AIMC Topic: Random Forest

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Predicting lncRNA-protein interactions through deep learning framework employing multiple features and random forest algorithm.

BMC bioinformatics Mar 12, 2024
RNA-protein interaction (RPI) is crucial to the life processes of diverse organisms. Various researchers have identified RPI through long-term and high-cost biological experiments. Although numerous machine learning and deep learning-based methods fo...
Animals Deep Learning RNA, Long Noncoding Neural Networks, Computer Random Forest Machine Learning Mice Computational Biology
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A three-gene random forest model for diagnosing idiopathic pulmonary fibrosis based on circadian rhythm-related genes in lung tissue.

Expert review of respiratory medicine Jan 31, 2024
BACKGROUND: The disorder of circadian rhythm could be a key factor mediating fibrotic lung disease Therefore, our study aims to determine the diagnostic value of circadian rhythm-related genes (CRRGs) in IPF.
Idiopathic Pulmonary Fibrosis Random Forest Humans Lung Circadian Rhythm Collagen Type VII
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Novel gene signatures predicting and immune infiltration analysis in Parkinson's disease: based on combining random forest with artificial neural network.

Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology Jan 24, 2024
BACKGROUND: Parkinson's disease (PD) ranks as the second most prevalent neurodegenerative disorder globally, and its incidence is rapidly rising. The diagnosis of PD relies on clinical characteristics. Although current treatments aim to alleviate sym...
Humans Computational Biology Parkinson Disease Mast Cells Random Forest Gene Expression Profiling Transcriptome Neural Networks, Computer Machine Learning
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Predicting vitamin D deficiency using optimized random forest classifier.

Clinical nutrition ESPEN Dec 28, 2023
BACKGROUND: Vitamin D can be acquired from various dietary sources, but exposure to sunlight's ultraviolet rays can convert a natural compound called ergosterol present in the skin into vitamin D.
Vitamin D Vitamin D Deficiency Random Forest Humans Cross-Sectional Studies Vitamins Female Male
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The application of structural and machine learning models to predict the default risk of listed companies in the Iranian capital market.

PloS one Nov 27, 2023
Inattention of economic policymakers to default risk and making inappropriate decisions related to this risk in the banking system and financial institutions can have many economic, political and social consequences. In this research, it has been tri...
Iran Decision Making Random Forest Machine Learning ROC Curve
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A model to forecast the two-year variation of subjective wellbeing in the elderly population.

BMC medical informatics and decision making Nov 8, 2023
BACKGROUND: The ageing global population presents significant public health challenges, especially in relation to the subjective wellbeing of the elderly. In this study, our aim was to investigate the potential for developing a model to forecast the ...
Aged Aging Machine Learning Forecasting Random Forest Middle Aged Logistic Models Humans
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A cluster-based ensemble approach for congenital heart disease prediction.

Computer methods and programs in biomedicine Nov 7, 2023
BACKGROUND: One of the most prevalent birth disorders is congenital heart diseases (CHD). Although CHD risk factors have been the subject of numerous studies, their propensity to cause CHD has not been tested. Particularly few research has attempted ...
Cross-Sectional Studies Random Forest Heart Defects, Congenital Machine Learning Forecasting Humans
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Detection of Ponzi scheme on Ethereum using machine learning algorithms.

Scientific reports Oct 27, 2023
Security threats posed by Ponzi schemes present a considerably higher risk compared to many other online crimes. These fraudulent online businesses, including Ponzi schemes, have witnessed rapid growth and emerged as major threats in societies like N...
Fraud Algorithms Neural Networks, Computer Humans Random Forest Machine Learning
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Flexible Machine Learning Estimation of Conditional Average Treatment Effects: A Blessing and a Curse.

Epidemiology (Cambridge, Mass.) Oct 25, 2023
Causal inference from observational data requires untestable identification assumptions. If these assumptions apply, machine learning methods can be used to study complex forms of causal effect heterogeneity. Recently, several machine learning method...
Random Forest Humans Machine Learning Causality
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AI-Assisted Cotton Grading: Active and Semi-Supervised Learning to Reduce the Image-Labelling Burden.

Sensors (Basel, Switzerland) Oct 24, 2023
The assessment of food and industrial crops during harvesting is important to determine the quality and downstream processing requirements, which in turn affect their market value. While machine learning models have been developed for this purpose, t...
Random Forest Supervised Machine Learning Problem-Based Learning Machine Learning
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