This study employed state-of-the-art machine learning to evaluate the mortality effects of alpha-emitting radionuclides (241Am, 249Cf, 252Cf, 238Pu, 239Pu, 224Ra, 226Ra, 228Th) on 2,576 dogs, factoring in radioactivity levels, composition, administra...
Chloride channel accessory 1 (CLCA1) is considered a potential prognostic biomarker for colon adenocarcinoma (COAD). The objective of this research was to develop two pathomics models to predict CLCA1 expression from hematoxylin-eosin (H&E) stained p...
Diabetic kidney disease (DKD) is a major cause of end-stage renal disease globally, with podocytes being implicated in its pathogenesis. However, the underlying mechanisms of podocyte involvement remain unclear. The aim of the present study was to id...
OBJECTIVES: Predicting optimal coagulation control using heparin in intensive care units (ICUs) remains a significant challenge. This study aimed to develop a machine learning (ML) model to predict activated partial thromboplastin time (aPTT) in ICU ...
INTRODUCTION: Accurate breast cancer risk prediction is essential for early detection and personalized prevention strategies. While traditional models, such as Gail and Tyrer-Cuzick, are widely utilized, machine learning-based approaches may offer en...
Cerebral amyloid angiopathy (CAA) and insomnia are age-related neurological disorders increasingly recognized as being closely associated. However, research on the shared genes and their biological mechanisms remains limited. This study aims to ident...
Acute myocardial infarction (AMI) is a serious heart disease with high fatality rates. The progress of AMI involves immune cell infiltration. However, suitable clinical diagnostic biomarkers and the roles of immune cells in AMI remain unknown. Three ...
In this paper, we present a predictive model based on artificial neural network (ANN) to evaluate principal physicochemical properties of a set of anti-inflammatory drugs based on chosen topological indices. The molecular descriptors were calculated ...
BACKGROUND: Identification of drug target interactions (DTI) is an important part of the drug discovery process. Since prediction of DTI using laboratory tests is time consuming and laborious, automated tools using computational intelligence (CI) tec...
Fertility preferences significantly influence population dynamics and reproductive health outcomes, particularly in low-resource settings, such as Somalia, where high fertility rates and limited healthcare infrastructure pose significant challenges. ...
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