Deciphering the proteome of Escherichia coli K-12: Integrating transcriptomics and machine learning to annotate hypothetical proteins by Sagarika Chakraborty, Zachary Ardern, Habibu Aliyu, Anne-Kristin Kaster

“…We further provide experimental validation of in silico predicted functions for three HP-encoding genes (yhdN, yeaC and ydgH) as proof of concept, by analyzing growth patterns of deletion mutants compared to the wild type, as well as their transcriptional responses to specific conditions. …”
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Automatic priority analysis of emergency response systems using internet of things (IoT) and machine learning (ML) by Abu S.M. Mohsin, Shadab H. Choudhury, Munyem Ahammad Muyeed

“…ABSTRACT: Effective and timely resource deployment is essential during emergencies. By integrating machine learning (ML) and the Internet of Things (IoT), automatic priority analysis of emergency response systems could revolutionise this vital process, save life and minimize damages. …”
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Enhanced Gold Ore Classification: A Comparative Analysis of Machine Learning Techniques with Textural and Chemical Data by Fabrizzio Rodrigues Costa, Cleyton de Carvalho Carneiro, Carina Ulsen

“…Several supervised and unsupervised machine learning methods and applications integrate a wide variety of algorithms that aim at the efficient recognition of patterns and similarities and the ability to make accurate and assertive decisions. …”
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Identifying Diagnostic Biomarkers for Electroacupuncture Treatment of Rheumatoid Arthritis Using Bioinformatic Analysis and Machine Learning Algorithms by Sun Y, Dong G, Gao H, Yao Y, Yang H

“…A rat model of RA was established using Complete Freund’s Adjuvant (CFA), and quantitative real-time PCR was performed to confirm the differential expression of identified diagnostic biomarkers and assess the modulatory impact of EA on these genes.Results: Twenty-six genes were identified as differentially expressed following EA treatment. Three machine learning algorithms converged on ARHGAP17 and VEGFB as potential diagnostic biomarkers for RA, exhibiting robust diagnostic performance (AUC > 0.75) and consistent expression patterns across multiple RA cohorts (GSE17755, GSE205962 and GSE93272). …”
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Mathematics and Machine Learning for Visual Computing in Medicine: Acquisition, Processing, Analysis, Visualization, and Interpretation of Visual Information by Bin Li, Shixiang Feng, Jinhong Zhang, Guangbin Chen, Shiyang Huang, Sibei Li, Yuxin Zhang

“…Visual computing in medicine involves handling the generation, acquisition, processing, analysis, exploration, visualization, and interpretation of medical visual information. Machine learning has become a prominent tool for data analytics and problem-solving, which is the process of enabling computers to automatically learn from data and obtain certain knowledge, patterns, or input–output relationships. …”
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Well Performance from Numerical Methods to Machine Learning Approach: Applications in Multiple Fractured Shale Reservoirs by Kailei Liu, Boyue Xu, Changjea Kim, Jing Fu

“…This paper presents a thorough analysis of the feasibility of machine learning in multiple fractured shale reservoirs. …”
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Advanced Machine Learning and Deep Learning Approaches for Estimating the Remaining Life of EV Batteries—A Review by Daniel H. de la Iglesia, Carlos Chinchilla Corbacho, Jorge Zakour Dib, Vidal Alonso-Secades, Alfonso J. López Rivero

“…This systematic review presents a critical analysis of advanced machine learning (ML) and deep learning (DL) approaches for predicting the remaining useful life (RUL) of electric vehicle (EV) batteries. …”
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A multi-biomarker machine learning approach for early prediction of interstitial lung disease in rheumatoid arthritis by Jiaojiao Xu, Wei Zhang, Weili Bai, Nannan Gai, Jing Li, Yunqi Bao

“…The ILD group exhibited significantly elevated levels of inflammatory markers and specific biomarkers, particularly KL-6 (826.4 ± 458.2 vs. 285.6 ± 124.8 U/ml, P < 0.001), alongside distinct patterns in hematological parameters. Conclusion Machine learning approaches, particularly XGBoost, demonstrate promising potential for early RA-ILD prediction. …”
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Identification of novel diagnostic and prognostic microRNAs in sarcoma on TCGA dataset: bioinformatics and machine learning approach by Rahem Rahmati, Fatemeh Zarimeidani, Farnaz Ahmadi, Hannaneh Yousefi-Koma, Abdolreza Mohammadnia, Maryam Hajimoradi, Shadi Shafaghi, Elham Nazari

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A derecho climatology (2004–2021) in the United States based on machine learning identification of bow echoes by J. Li, A. Geiss, Z. Feng, L. R. Leung, Y. Qian, W. Cui, W. Cui

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Machine learning-based hydrograph modeling with LSTM: A case study in the Jatigede Reservoir Catchment, Indonesia by Neil Andika, Piter Wongso, Faizal Immaddudin Wira Rohmat, Siska Wulandari, Ammar Fadhil, Riswanto Rosi, Nabila Siti Burnama

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Identification of potential biomarkers and mechanisms for keloid disorder based on comprehensive bioinformatics analysis and machine learning algorithms by Bowen Zheng, Jianxiong Qiao, Xiaoping Yu, Hanghang Zhou, Anqi Wang, Xuanfen Zhang

“…This study sought to identify biomarkers and potential therapeutic targets for KD through an integrative bioinformatics approach and machine learning analysis of RNA sequencing data. …”
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Machine learning of clinical phenotypes facilitates autism screening and identifies novel subgroups with distinct transcriptomic profiles by Wasana Yuwattana, Thanit Saeliw, Marlieke Lisanne van Erp, Chayanit Poolcharoen, Songphon Kanlayaprasit, Pon Trairatvorakul, Weerasak Chonchaiya, Valerie W. Hu, Tewarit Sarachana

“…This integrated approach combining clinical and molecular data through machine learning offers promising directions for developing more precise screening methods and personalized intervention strategies for individuals with ASD.…”
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Predicting determinants of unimproved water supply in Ethiopia using machine learning analysis of EDHS-2019 data by Jember Azanaw, Mihret Melese, Eshetu Abera Worede

“…The Ethiopia Demographic and Health Survey (EDHS-2019), which offers thorough data on socioeconomic, demographic, and water access determinants, was the data source for this study. The following six machine-learning models were used: k-nearest Neighbors, Random Forest, Support Vector Machines, Gradient Boosting Machines, and Artificial Neural Networks. …”
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Metode Deteksi Intrusi Menggunakan Algoritme Extreme Learning Machine dengan Correlation-based Feature Selection by Sulandri Sulandri, Achmad Basuki, Fitra Abdurrachman Bachtiar

“…Intrusion detection is the process of monitoring traffic on a network to detect any data patterns that are considered suspicious, which allows network attacks. …”
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Bind: large-scale biological interaction network discovery through knowledge graph-driven machine learning by Naafey Aamer, Muhammad Nabeel Asim, Aamer Iqbal Bhatti, Andreas Dengel

“…Results Architecturally simpler embedding models captured biological interaction patterns, often outperforming complex approaches. The two-stage training strategy achieved improvements up to 26.9% for protein-protein interactions. …”
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Unveiling new insights into migraine risk stratification using machine learning models of adjustable risk factors by Yu-Chen Liu, Ye-Hai Liu, Hai-Feng Pan, Wei Wang

“…Second, we trained ensemble machine learning (ML) algorithms that incorporated these factors, with Shapley Additive exPlanations (SHAP) value analysis quantifying predictor importance. …”
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Estimation of the air conditioning energy consumption of a classroom using machine learning in a tropical climate by Liliana Ortega-Diaz, Julian Jaramillo-Ibarra, German Osma-Pinto

“…In this study, three machine learning models were used to predict the air conditioning energy demand in a classroom of an educational building in a hot tropical climate. …”
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Comparative Study of Cell Nuclei Segmentation Based on Computational and Handcrafted Features Using Machine Learning Algorithms by Rashadul Islam Sumon, Md Ariful Islam Mozumdar, Salma Akter, Shah Muhammad Imtiyaj Uddin, Mohammad Hassan Ali Al-Onaizan, Reem Ibrahim Alkanhel, Mohammed Saleh Ali Muthanna

“…<b>Methods:</b> This work explores machine learning approaches for nuclei segmentation by evaluating the quality of nuclei image segmentation. …”
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Self-Disclosure and Social Support in a Web-Based Opioid Recovery Community: Machine Learning Analysis by Yu Chi, Huai-yu Chen, Khushboo Thaker

“…By uncovering interaction patterns, this study provides valuable insights for leveraging online support groups as complementary resources to traditional recovery interventions.…”
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