Deciphering the cellular and molecular landscape of pulmonary fibrosis through single-cell sequencing and machine learning by Yong Zhou, Zhongkai Tong, Xiaoxiao Zhu, Chunli Wu, Ying Zhou, Zhaoxing Dong

“…This study leverages single-cell sequencing and machine learning to unravel the complex cellular and molecular mechanisms underlying pulmonary fibrosis, aiming to improve diagnostic accuracy and uncover potential therapeutic targets. …”
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Machine learning for clustering and classification of early knee osteoarthritis using single-leg standing kinematics by Ui-Jae Hwang, Kyu Sung Chung, Sung-Min Ha

“…This study investigated the application of machine learning techniques to single-leg standing (SLS) kinematics to classify and predict EOA. (1) To identify distinct groups based on SLS kinematic patterns using unsupervised learning algorithms, (2) to develop supervised learning models to predict EOA status, and (3) to identify the most influential kinematic variables associated with EOA. …”
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Advanced computational tools, artificial intelligence and machine-learning approaches in gut microbiota and biomarker identification by Tikam Chand Dakal, Caiming Xu, Caiming Xu, Abhishek Kumar, Abhishek Kumar

“…This review hunts through the cutting-edge computational methodologies that integrate multi-omics data—such as metagenomics, metaproteomics, and metabolomics—providing a comprehensive understanding of the gut microbiome's composition and function. Additionally, machine learning (ML) approaches, including deep learning and network-based methods, are explored for their ability to uncover complex patterns within microbiome data, offering unprecedented insights into microbial interactions and their link to host health. …”
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LEVERAGING MACHINE LEARNING METHODS IN PREDICTING AND ANALYZING THE ASSOCIATION BETWEEN DIETARY INFLAMMATORY INDEX AND ALOPECIA by Mohammed Sarwat M Salih, Hawal Lateef Fateh, Soran Abdulkarim Pasha, Hassan M Tawfiq

“…This study presents evidence about the association between inflammatory food patterns and AA, which may provide important implications for future treatment and dietary interventions. …”
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Combination of machine learning and Raman spectroscopy for prediction of drug release in targeted drug delivery formulations by Wael A. Mahdi, Adel Alhowyan, Ahmad J. Obaidullah

“…Abstract In this research, advanced regression techniques are investigated for modeling intricate release patterns utilizing a high-dimensional dataset comprising more than 1500 spectrum-based variables and categorical inputs. …”
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Exploring high opioid prescriptions among nephrologists in the United States using machine learning algorithms by Shivashankar Basapura Chandrashekarappa, Sulaf Assi, Manoj Jayabalan, Abdullah Al-Hamid, Dhiya Al-Jumeily

“…As these factors are complex in nature, understanding them requires machine learning approach. This study explored overprescribing opioids among nephrologists in the US using unsupervised machine learning algorithms. …”
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Defect Detection and Correction in OpenMP: A Static Analysis and Machine Learning-Based Solution by Norah A. Al-Johany, Fathy E. Eassa, Sanaa A. Sharaf, Eynas H. Balkhair, Sara M. Assiri

“…To enhance predictive accuracy, the tool incorporates machine learning classifiers—Naive Bayes (NB), Decision Tree (DT), Random Forest (RF), and Linear Support Vector Machine (LSVM)—trained on various feature combinations, including Abstract Features (AF), Halstead Features (HF), and Semantic Features (SF). …”
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Depression Detection in Social Media: A Comprehensive Review of Machine Learning and Deep Learning Techniques by Waleed Bin Tahir, Shah Khalid, Sulaiman Almutairi, Mohammed Abohashrh, Sufyan Ali Memon, Jawad Khan

“…The rapidly growing world of social media sites such as Twitter, Reddit, Facebook, Instagram, and Weibo has provided new avenues for depression detection using Machine Learning (ML) as well as Deep Learning (DL), which analyze user behavior patterns and linguistic cues for more accurate detection of depression. …”
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Using Permutation-Based Feature Importance for Improved Machine Learning Model Performance at Reduced Costs by Adam Khan, Asad Ali, Jahangir Khan, Fasee Ullah, Muhammad Faheem

“…This task is commonly achieved through Machine Learning (ML) techniques, but improving model performance typically incurs significant computational costs. …”
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Enhanced wind power forecasting using machine learning, deep learning models and ensemble integration by T. A. Rajaperumal, C. Christopher Columbus

“…To overcome these limitations, this study applies advanced machine learning (ML) and deep learning (DL) techniques with systematic hyperparameter tuning to enhance predictive performance. …”
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Machine Learning-Based Detection of Icebergs in Sea Ice and Open Water Using SAR Imagery by Zahra Jafari, Pradeep Bobby, Ebrahim Karami, Rocky Taylor

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Predicting depressive symptoms through social support: a machine learning approach in military populations by Kun-Huang Chen, Pao-Lung Chiu, Ming-Hsuan Chen

“…Feature importance analyses using the Gini index indicated that different support sources (e.g. leader, peer, senior student) played varying roles across subgroups.Conclusions: Machine learning approaches demonstrate high AUPRC in predicting depressive symptoms and reveal nuanced subgroup patterns in perceived social support needs. …”
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Penetration Testing and Machine Learning-Driven Cybersecurity Framework for IoT and Smart City Wireless Networks by Tamara Zhukabayeva, Zulfiqar Ahmad, Aigul Adamova, Nurdaulet Karabayev, Yerik Mardenov, Dina Satybaldina

“…Anomalies were identified using an optimized Isolation Forest model, revealing patterns such as unusual activity involving the Tenda_476300 WiFi network. …”
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Predicting drug-target interactions using machine learning with improved data balancing and feature engineering by Md. Alamin Talukder, Mohsin Kazi, Ammar Alazab

“…These results demonstrate the efficacy of the GAN-based approach in capturing complex patterns, significantly improving DTI prediction outcomes. …”
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Investigating the performance of random oversampling and genetic algorithm integration in meteorological drought forecasting with machine learning by Tahsin Baykal, Özlem Terzi, Gülsün Yıldırım, Emine Dilek Taylan

“…However, traditional drought monitoring approaches are limited in dealing with data imbalances and capturing complex temporal patterns. Therefore, this study aims to evaluate the effectiveness of machine learning methods for meteorological drought estimation and to integrate Random Oversampling (ROS) and Genetic Algorithm (GA) methods to improve estimation accuracy. …”
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Machine learning-based prediction of scale formation in produced water as a tool for environmental monitoring by Arash Tayyebi, Ali Alshami, Erfan Tayyebi, Ademola Owoade, MusabbirJahan Talukder, Nadhem Ismail, Zeinab Rabiei, Xue Yu, Glavic Tikeri

“…This is primarily due to the continuous variation in salt concentrations, temperature and pressure affecting inorganic scale composition. Machine learning (ML) as a data-driven method is a powerful tool for uncovering hidden patterns in experimental data necessary for decision-making on scale formation predictions by analyzing the complex relationships between mainly the water chemistry and the pH. …”
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Single-cell and machine learning integration reveals ferroptosis-driven immune landscapes for melanoma stratification by Lei Wang, Lei Wang, Xueying Jin, Yuchen Wu, Runing Qiu, Jianfang Wang

“…This study aims to construct a multi-omics framework combining ferroptosis-related signatures, immune infiltration patterns, and machine-learning approaches to stratify melanoma patients and guide therapeutic decision-making.MethodsWe developed a multi-omics framework integrating bulk transcriptomics (TCGA/GEO), single-cell RNA sequencing, and machine learning to decode melanoma's ferroptosis-immune axis. …”
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Parkinson disease detection based on in-air dynamics feature extraction and selection using machine learning by Jungpil Shin, Abu Saleh Musa Miah, Koki Hirooka, Md. Al Mehedi Hasan, Md. Maniruzzaman

“…While this method can capture broad patterns, it has several limitations, including a lack of focus on dynamic change, oversimplified feature representation, a lack of directional information, and missing micro-movements or subtle variations. …”
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Evaluation of Machine Learning Models for Estimating Grassland Pasture Yield Using Landsat-8 Imagery by Linming Huang, Fen Zhao, Guozheng Hu, Hasbagan Ganjurjav, Rihan Wu, Qingzhu Gao

“…These data, combined with field-measured pasture yields, were employed to construct models using four machine learning algorithms: elastic net regression (Enet), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM). …”
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Identification of hub genes in myocardial infarction by bioinformatics and machine learning: insights into inflammation and immune regulation by Juan Yang, Xiang Li, Li Ma, Jun Zhang

“…A systematic analysis will be conducted using bioinformatics and machine learning methods.MethodsGene expression data of GSE60993, GSE61144, GSE66360 and GSE48060 from four datasets were collected from the Gene Expression Omnibus (GEO) database. …”
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