AIMC Topic: Machine Learning

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Preoperative risk assessment of invasive endometrial cancer using MRI-based radiomics: a systematic review and meta-analysis.

Abdominal radiology (New York)
OBJECTIVE: Image-derived machine learning (ML) is a robust and growing field in diagnostic imaging systems for both clinicians and radiologists. Accurate preoperative radiological evaluation of the invasive ability of endometrial cancer (EC) can incr...

An integrated bioinformatics and machine learning-based approach to depict key immunological players associated with candidemia during immunodeficiency.

Computational biology and chemistry
It is evident that a robust immune system keeps Candida albicans infection in check, but weakened immunity opens the door for shifting from a benign yeast form to an invasive hyphal form which leads to systemic candidiasis with high mortality rate. H...

A machine learning-based glycolysis and fatty acid metabolism-related prognostic signature is constructed and identified ACSL5 as a novel marker inhibiting the proliferation of breast cancer.

Computational biology and chemistry
INTRODUCTION: A new perspective on cancer metabolism suggests that it varies by context and is diverse. Cancer metabolism reprogramming can create a heterogeneous microenvironment that affects immune cell infiltration and function, complicating the s...

Unveiling the dark matter of the metabolome: A narrative review of bioinformatics tools for LC-HRMS-based compound annotation.

Talanta
Compound annotation, including the unveiling of dark matter in the metabolomics study represents a pivotal undertaking within the metabolomics field, serving as the linchpin for unraveling the identities and attributes of chemical entities. This narr...

Mapping the landscape of AI and ML in vaccine innovation: A bibliometric study.

Human vaccines & immunotherapeutics
With the rapid advancement of artificial intelligence (AI) and machine learning (ML) technologies, their applications in the medical field have expanded significantly. Particularly in vaccine innovation, AI and ML have shown considerable potential. T...

"MR Fingerprinting for Imaging Brain Hemodynamics and Oxygenation".

Journal of magnetic resonance imaging : JMRI
Over the past decade, several studies have explored the potential of magnetic resonance fingerprinting (MRF) for the quantification of brain hemodynamics, oxygenation, and perfusion. Recent advances in simulation models and reconstruction frameworks ...

A novel sensor array combined with machine learning for the effective differentiation of multiple classes of antibiotics based on copper-based nanozymes with laccase-like activity.

Talanta
A nanozyme sensor array was developed combining with machine learning algorithms for the effective identification and differentiation of multiple antibiotic classes and antibiotic individuals. The four channel sensor array was composed by four copper...

Unveiling the role of oxidative stress in ANCA-associated glomerulonephritis through integrated machine learning and bioinformatics analyses.

Renal failure
Anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) is a systemic autoimmune disease often leading to rapidly progressive glomerulonephritis. Oxidative stress plays a critical role in the development and progression of ANCA-associ...

Prediction of Poor Visual Outcomes at Idiopathic Intracranial Hypertension Diagnosis Using a Supervised Machine Learning Algorithm.

Journal of neuro-ophthalmology : the official journal of the North American Neuro-Ophthalmology Society
BACKGROUND: Idiopathic intracranial hypertension (IIH) is a vision-threatening disorder mainly affecting women of a reproductive age. Prompt diagnosis and intervention are vital to prevent vision loss, but validated tools to predict visual outcomes a...