AIMC Topic: Machine Learning

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A lightweight hybrid DL model for multi-class chest x-ray classification for pulmonary diseases.

Biomedical physics & engineering express
Pulmonary diseases have become one of the main reasons for people's health decline, impacting millions of people worldwide. Rapid advancement of deep learning has significantly impacted medical image analysis by improving diagnostic accuracy and effi...

Strategic forecasting of renewable energy production for sustainable electricity supply: A machine learning approach considering environmental, economic, and oil factors in Türkiye.

PloS one
Providing electricity needs from renewable energy sources is an important issue in the energy policies of countries. Especially changes in energy usage rates make it necessary to use renewable energy resources to be sustainable. The electricity usage...

Machine learning-based predictive modeling of angina pectoris in an elderly community-dwelling population: Results from the PoCOsteo study.

PloS one
BACKGROUND: Angina pectoris, a comparatively common complaint among older adults, is a critical warning sign of underlying coronary heart disease. We aimed to develop machine learning-based models using multiple algorithms to predict and identify the...

Personalized colorectal cancer risk assessment through explainable AI and Gut microbiome profiling.

Gut microbes
The clinical adenoma - carcinoma progression represents a well-established framework for understanding colorectal cancer (CRC) development, although the molecular mechanisms underlying this transition remain only partially understood. Increasing evid...

Comparison of machine learning algorithms for predicting length of stay in chronic kidney disease patients.

Computers in biology and medicine
The length of stay (LOS) for patients in hospitals is crucial for workforce planning, resource allocation, and bed capacity management, impacting healthcare costs, future needs and financial planning. This study focuses on calculating the LOS for Chr...

Augmenting Chemical Databases for Atomistic Machine Learning by Sampling Conformational Space.

Journal of chemical information and modeling
Machine learning (ML) has become a standard tool for the exploration of the chemical space. Much of the performance of such models depends on the chosen database for a given task. Here, this aspect is investigated for "chemical tasks" including the p...

A Molecular Representation Learning Model Based on Multidimensional Joint and Cross-Learning for Drug-Drug Interaction Prediction.

Journal of chemical information and modeling
Drug-drug interactions (DDIs) present significant challenges within clinical pharmacology, as they can impact therapeutic outcomes, especially given the growing prevalence of polypharmacy. Traditional methods for the clinical validation of DDIs typic...

Prediction of the hypothalamus-pituitary organoid formation using machine learning.

Cell reports methods
Multi-cellular organoids are self-assembly aggregates that mimic biological functions and developmental processes of many tissue types in vitro. They are widely employed for disease modeling and functional studies. Hypothalamus-pituitary organoids ca...

Integrated multiple machine learning and Mendelian randomization reveal LTF gene as a prognostic biomarker for nonspecific orbital inflammation.

BMC pharmacology & toxicology
BACKGROUND: Nonspecific orbital inflammation (NSOI), also known as idiopathic orbital inflammation, comprises a heterogeneous group of immune-mediated disorders affecting orbital tissues, unified by the absence of a defined etiology. Lactotransferrin...

An open dataset and machine learning algorithms for Niacin Skin-Flushing Response based screening of psychiatric disorders.

BMC psychiatry
BACKGROUND: Niacin Skin-Flushing Response (NSR) has emerged as a promising objective biomarker for the precise diagnosis of mental disorders. However, its diagnostic potential has been constrained by the limitations of traditional statistical approac...