Clustering Electrophysiological Predisposition to Binge Drinking: An Unsupervised Machine Learning Analysis by Marcos Uceta, Alberto del Cerro‐León, Danylyna Shpakivska‐Bilán, Luis M. García‐Moreno, Fernando Maestú, Luis Fernando Antón‐Toro

“…Methods In this article, using unsupervised machine learning (UML) algorithms, we analyze the relationship between electrophysiological activity of healthy teenagers and the levels of consumption they had 2 years later. …”
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Evaluating Material Parameter Influence on Drapability Using VStitcher by Malin Schiller, Soraya Flohr, Jeanne Marisol Delmas, Anja Krüger, Anne-Marie Miene, Patrizia Zimmermann, Lena Wolf, Miriam Seffers, Michelle-Sophie Ruchay-Drammeh, Ulrike Reinhardt, Lilia Sabantina

“…The prerequisite for 3D garment simulation is the correct application of standardized simulation programs, which is rarely accomplished in practice because the providers of 3D simulation software do not disclose their simulation algorithms, making accurate analysis difficult. In this study, an objective image comparison is carried out using the VStitcher simulation program as an example, which allows an assessment of the value of the relevant material parameters. …”
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Effect of phosphorus fractions on benthic chlorophyll-a: Insight from the machine learning models by Yuting Wang, Sangar Khan, Zongwei Lin, Xinxin Qi, Kamel M. Eltohamy, Collins Oduro, Chao Gao, Paul J. Milham, Naicheng Wu

“…To address this gap, we applied two machine learning algorithms—random forest (RF), and artificial neural networks (ANN) to predict benthic chl-a concentrations by incorporating these specific P fractions as separate variables. …”
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Machine learning identifies lipid-associated genes and constructs diagnostic and prognostic models for idiopathic pulmonary fibrosis by Xingren Liu, Junmei Song, Shujin Guo, Yi Liao, Jun Zou, Liqing Yang, Caiyu Jiang

“…Weighted gene co-expression network analysis (WGCNA) was performed on the identified clusters to determine core modules and hub genes associated with IPF. Machine learning algorithms were applied to these hub genes to construct diagnostic and prognostic models, which were validated across multiple datasets. …”
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Radiomics model based on computed tomography images for prediction of radiation-induced optic neuropathy following radiotherapy of brain and head and neck tumors by Elham Raiesi Nafchi, Pedram Fadavi, Sepideh Amiri, Susan Cheraghi, Maryam Garousi, Mansoureh Nabavi, Iman Daneshi, Marzieh Gomar, Malihe Molaie, Ali Nouraeinejad

“…Decision Tree and Random Forest with the highest AUC (97 % and 95 % respectively) among the five classifiers were the most powerful algorithms to predict radiation-induced optic neuropathy. …”
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The role of LAMA5 in breast cancer progression and its potential in immunotherapy by Xiaoli Zhang, Hongfang Yan, Lihan Bie, Zheng Xu, Xiaoyang Yao, Zhilan Li, Chengshan He, Zhouhong Xiang, Xiudi Jiang

“…Immune infiltration was evaluated using CIBERSORT and ESTIMATE algorithms. Single-cell RNA sequencing and spatial transcriptomics were used to explore the spatial distribution and cellular localization of LAMA5. …”
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A panting behavior-driven assessment framework for summer ventilation quality optimization in layer houses by Zixuan Zhou, Lihua Li, Hao Xue, Yuchen Jia, Yao Yu, Zongkui Xie, Yuhan Gu

“…K-means clustering and linear regression algorithms were employed to establish a quantitative correlation curve between ventilation quality and panting behavior, forming a Normal-Alert-Danger ventilation quality (VQ) classification standard. …”
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Pan-Cancer Analysis of Disulfidptosis-Related Genes Affecting Prognosis and Tumor Microenvironment by Jingyang SUN, Rongxuan JIANG, Liren HOU, Huanhuan DONG, Yihan LIN, Niuniu DONG, Guangjian ZHANG, Yanpeng ZHANG

“…MethodsPan-cancer RNA-seq data, mutation profiles, clinical information, TMB, MSI, stemness scores, and tumor and immune microenvironment data contained in TCGA and various open-source online databases, and multi-group R-language algorithms were used for comprehensive analysis. The expression levels of DRGs at the cellular level were experimentally validated using qPCR. …”
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Computer Vision Corrections Enhance UAV-Based Retrievals in Shallow Waters by Dario Scilla, Omar A. Lopez, Brian Owain Nieuwenhuis, Kasper Johansen, Mariana Elias-Lara, Victor Angulo, Jorge L. Rodriguez, Burton H. Jones, Matthew F. McCabe

“…By applying color transferring algorithms and median filtering, we achieved greater than 10% improvement in the correlation between frames at low altitudes (<20 m height), where the refraction phenomenon is more dominant, while also restoring geometries with errors of less than 5% compared with the true shape dimensions. …”
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Visible, near-infrared, and shortwave-infrared spectra as an input variable for digital mapping of soil organic carbon by Vahid Khosravi, Asa Gholizadeh, Radka Kodešová, Prince Chapman Agyeman, Mohammadmehdi Saberioon, Luboš Borůvka

“…Models were developed on environmental predictor covariates (EPCs) and thirty genetic algorithms (GA)-selected visible, near-infrared, and shortwave-infrared (VNIR–SWIR) wavelengths spectra, individually and combined. …”
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Radiomics-based Machine Learning Approach to Predict Chemotherapy Responses in Colorectal Liver Metastases by Yuji Miyamoto, Takeshi Nakaura, Mayuko Ohuchi, Katsuhiro Ogawa, Rikako Kato, Yuto Maeda, Kojiro Eto, Masaaki Iwatsuki, Yoshifumi Baba, Toshinori Hirai, Hideo Baba

“…Employing Random Forest and Boruta algorithms, we identified significant features for responder-non-responder differentiation. …”
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Deep learning for predicting rehospitalization in acute heart failure: Model foundation and external validation by Mi‐Na Kim, Yong Seok Lee, Youngmin Park, Ayoung Jung, Hanjee So, Joonwoong Park, Jin‐Joo Park, Dong‐Joo Choi, So‐Ree Kim, Seong‐Mi Park

“…In performing deep learning‐based predictive algorithms for HF rehospitalization, we use hyperbolic tangent activation layers followed by recurrent layers with gated recurrent units. …”
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Using composable NFTs and blockchain for the creation of EV battery digital passports with sustainability and traceability features by Haya R. Hasan, Khaled Salah, Ahmad Mayyas, Ahmad Musamih, Ibrar Yaqoob, Mohammed Omar, Raja Jayaraman

“…We present a system architecture, smart contracts, and supporting algorithms are presented, with testing and validation in a simulated environment. …”
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SUMOylation-related genes define prognostic subtypes in stomach adenocarcinoma: integrating single-cell analysis and machine learning analyses by Kaiping Luo, Kaiping Luo, Donghui Xing, Donghui Xing, Xiang He, Yixin Zhai, Yanan Jiang, Hongjie Zhan, Zhigang Zhao

“…A SUMOylation Risk Score (SRS) model was developed using 69 machine learning models across 10 algorithms, with performance evaluated by C-index and AUC. …”
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Exploring the potential of antifungal-loaded proniosomes to consolidate corneal permeation in fungal keratitis: A comprehensive investigation from laboratory characterization to mi... by Sadek Ahmed, Michael M. Farag, Heba Attia, Bander Balkhi, Islam M. Adel, Asmaa Ashraf Nemr

“…Numerical desirability algorithms selected an optimum TCZ-PNS which boasted plausible %EE (89.51 % ± 0.94 %), nanoscale vesicles consistent with TEM measurements (247.9 ± 0.42 nm), a sufficiently high ZP (−43.42 ± 0.85 mV), and an in-vitro biphasic release profile that remained stable even after Gamma irradiation and short-term storage. …”
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Pulmonary hypertension associated with COPD: a phenotype analysis by Mathilde Steger, Matthieu Canuet, Guillaume Martin, Aissam Labani, Jean Charles Schwartz, Irina Enache, Armelle Schuller, Léo Meyer, Ari Chaouat, Romain Kessler, David Montani, Marianne Riou

“…In the current debate on how to distinguish PH-COPD phenotypes, it might be of interest to include quantitative thresholds for emphysema in future diagnostic and management algorithms.…”
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Tiny dLIF: a dendritic spiking neural network enabling a time-domain energy-efficient seizure detection system by Luis Fernando Herbozo Contreras, Leping Yu, Zhaojing Huang, Ziyao Zhang, Armin Nikpour, Omid Kavehei

“…Drawing inspiration from brain architecture, we investigate biologically plausible algorithms, specifically emphasizing time-domain inputs with low computational overhead. …”
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Preoperative digital 6-minute walk test reveals risk of postoperative pulmonary complications in patients undergoing heart valve surgery: a pilot feasibility study by Lixuan Li, Yuqiang Wang, Zhengbo Zhang, Zeruxin Luo, Wenqing Wang, Jiachen Wang, Xiaoli Liu, Ying Shi, Tian Yuan, Yong Fan, Hong Liang, Yingqiang Guo, Buqing Wang, Jing Wang, Jiaoxue Deng

“…We extracted 94 physiological features across 6MWT phases (baseline, walking, recovery) and clinical variables, developing predictive models using five machine learning algorithms evaluated through rigorous five-fold cross-validation. …”
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DKK3 and SERPINB5 as novel serum biomarkers for gastric cancer: facilitating the development of risk prediction models for gastric cancer by Yan-Yu Liu, Yan-Yu Liu, Yan-Fang Fu, Yan-Fang Fu, Wan-Yu Yang, Wan-Yu Yang, Zheng Li, Zheng Li, Qian Lu, Qian Lu, Xin Su, Xin Su, Jin Shi, Si-Qi Wu, Di Liang, Yu-Tong He, Yu-Tong He

“…Multiple machine learning algorithms were used to identify key predictors for the GC risk prediction model, which was validated with an independent external cohort from multiple hospitals. …”
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Diagnosis and Severity Assessment of COPD Using a Novel Fast-Response Capnometer and Interpretable Machine Learning by Leeran Talker, Cihan Dogan, Daniel Neville, Rui Hen Lim, Henry Broomfield, Gabriel Lambert, Ahmed Selim, Thomas Brown, Laura Wiffen, Julian Carter, Helen F. Ashdown, Gail Hayward, Elango Vijaykumar, Scott T. Weiss, Anoop Chauhan, Ameera X. Patel

“…This study’s aim was to use interpretable machine learning to diagnose COPD and assess severity using 75-second carbon dioxide (CO2) breath records captured with TidalSense’s N-TidalTM capnometer.Method For COPD diagnosis, machine learning algorithms were trained and evaluated on 294 COPD (including GOLD stages 1–4) and 705 non-COPD participants. …”
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