AIMC Topic: Machine Learning

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Impact of analytical bias on machine learning models for sepsis prediction using laboratory data.

Clinical chemistry and laboratory medicine
OBJECTIVES: Machine learning (ML) models, using laboratory data, support early sepsis prediction. However, analytical bias in laboratory measurements can compromise their performance and validity in real-world settings. We aimed to evaluate how analy...

Machine learning-driven programmed cell death signature for prognosis and drug candidate discovery in diffuse large B-cell lymphoma: Multi-cohort study and experimental validation.

International immunopharmacology
BACKGROUND: Relapse and drug resistance are major contributor to chemotherapy failure in diffuse large B-cell lymphoma (DLBCL). Programmed cell death (PCD), a key mechanism in tumor progression and resistance, has emerged as a promising biomarker for...

MIA and CD163 as promising diagnostic biomarkers in vascular dementia: A multi-method study combining WGCNA, machine learning with validation in animal models and clinical samples.

International immunopharmacology
Vascular dementia (VaD), the second most common form of dementia, lacks reliable biomarkers for early diagnosis. Here, we integrated weighted gene co-expression network analysis (WGCNA) with machine learning to identify novel biomarkers and immune-me...

Late gadolinium enhancement imaging and sudden cardiac death.

European heart journal
The prediction and management of sudden cardiac death risk continue to pose significant challenges in cardiovascular care despite advances in therapies over the last two decades. Late gadolinium enhancement (LGE) on cardiac magnetic resonance-a marke...

How good is generative diffusion model for enhanced sampling of protein conformations across scales and in all-atom resolution?

The Journal of chemical physics
Molecular dynamics (MD) simulations are fundamental for probing the structural dynamics of biomolecules, yet their efficiency is limited by the high computational cost of exploring long-timescale events. Generative machine learning (ML) models, parti...

Can polycythaemia vera disease be predicted from haematologic parameters? A machine learning-based study.

Journal of clinical pathology
AIMS: The aim of this research is to diagnose polycythaemia vera (PV) disease using different machine learning (ML) algorithms with complete blood count (CBC) parameters before further investigations such as Janus kinase 2 (), erythropoietin (EPO) an...

A technological convergence in hepatobiliary oncology: Evolving roles of smart surgical systems.

Bioscience trends
Cancer remains a major threat to human health, with the incidence of hepatobiliary tumors consistently high. Treatment methods for hepatobiliary tumors include surgical intervention, ablation, embolization, and pharmacological treatments, with surger...

Insights into the comparison of machine learning models on rice grain arsenic prediction: Interplay of rice cultivation systems and soil environmental factors.

Environmental pollution (Barking, Essex : 1987)
Arsenic (As) exposure to rice threatens food safety while transferring As to rice from paddy soils significantly impacts increasing As levels in rice. This study explores establishing an efficient model for predicting As accumulation in rice grain us...

Differentiating estuarine dissolved organic matter composition by unsupervised and supervised machine learning.

Water research
Differentiating the composition of Dissolved Organic Matter (DOM) in estuaries is a major environmental concern, as the DOM characteristics are closely linked to biogeochemical and ecological considerations (e.g. water properties and trophic cycling)...