AIMC Topic: Humans

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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...

Enhancing schizophrenia diagnosis efficiency with EEGNet: a simplified recognition model based on γ band features.

Psychiatry research. Neuroimaging
OBJECTIVE: This study aims to develop an objective and efficient diagnostic model for schizophrenia (SCZ) by integrating electroencephalogram (EEG) signals with deep learning techniques. Building on previous research, γ wave activity is selected as a...

Screening, identification, and experimental validation of SUMOylation biomarkers in Parkinson's disease.

Hereditas
BACKGROUND: Parkinson's disease (PD) is a common neurodegenerative disorder. The role of protein post-translational modifications (PTMs), especially small ubiquitin-like modifier (SUMO) conjugation (SUMOylation), in PD pathogenesis remains unclear. T...

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....

Prediction of human pathogenic start loss variants based on self-supervised contrastive learning.

BMC biology
BACKGROUND: Start loss variants are a class of genetic variants that affect the bases of the start codon, disrupting the normal translation initiation process and leading to protein deletions or the production of different proteins. Accurate assessme...

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...

A hybrid reinforcement learning and knowledge graph framework for financial risk optimization in healthcare systems.

Scientific reports
Effective financial risk management in healthcare systems requires intelligent decision-making that balances treatment quality with cost efficiency. This paper proposes a novel hybrid framework that integrates reinforcement learning (RL) with knowled...

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...

Machine learning based analysis of leucocyte cell population data by Sysmex XN series hematology analyzer for the diagnosis of bacteremia.

Scientific reports
In clinical practice, early recognition of bacteremia leads to prognostic improvement. Recently, cell population data (CPD) from the Sysmex XN-series hematology analyzer has attracted attention as a new method for the early diagnosis of bacteremia, b...

Deep learning approach for automated hMPV classification.

Scientific reports
Human metapneumovirus (hMPV) is a significant cause of respiratory illness, particularly in children, elderly individuals, and immunocompromised patients. Despite its clinical relevance, hMPV poses diagnostic challenges due to its symptom similarity ...