Characteristic alterations of gut microbiota and serum metabolites in patients with chronic tinnitus: a multi-omics analysis by Jiang Wang, Jia-Hui Xiang, Xu-Yuan Peng, Min Liu, Le-Jia Sun, Min Zhang, Li-Yuan Zhang, Zhi-Bin Chen, Zheng-Quan Tang, Lei Cheng

“…We used the weighted gene co-expression network method to analyze the relationship between the gut microbiota and the serum metabolites. The random forest technique was utilized to select metabolites and gut taxa to construct predictive models. …”
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Identification of biomarkers for knee osteoarthritis through clinical data and machine learning models by Wei Chen, Haotian Zheng, Binglin Ye, Tiefeng Guo, Yude Xu, Zhibin Fu, Xing Ji, Xiping Chai, Shenghua Li, Qiang Deng

“…Based on these rankings, predictive models were constructed using Logistic Regression (LR), Random Forest (RF), eXtreme Gradient Boosting (xGBoost), Naive Bayes (NB), Support Vector Machine (SVM), and Decision Tree (DT) algorithms. …”
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Validation of biomarkers and clinical scores for the detection of uterine leiomyosarcoma: a case-control study with an update of pLMS by Marcus Vollmer, Günter Köhler, Julia Caroline Radosa, Marek Zygmunt, Julia Zimmermann, Martina Köller, Christine Seitz, Helena Bralo, Marc Philipp Radosa, Askin Cangül Kaya, Johann Krichbaum, Erich-Franz Solomayer, Lars Kaderali, Zaher Alwafai

“…Missing values were imputed by random forest imputation to create the updated scoring system ‘pLMS2’ using penalized logistic regression based on the pooled data sets of 384 uLMS and 1485 LM. …”
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Comparative analysis of the human microbiome from four different regions of China and machine learning-based geographical inference by Yinlei Lei, Min Li, Han Zhang, Yu Deng, Xinyu Dong, Pengyu Chen, Ye Li, Suhua Zhang, Chengtao Li, Shouyu Wang, Ruiyang Tao

“…Individuals from the four regions could be distinguished and predicted based on a model constructed using the random forest algorithm, with the predictive effect of palmar microbiota being better than that of oral and nasal cavities. …”
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Identifying disulfidptosis-related biomarkers in epilepsy based on integrated bioinformatics and experimental analyses by Sijun Li, Lanfeng Sun, Hongmi Huang, Xing Wei, Yuling Lu, Kai Qian, Yuan Wu

“…The optimal machine learning model was revealed to be the random forest (RF) model. G protein guanine nucleotide-binding protein alpha subunit q (GNAQ) was linked to sodium valproate resistance. …”
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Comparative Analysis of Tillage Indices and Machine Learning Algorithms for Maize Residue Cover Prediction by Jian Li, Kewen Shao, Jia Du, Kaishan Song, Weilin Yu, Zhengwei Liang, Weijian Zhang, Jie Qin, Kaizeng Zhuo, Cangming Zhang, Yu Han, Yiwei Zhang, Bingrun Sui

“…MRC estimation models were built using six machine learning algorithms, including back propagation neural network (BPNN), random forest (RF), support vector regression (SVR), extreme gradient boosting (XGBoost), Stacking1, and Stacking2. …”
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Unraveling the immunological landscape and gut microbiome in sepsis: a comprehensive approach to diagnosis and prognosisResearch in context by Yali Luo, Jian Gao, Xinliang Su, Helian Li, Yingcen Li, Wenhao Qi, Xuling Han, Jingxuan Han, Yiran Zhao, Alin Zhang, Yan Zheng, Feng Qian, Hongyu He

“…Immunophenotype shifts were evaluated using differential expression sliding window analysis, and random forest models were developed for sepsis diagnosis or prognosis prediction. …”
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Developing a Prototype Machine Learning Model to Predict Quality of Life Measures in People Living With HIV by Mercadal-Orfila G, Serrano López de las Hazas J, Riera-Jaume M, Herrera-Perez S

“…Patient-Reported Outcome Measures (PROMs) and Patient-Reported Experience Measures (PREMs) have become essential in evaluating the broader impacts of treatments, especially for chronic conditions like HIV, reflecting patient health and well-being comprehensively.Purpose: The study aims to leverage Machine Learning (ML) technologies to predict health outcomes from PROMs/PREMs data, focusing on people living with HIV.Patients and Methods: Our research utilizes a ML Random Forest Regression to analyze PROMs/PREMs data collected from over 1200 people living with HIV through the NAVETA telemedicine system.Results: The findings demonstrate the potential of ML algorithms to provide precise and consistent predictions of health outcomes, indicating high reliability and effectiveness in clinical settings. …”
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Machine Learning-Based Alzheimer’s Disease Stage Diagnosis Utilizing Blood Gene Expression and Clinical Data: A Comparative Investigation by Manash Sarma, Subarna Chatterjee

“…DL, support vector machine (SVM), gradient boosting (GB), and random forest (RF) classifiers were used for the AD stage detection from gene expression profile data. …”
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Recommendations for developing, documenting, and distributing data products derived from NEON data by Jeff W. Atkins, Kelly S. Aho, Xuan Chen, Andrew J. Elmore, Rich Fiorella, Wenqi Luo, Danica Lombardozzi, Claire Lunch, Leah Manak, Luis X. dePablo, Allison N. Myers‐Pigg, Sydne Record, Tong Qiu, Samuel Reed, Benjamin Ruddell, Brandon Strange, Christa L. Torrens, Kelsey Yule, Andrew D. Richardson

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Comparing Deep Learning models for mapping rice cultivation area in Bhutan using high-resolution satellite imagery by Biplov Bhandari, Timothy Mayer

“…This study focuses on Paro, one of the top rice-yielding districts in Bhutan, and employs publicly available Norway’s International Climate and Forest Initiative (NICFI) high-resolution satellite imagery from Planet Labs. …”
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Predicting ixodid tick distribution in Tamil Nadu domestic mammals using ensemble species distribution models by Ayyanar Elango, Hari Kishan Raju, Ananganallur Nagarajan Shriram, Ashwani Kumar, Manju Rahi

“…Haemaphysalis spinigera, the primary Kyasanur Forest Disease vector, was predicted along the Western Ghats using the MaxEnt model. …”
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The T4/T3 quotient as a risk factor for differentiated thyroid cancer: a case control study by Mark Sasson, Emily Kay-Rivest, Rami Shoukrun, Anca Florea, Michael Hier, Veronique-Isabelle Forest, Michael Tamilia, Richard J. Payne

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Determining the status of ecosystem degradation trends and their implications for ecological integrity in the southern African grassland biome by L.R. Vukeya, T.M. Mokotjomela, N. Pillay

“…We recorded eleven prominent land cover use classes dominated by agricultural activities accounting for 31.9 % (365,629 km2) of which 27.4 % was cultivated area, and 4.5 % was forest plantation, and human settlement covered 4.2 %. …”
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Identification of Potential lncRNAs and miRNAs as Diagnostic Biomarkers for Papillary Thyroid Carcinoma Based on Machine Learning by Fei Yang, Jie Zhang, Baokun Li, Zhijun Zhao, Yan Liu, Zhen Zhao, Shanghua Jing, Guiying Wang

“…Optimal diagnostic lncRNA and miRNA biomarkers were identified via random forest. The regulatory network between optimal diagnostic lncRNA and mRNAs and optimal diagnostic miRNA and mRNAs was identified, followed by the construction of ceRNA network of lncRNA-mRNA-miRNA. …”
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Design of Intelligent Feature Selection Technique for Phishing Detection by Sharvari Sagar Patil, Narendra M. Shekokar, Sridhar Chandramohan Iyer

“…Based on the evaluation, our proposed methodology of dynamic feature selection gives the best accuracy of 99.07 % with the random forest classifier model. Our work contributes to advancing phishing detection methodology by developing a dynamic feature selection technique. …”
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Microinvasive interventions in the management of proximal caries lesions in primary and permanent teeth- systematic review and meta-analysis by Robina Tasleem, Saleh Ali Alqahtani, Naif Abogazalah, Hussain Almubarak, Ammara Riaz, Sarosh Sher Ali, Zeeshan Allana

“…The publication bias was assessed using forest plot and there was no significant publication bias. …”
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Factors and Reasons Associated with Hesitating to Seek Care for Migraine: Results of the OVERCOME (US) Study by Robert E. Shapiro, Eva Jolanda Muenzel, Robert A. Nicholson, Anthony J. Zagar, Michael L. Reed, Dawn C. Buse, Susan Hutchinson, Sait Ashina, Eric M. Pearlman, Richard B. Lipton

“…Supervised machine learning (random forest, least absolute shrinkage and selection operator) identified factors associated with hesitation; logistic regression models assessed association of factors on hesitation. …”
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Stochastic Modeling of Adaptive Trait Evolution in Phylogenetics: A Polynomial Regression and Approximate Bayesian Computation Approach by Dwueng-Chwuan Jhwueng, Chia-Hua Chang

“…We also plan to apply models to the empirical study using the two datasets: the longevity vs. fecundity in the Mediterranean nekton group, and the trophic niche breadth vs. body mass in carnivores in a European forest region.…”
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Analisis Perubahan Luas Lahan Hijau Di Kota Bogor Dengan Citra Landsat 8 Menggunakan Normalized Difference Vegetation Index by Asep Denih, Irma Anggraeni, Runanto

“…The use of land for infrastructure has a significant impact on the reduction of agricultural land and forests, which can automatically reduce the level of vegetation density. …”
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