AIMC Topic: Machine Learning

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Sampling and Ranking of Protein Conformations Using Machine Learning Techniques Do Not Improve the Quality of Rigid Protein-Protein Docking.

Journal of chemical information and modeling
Rigid docking remains the most popular method of predicting protein-protein interactions in cases when experimental 3D structures of the complexes are not available. The docking often relies on known unbound (Apo) protein structures, which may differ...

Machine-Learning-Based Prediction of Algal Density Using Algal Volatile Organic Compounds for Bloom Early Warning.

Environmental science & technology
Harmful algal blooms (HABs) pose severe threats to aquatic ecosystems, yet rapid and accurate prediction of algal density remains challenging. As integrated metabolites are released throughout the algal growth, algal volatile organic compounds (AVOCs...

Label-Free SERS Platform Assisted by Machine Learning for Multi-Target Detection and Physiological State Classification in Sweat.

Analytical chemistry
The detection of sweat metabolites is crucial for health monitoring, disease screening, and personalized medicine. Traditional methods encounter challenges like low metabolite concentrations, complex biological matrices, and difficulty in achieving m...

Interpretable Machine Learning for Predicting Adverse Pregnancy Outcomes in Gestational Diabetes: Retrospective Cohort Study.

JMIR medical informatics
BACKGROUND: Gestational diabetes mellitus (GDM) affects over 5% of pregnancies worldwide, elevating risks of type 2 diabetes post partum and complications such as fetal death, miscarriage, and congenital abnormalities. Effective GDM management is ess...

Using Wearable Device and Machine Learning to Predict Mood Symptoms in Bipolar Disorder: Development and Usability Study.

JMIR medical informatics
BACKGROUND: Bipolar disorder (BD) is a highly recurrent disorder. Early detection, early intervention, and prevention of recurrent bipolar mood symptoms are key to a better prognosis.

Development of a Data-Based Method for Predicting Nursing Workload in an Acute Care Hospital: Methodological Study.

Journal of medical Internet research
BACKGROUND: Determining effective nurse staffing levels is crucial for ensuring quality patient care and operational efficiency within hospitals. Traditional workload prediction methods often rely on professional judgment or simple volume-based appro...

Real-Time Estimation of Arterial Partial Pressure of Carbon Dioxide in Patients Undergoing General Anesthesia: Predictive Modeling Study.

JMIR medical informatics
BACKGROUND: Adequate ventilation in mechanically ventilated patients is contingent upon the monitoring of the arterial partial pressure of carbon dioxide (PaCO2) during general anesthesia. Despite its significance, continuous monitoring remains chall...

Multi-model machine learning for automated identification of rice diseases using leaf image data.

PloS one
Rice, a staple meal for about half of the world's population, is critical to global food security, especially in Asia. However, diseases have a severe impact on rice production, resulting in significant yield losses or outright crop failure. Traditio...

Development of a prostate cancer biochemical recurrence risk signature using machine learning and motor protein-related genes.

PloS one
BACKGROUND: Motor proteins play significant roles in cancer progression, but their involvement in biochemical recurrence (BCR) of prostate cancer remains unclear. The objective of the study is to develop a prognostic indicator for BCR using machine l...

Prediction of reduction behavior by heating and strain variability of Campylobacter jejuni using amino acid phylogenetics from whole genome sequencing data.

International journal of food microbiology
Predicting bacterial behavior and strain variability is essential for quantitative microbial risk assessments in food safety. The growing availability of whole-genome sequencing (WGS) data enables deeper insights into microbial thermotolerance. Howev...