AIMC Topic: Machine Learning

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Diagnostic accuracy of machine learning approaches to identify psoriatic arthritis: a meta-analysis.

Clinical and experimental medicine
While machine learning (ML) approaches are commonly utilized in medical diagnostics, the accuracy of these methods in identifying psoriatic arthritis (PsA) remains uncertain. To evaluate the accuracy of ML approaches in the medical diagnosis of PsA. ...

Integrated phenotypic analysis, predictive modeling, and identification of novel trait-associated loci in a diverse Theobroma cacao collection.

BMC plant biology
BACKGROUND: Cacao (Theobroma cacao L.) breeding and improvement rely on understanding germplasm diversity and trait architecture. This study characterized a cacao collection (173 accessions) evaluated in Puerto Rico, examining phenotypic diversity, t...

Application of causal forest double machine learning (DML) approach to assess tuberculosis preventive therapy's impact on ART adherence.

Scientific reports
Adherence to antiretroviral therapy (ART) is critical for HIV treatment success, yet the impact of tuberculosis preventive therapy (TPT) remains inadequately understood. Using observational data from 4152 HIV patients in Ethiopia (2005-2024), we appl...

WGCNA combined with machine learning for analysis of diagnostic markers of preeclampsia associated with the hedgehog signaling pathway.

Hypertension in pregnancy
BACKGROUND: Abnormal hedgehog (Hh) signaling is linked to preeclampsia (PE). This study aimed to identify Hh-related diagnostic biomarkers for PE and assess the role of immune infiltration.

Machine learning-assisted affinity ultrafiltration for bioactive natural products discovery:Application to screening of neuraminidase inhibitors from medicinal herbs.

Analytica chimica acta
BACKGROUND: Bioactive natural products represent a vital resource for combating human diseases. However, their discovery often encounters multiple challenges. Bioactivity-guided isolation can yield bioactive compounds but are labor-intensive and have...

Wetland dynamics in the Indus River Delta: A Sentinel-2 and machine learning approach.

Journal of environmental management
Coastal wetlands of the Indus River Delta are vital ecological regions that have undergone significant transformations driven by anthropogenic activities and environmental stressors. This study assesses the dynamics of wetlands and reclamation in the...

A Machine Learning-Based Modeling Approach for Dye Removal Using Modified Natural Adsorbents.

Journal of chemical information and modeling
This study used machine learning models to investigate the potential of biosorbents derived from natural fruit seed waste (apricot, almond, and walnut) for removing a cationic dye. Levulinic acid (LA)-modified powders of almond shell (ASh), apricot k...

Integrating bioinformatics analysis, machine learning, and experimental validation to identify pyroptosis-related genes in the diagnosis of sepsis combined with acute liver failure.

Hereditas
BACKGROUND: Sepsis is frequently combined with acute liver failure (ALF), a critical determinant in the mortality of septic patients. Pyroptosis is a significant form of programmed cell death that plays an important role in the inflammatory response....

Development and validation of interpretable machine learning models for predicting AKI risk in patients treated with PD-1/PD-L1: a retrospective study.

BMC medical informatics and decision making
BACKGROUND: Anti-programmed cell death protein 1 (PD-1)/programmed cell death ligand 1 (PD-L1) immunotherapy has revolutionized cancer treatment. However, it can cause immune-related adverse events, including acute kidney injury (AKI). Such adverse e...

Predicting COVID-19 severity in pediatric patients using machine learning: a comparative analysis of algorithms and ensemble methods.

Scientific reports
COVID-19 has posed a significant global health challenge, affecting individuals across all age groups. While extensive research has focused on adults, pediatric patients exhibit distinct clinical characteristics that necessitate specialized predictiv...