Automated on-site broiler live weight estimation through YOLO-based segmentation by Mahmoud Y. Shams, Wael M. Elmessery, Awad Ali Tayoush Oraiath, Ahmed Elbeltagi, Ali Salem, Pankaj Kumar, Tamer M. El-Messery, Tarek Abd El-Hafeez, Mohamed F. Abdelshafie, Gomaa G. Abd El-Wahhab, Ibrahim S. El-Soaly, Abdallah Elshawadfy Elwakeel

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Application of deep learning models on single-cell RNA sequencing analysis uncovers novel markers of double negative T cells by Tian Xu, Qin Xu, Ran Lu, David N. Oakland, Song Li, Liwu Li, Christopher M. Reilly, Xin M. Luo

“…They have increasingly gained recognition for their novel roles in the immune system, especially under autoimmune conditions. Conventional machine learning approaches such as principal component analysis have been employed in single-cell RNA sequencing (scRNA-seq) analysis to characterize DNT cells. …”
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Using partially shared radiomics features to simultaneously identify isocitrate dehydrogenase mutation status and epilepsy in glioma patients from MRI images by Yida Wang, Ankang Gao, Hongxi Yang, Jie Bai, Guohua Zhao, Huiting Zhang, Yang Song, Chenglong Wang, Yong Zhang, Jingliang Cheng, Guang Yang

“…Abstract Prediction of isocitrate dehydrogenase (IDH) mutation status and epilepsy occurrence are important to glioma patients. Although machine learning models have been constructed for both issues, the correlation between them has not been explored. …”
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Point-of-care lung ultrasonography for early identification of mild COVID-19: a prospective cohort of outpatients in a Swiss screening center by Olivier Hugli, Jean-Yves Meuwly, Noémie Boillat-Blanco, Pierre-Nicolas Carron, Siméon Schaad, Thomas Brahier, Mary-Anne Hartley, Jean-Baptiste Cordonnier, Luca Bosso, Tanguy Espejo, Olivier Pantet

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Social media discourse and internet search queries on cannabis as a medicine: A systematic scoping review. by Christine Mary Hallinan, Sedigheh Khademi Habibabadi, Mike Conway, Yvonne Ann Bonomo

“…It also demonstrates the need for the development of a systematic approach for evaluating the quality of social media studies and highlights the utility of automatic processing and computational methods (machine learning technologies) for large social media datasets. …”
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Screening of necroptosis-related genes and evaluating the prognostic capacity, clinical value, and the effect of their copy number variations in acute myeloid leukemia by Dake Wen, Ru Yan, Lin Zhang, Haoyang Zhang, Xuyang Chen, Jian Zhou

“…Methods Necroptosis-related differentially expressed genes (NRDEGs) were identified after intersecting differentially expressed genes (DEGs) from the Gene Expression Omnibus(GEO) database with NRGs from GeneCards, the Molecular Signatures Database (MSigDB) and literatures. Machine learning was applied to obtain hub-NRDEGs. The expression levels of the hub-NRDEGs were validated in vitro. …”
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Toward super-clean bearing steel by a novel physical-data integrated design strategy by Jian Guan, Guolei Liu, Wenguang Hu, Hongwei Liu, Paixian Fu, Yanfei Cao, Dong-Rong Liu, Dianzhong Li

“…To address this issue, a physical-data integrated design strategy was developed to optimize vacuum arc remelting (VAR) parameters, combining numerical simulation, machine learning (ML), and experimental validation. Initially, a multi-phase, multi-physics coupled model was developed to predict the movement and distribution of inclusions during the VAR process. …”
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Phytocompounds from Indonesia Medicinal Herbs as Potential Apelin Receptor Agonist for Heart Failure Therapy: An In-silico Approach by Muhamad Rizqy Fadhillah, Wawaimuli Arozal, Muhammad Habiburrahman, Somasundaram Arumugam, Heri Wibowo, Suci Widya Primadhani, Aryo Tedjo, Surya Dwira, Nurul Gusti Khatimah

“…This study investigates bioactive phytochemicals from ten Indonesian medicinal herbs using computer-aided drug design (CADD) to predict ligand-receptor interactions via molecular docking and bioactivity prediction through machine learning. The selected herbs include Andrographis paniculata, Centella asiatica, Zingiber officinale, Curcuma longa, Curcuma domestica, Morinda citrifolia, Guazuma ulmifolia, Orthosiphon stamineus, Moringa oleifera, and Garcinia mangostana. …”
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Deep learning and hyperspectral features for seedling stage identification of barnyard grass in paddy field by Siqiao Tan, Qiang Xie, Wenshuai Zhu, Yangjun Deng, Lei Zhu, Xiaoqiao Yu, Zheming Yuan, Zheming Yuan, Yuan Chen, Yuan Chen

“…Notably, this surpasses the capabilities of other models that rely on amalgamations of machine learning algorithms and feature dimensionality reduction methods. …”
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Combining a Risk Factor Score Designed From Electronic Health Records With a Digital Cytology Image Scoring System to Improve Bladder Cancer Detection: Proof-of-Concept Study by Sandie Cabon, Sarra Brihi, Riadh Fezzani, Morgane Pierre-Jean, Marc Cuggia, Guillaume Bouzillé

“…MethodsThe first step relied on designing a predictive model based on clinical data (ie, risk factors identified in the literature) extracted from the clinical data warehouse of the Rennes Hospital and machine learning algorithms (logistic regression, random forest, and support vector machine). …”
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Integration of clinical, pathological, radiological, and transcriptomic data improves prediction for first-line immunotherapy outcome in metastatic non-small cell lung cancer by Nicolas Captier, Marvin Lerousseau, Fanny Orlhac, Narinée Hovhannisyan-Baghdasarian, Marie Luporsi, Erwin Woff, Sarah Lagha, Paulette Salamoun Feghali, Christine Lonjou, Clément Beaulaton, Andrei Zinovyev, Hélène Salmon, Thomas Walter, Irène Buvat, Nicolas Girard, Emmanuel Barillot

“…In this study, we explore the benefits of multimodal approaches to predict immunotherapy outcome using multiple machine learning algorithms and integration strategies. …”
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Radiomic prediction for durable response to high‐dose methotrexate‐based chemotherapy in primary central nervous system lymphoma by Haoyi Li, Mingming Xiong, Ming Li, Caixia Sun, Dao Zheng, Leilei Yuan, Qian Chen, Song Lin, Zhenyu Liu, Xiaohui Ren

“…For each patient, a total of 1218 radiomic features were extracted from prebiopsy T1 contrast‐enhanced MR images. Multiple machine‐learning algorithms were utilized for feature selection and classification to build a radiomic signature. …”
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Development of the relationship between visual selective attention and auditory change detection by Yuanjun Kong, Xuye Yuan, Yiqing Hu, Bingkun Li, Dongwei Li, Jialiang Guo, Meirong Sun, Yan Song

“…Further, we employed both ERP analysis and multivariate pattern machine learning to investigate developmental patterns. …”
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TransRAUNet: A Deep Neural Network with Reverse Attention Module Using HU Windowing Augmentation for Robust Liver Vessel Segmentation in Full Resolution of CT Images by Kyoung Yoon Lim, Jae Eun Ko, Yoo Na Hwang, Sang Goo Lee, Sung Min Kim

“…<b>Method:</b> As a segmentation method, UNet is widely used as a baseline, and a multi-scale block or attention module has been introduced to extract context information. In recent machine learning efforts, not only has the global context extraction been improved by introducing Transformer, but a method to reinforce the edge area has been proposed. …”
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Tropical Cyclone Size Prediction and Development of An Error Correction Method by Guo Ruichen, Xu Jing, Wang Yuqing

“…Based on this relationship, a machine learning model, XGBoost, is used to develop an R17 size correction scheme that incorporate initial and forecast intensity, inner-core and outer-core sizes, and initial errors as predictors to estimate and correct model-predicted size errors. …”
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Identification and validation of a prognostic risk model based on radiosensitivity-related genes in nasopharyngeal carcinoma by Yi Li, Xinyi Hong, Wenqian Xu, Jinhong Guo, Yongyuan Su, Haolan Li, Yingjie Xie, Xing Chen, Xiong Zheng, Sufang Qiu

“…Differentially expressed genes (DEGs) were identified between radiotherapy-sensitive and resistant samples. Machine learning algorithms and Cox regression were used to construct a prognostic risk model, validated in the GSE102349 dataset. …”
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Exploring Magnetic Fields in Molecular Clouds through Denoising Diffusion Probabilistic Models by Duo Xu, Jenna Karcheski, Chi-Yan Law, Ye Zhu, Chia-Jung Hsu, Jonathan C. Tan

“…Accurately measuring magnetic field strength in the interstellar medium, including giant molecular clouds, remains a significant challenge. We present a machine learning approach using denoising diffusion probabilistic models (DDPMs) to estimate magnetic field strength from synthetic observables such as column density, orientation angles of the dust continuum polarization vector, and line-of-sight (LOS) nonthermal velocity dispersion. …”
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Diagnostic impact of DNA methylation classification in adult and pediatric CNS tumors by Laetitia Lebrun, Nathalie Gilis, Manon Dausort, Chloé Gillard, Stefan Rusu, Karim Slimani, Olivier De Witte, Fabienne Escande, Florence Lefranc, Nicky D’Haene, Claude Alain Maurage, Isabelle Salmon

“…DNA methylation classification has emerged as a powerful machine learning approach for clinical decision-making in CNS tumors. …”
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Intelligent Electrochemical Sensing: A New Frontier in On-the-Fly Coffee Quality Assessment by Simone Grasso, Maria Vittoria Di Loreto, Alessandro Zompanti, Davide Ciarrocchi, Laura De Gara, Giorgio Pennazza, Luca Vollero, Marco Santonico

“…A specific experimental setup has been designed, and the data has been analyzed using machine learning techniques. The results obtained from Principal Component Analysis (PCA) and Partial Least Square Discriminant Analysis (PLS-DA) show the sensor’s capability to distinguish between samples of different quality, with a percentage of correct classification of 86.6%. …”
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Discrimination between the facial gestures of vocalising and non-vocalising lemurs and small apes using deep learning by Filippo Carugati, Olivier Friard, Elisa Protopapa, Camilla Mancassola, Emanuela Rabajoli, Chiara De Gregorio, Daria Valente, Valeria Ferrario, Walter Cristiano, Teresa Raimondi, Valeria Torti, Brice Lefaux, Longondraza Miaretsoa, Cristina Giacoma, Marco Gamba

“…We extracted and labelled frames of different primate species, trained deep-learning models to identify key points on their faces, and computed distances between them to identify facial gestures. We used machine learning algorithms to classify vocalised and non-vocalised gestures across different species. …”
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