AIMC Topic: Machine Learning

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The signed two-space proximity model for learning representations in protein-protein interaction networks.

Bioinformatics (Oxford, England)
MOTIVATION: Accurately predicting complex protein-protein interactions (PPIs) is crucial for decoding biological processes, from cellular functioning to disease mechanisms. However, experimental methods for determining PPIs are computationally expens...

From planning to prognosis: predicting renal function after minimally-invasive partial nephrectomy with artificial intelligence.

Minerva urology and nephrology
This study presents a machine learning model to predict renal function decline following minimally-invasive partial nephrectomy. Using a dataset of 556 patients treated between 2015 and 2023, the model incorporated patient, tumor, and intraoperative ...

Development and Validation of Quantile Regression Forests for Prediction of Reference Quantiles in Handgrip and Chair-Stand Test.

Journal of cachexia, sarcopenia and muscle
BACKGROUND: Muscle strength is one of the key components in the diagnosis of sarcopenia. The aim of this study was to train a machine learning model to predict reference values and percentiles for handgrip strength and chair-stand test (CST), in a la...

[Risk prediction of demoralization syndrome in patients with oral cancer].

Hua xi kou qiang yi xue za zhi = Huaxi kouqiang yixue zazhi = West China journal of stomatology
OBJECTIVES: This study aimed to construct a risk prediction model for the occurrence of the demora-lization syndrome in patients with oral cancer and provide a scientific basis for the prevention of this syndrome in patients with oral cancer and the ...

Predicting Stress, Anxiety, and Depression in Adult Men Based on Nutritional and Lifestyle Variables: A Comparative Analysis of Machine Learning Methods.

Journal of food science
Mental health disorders like depression, anxiety, and stress (DAS) are rising globally. Understanding how diet and lifestyle influence these conditions is vital for targeted interventions. This study explores the potential of machine learning (ML) to...

Rapid Identification and Typing of Carbapenem-Resistant Klebsiella pneumoniae Using MALDI-TOF MS and Machine Learning.

Microbial biotechnology
Use matrix-assisted laser desorption ionisation time-of-flight mass spectrometry (MALDI-TOF MS) to screen the specific mass peaks of carbapenem-resistant Klebsiella pneumoniae (CRKP), compare the differences in spectrum peaks between intestinal and b...

Development of Machine Learning-Based Models for Mutagenicity Predictions with Applications to Non-Sugar Sweeteners.

Molecular informatics
Artificial sweeteners, often known as non-sugar sweeteners (NSSs), have been utilized as food additives since World War II. However, there is also concern regarding the mutagenicity potential of NSSs. Every new chemical registration in the food and p...

Development of Machine-Learning-Based Models for Detection of Cognitive Impairment in Patients Receiving Maintenance Hemodialysis.

European journal of neurology
BACKGROUND: Cognitive impairment is common but frequently undiagnosed in the dialysis population. We aimed to develop and validate a quick and accurate screening tool using machine-learning-based approaches in them.

A Roadmap to Holographic Focused Ultrasound Approaches for Generating Gradient Thermal Patterns.

International journal for numerical methods in biomedical engineering
In therapeutic focused ultrasound (FUS), such as thermal ablation and hyperthermia, effective acousto-thermal manipulation requires precise targeting of complex geometries, sound wave propagation through irregular structures, and selective focusing a...

Gray Matter Differences in Adolescent Psychiatric Inpatients: A Machine Learning Study of Bipolar Disorder and Other Psychopathologies.

Brain and behavior
BACKGROUND: Bipolar disorder (BD) is among the psychiatric disorders most prone to misdiagnosis, with both false positives and false negatives resulting in treatment delay. We employed a whole-brain machine learning approach focusing on gray matter v...