other high-fidelity simulation techniques to tackle fluid...