Comparative Analysis of pKa Predictions for Arsonic Acids Using Density Functional Theory-Based and Machine Learning Approaches by Miroslava Nedyalkova, Diana Heredia, Joaquín Barroso-Flores, Marco Lattuada

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ChromatinHD connects single-cell DNA accessibility and conformation to gene expression through scale-adaptive machine learning by Wouter Saelens, Olga Pushkarev, Bart Deplancke

“…Abstract Gene regulation is inherently multiscale, but scale-adaptive machine learning methods that fully exploit this property in single-nucleus accessibility data are still lacking. …”
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Predictions of Multilevel Linguistic Features to Readability of Hong Kong Primary School Textbooks: A Machine Learning Based Exploration by Zhengye Xu, Yixun Li, Duo Liu

“…Purpose: To develop advanced readability formulas for Chinese texts using machine-learning algorithms that can handle hundreds of predictors. …”
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Multiobjective Optimization of Surface Roughness and Tool Wear in High-Speed Milling of AA6061 by Machine Learning and NSGA-II by Anh-Tu Nguyen, Van-Hai Nguyen, Tien-Thinh Le, Nhu-Tung Nguyen

“…We applied a two-pronged approach that combines machine learning (ML) and a Nondominated Sorting Genetic Algorithm (NSGA-II) to model and optimize Ra and Vbmax. …”
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A machine learning model and identification of immune infiltration for chronic obstructive pulmonary disease based on disulfidptosis-related genes by Sijun Li, Qingdong Zhu, Aichun Huang, Yanqun Lan, Xiaoying Wei, Huawei He, Xiayan Meng, Weiwen Li, Yanrong Lin, Shixiong Yang

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Machine learning reveals sex differences in distinguishing between conduct-disordered and neurotypical youth based on emotion processing dysfunction by Gregor Kohls, Erik M. Elster, Peter Tino, Graeme Fairchild, Christina Stadler, Arne Popma, Christine M. Freitag, Stephane A. De Brito, Kerstin Konrad, Ruth Pauli

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Predicting the Shanghai Composite Index Using Chinese TikTok Self-Media Data and Machine Learning Model in China by Li Zhiming, Han Huijian, Li Zongwei, Zhang Rui

“…In addition, we compared and analyzed the prediction results of several machine learning and deep learning models in the short term, medium term, and long term. …”
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A Machine Learning Approach Using Topic Modeling to Identify and Assess Experiences of Patients With Colorectal Cancer: Explorative Study by Kelly Voigt, Yingtao Sun, Ayush Patandin, Johanna Hendriks, Richard Hendrik Goossens, Cornelis Verhoef, Olga Husson, Dirk Grünhagen, Jiwon Jung

“…Topic modeling, as a part of machine learning, was used to recognize the topic patterns in the posts. …”
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Integrating machine learning with advanced processing and characterization for polycrystalline materials: a methodology review and application to iron-based superconductors by Akiyasu Yamamoto, Akinori Yamanaka, Kazumasa Iida, Yusuke Shimada, Satoshi Hata

“…In this review, we present a new set of machine learning-based materials research methodologies for polycrystalline materials developed through the Core Research for Evolutionary Science and Technology project of the Japan Science and Technology Agency. …”
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Machine learning-driven power prediction in continuous extrusion of pure titanium for enhanced structural resilience under extreme loading by Ahmed Ghazi Abdulameer, Muhannad M. Mrah, Maryam Bazerkan, Luttfi A. Al-Haddad, Mustafa I. Al-Karkhi

Subjects: “…Machine learning…”
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Machine learning identifies the association between second primary malignancies and postoperative radiotherapy in young-onset breast cancer patients by Yulin Lai, Peiyuan Huang

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Enhancing Generalizability of a Machine Learning Model for Infrared Thermographic Defect Detection by Using 3D Numerical Modeling by Vladimir Vavilov, Arsenii Chulkov, Alexey Moskovchenko

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Machine learning-random forest model was used to construct gene signature associated with cuproptosis to predict the prognosis of gastric cancer by Xiaolong Liu, Pengxian Tao, He Su, Yulan Li

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Machine learning based identification of an amino acid metabolism related signature for predicting prognosis and immune microenvironment in pancreatic cancer by Xiaohong Liu, Xing Wang, Jie Ren, Yuan Fang, Minzhi Gu, Feihan Zhou, Ruiling Xiao, Xiyuan Luo, Jialu Bai, Decheng Jiang, Yuemeng Tang, Bo Ren, Lei You, Yupei Zhao

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Retracted: Biocomposite’s Multiple Uses for a New Approach in the Diagnosis of Parkinson’s Disease Using a Machine Learning Algorithm by Adsorption Science and Technology

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A novel machine learning approach for spatiotemporal prediction of EMS events: A case study from Barranquilla, Colombia by Dionicio Neira-Rodado, Juan Camilo Paz-Roa, John Willmer Escobar, Miguel Ángel Ortiz-Barrios

“…The proposed spatiotemporal approach integrates machine learning, signal analysis, and statistical features, capturing geographical, temporal, and event-specific factors. …”
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Construction of an Interpretable Model of the Risk of Post-Traumatic Brain Infarction Based on Machine Learning Algorithms: A Retrospective Study by Li S, Li H, Wu B, Pan R, Liu Y, Wang J, Wei D, Gao H

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Rapid diagnosis of bacterial vaginosis using machine-learning-assisted surface-enhanced Raman spectroscopy of human vaginal fluids by Xin-Ru Wen, Jia-Wei Tang, Jie Chen, Hui-Min Chen, Muhammad Usman, Quan Yuan, Yu-Rong Tang, Yu-Dong Zhang, Hui-Jin Chen, Liang Wang

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Combining GIS and machine learning for enhanced tsunami risk management: A review of current approaches and unexplored future potential by Tahri Ayoub, Beroho Mohamed, Tichli Soufiane, El Talibi Hajar, El Moussaoui Said, Aboumaria Khadija

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ChIMES Carbon 2.0: A transferable machine-learned interatomic model harnessing multifidelity training data by Rebecca K. Lindsey, Sorin Bastea, Sebastien Hamel, Yanjun Lyu, Nir Goldman, Vincenzo Lordi

“…Abstract We present new parameterizations of the ChIMES physics informed machine-learned interatomic model for simulating carbon under conditions ranging from 300 K and 0 GPa to 10,000 K and 100 GPa, along with a new multi-fidelity active learning strategy. …”
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