A MALDI-ToF mass spectrometry database for identification and classification of highly pathogenic bacteria by Peter Lasch, Wolfgang Beyer, Alejandra Bosch, Rainer Borriss, Michal Drevinek, Susann Dupke, Monika Ehling-Schulz, Xuewen Gao, Roland Grunow, Daniela Jacob, Silke R. Klee, Armand Paauw, Jörg Rau, Andy Schneider, Holger C. Scholz, Maren Stämmler, Le Thi Thanh Tam, Herbert Tomaso, Guido Werner, Joerg Doellinger

“…We hope that our MALDI-ToF MS data may also be a valuable resource for developing machine learning-based bacterial identification and classification methods.…”
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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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Time-Series Forecasting of PM_2.5 and PM₁₀ Concentrations Based on the Integration of Surveillance Images by Yong Wu, Xiaochu Wang, Meizhen Wang, Xuejun Liu, Sifeng Zhu

“…Experiments conducted on the 2021 Shanghai dataset demonstrate that the proposed model significantly outperforms traditional machine learning methods in terms of accuracy and robustness for time-series forecasting, achieving <i>R</i>² values of 0.9459 and 0.9045 and RMSE values of 4.79 μg/m³ and 11.51 μg/m³ for PM_2.5 and PM₁₀, respectively. …”
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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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IA na investigação, educação e prática da engenharia sísmica e estrutural - uma reflexão sobre impactos, desafios e direções futuras by Tiago Ferreira

“…Nos últimos anos, a integração de ferramentas baseadas em inteligência artificial (IA), aprendizagem automática—machine learning (ML) na terminologia anglo-saxónica—e deep-learning (DL), tem vindo a reformular os paradigmas tradicionais. …”
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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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Clinical Decision Support Using Speech Signal Analysis: Systematic Scoping Review of Neurological Disorders by Upeka De Silva, Samaneh Madanian, Sharon Olsen, John Michael Templeton, Christian Poellabauer, Sandra L Schneider, Ajit Narayanan, Rahmina Rubaiat

“…From these tasks, conventional speech features (such as fundamental frequency, jitter, and shimmer), advanced digital signal processing–based speech features (such as wavelet transformation–based features), and spectrograms in the form of audio images were analyzed. Traditional machine learning and deep learning approaches were used to build predictive models, whereas statistical analysis assessed variable relationships and reliability of speech features. …”
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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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