AIMC Topic: Machine Learning

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Machine learning-based identification of a transcriptomic blood signature discriminating between systemic autoimmunity and infection.

Med (New York, N.Y.)
BACKGROUND: Pathogenic responses against self and foreign antigens in systemic autoimmunity and infection, respectively, engage similar immunologic components, thus lacking distinguishing diagnostic biomarkers. Herein, we tested whether whole-blood t...

When is Not Enough: Evaluating Simple Metrics for Predicting Phase Separation of Intrinsically Disordered Proteins.

The journal of physical chemistry. B
Understanding and predicting the phase behavior of intrinsically disordered proteins (IDPs) is of significant interest due to their role in many biological processes. However, effectively characterizing phase behavior and its complex dependence on pr...

Phenolic Profile as a Powerful Machine Learning Tool for Identification, Traceability, and Quality Control of Olive Cultivars.

Journal of agricultural and food chemistry
This study investigates the phenolic and fatty acid profiles of olives from four cultivars (Arbequina, Arbosana, Frantene, and Koroneiki), widely grown in the Mediterranean region and collected at different ripening stages in Italy. The aim was to a...

Deconvoluting and Interpreting Nontargeted Chemical Data: A Data-Driven Forensic Workflow for Identifying the Most Prominent Chemical Sources in Receiving Waters.

Environmental science & technology
Chemical forensics aims to identify major contamination sources, but existing workflows often rely on predefined targets and known sources, introducing bias. Here, we present a data-driven workflow that reduces this bias by applying an unsupervised m...

Machine learning for the prediction of blood transfusion risk during or after mitral valve surgery: a multicenter retrospective cohort study.

Scientific reports
This study aimed to identify the optimal prediction method and key preoperative variables for red blood cell (RBC) transfusion risk in patients undergoing mitral valve surgery. We conducted a retrospective study involving 1477 patients from eight lar...

Machine learning- and multilayer molecular network-assisted screening hunts fentanyl compounds.

Science advances
Fentanyl and its analogs are a global concern, making their accurate identification essential for public health. Here, we introduce Fentanyl-Hunter, a screening platform that uses a machine learning classifier and multilayer molecular network to sele...

Interpretable machine learning model predicts 1-year inguinal hernia risk after robot-assisted radical prostatectomy.

Journal of robotic surgery
Inguinal hernia represents a clinically significant yet underreported complication of robot-assisted radical prostatectomy (RARP) for localized prostate cancer, with a notably high incidence within the first postoperative year. Despite its adverse im...

DeepGAM: An interpretable deep neural network using generalized additive model for depression diagnosis: Data from the heart and soul study.

PloS one
Deep neural networks have achieved significant performance breakthroughs across a range of tasks. For diagnosing depression, there has been increasing attention on estimating depression status from personal medical data. However, the neural networks ...

Iterative rolling difference-Z-score and machine learning imputation for wind turbine foundation monitoring.

PloS one
In engineering structure performance monitoring, capturing real-time on-site data and conducting precise analysis are critical for assessing structural condition and safety. However, equipment instability and complex on-site environments often lead t...