Estimating progression of Alzheimer’s disease with extracellular vesicle-related multi-omics risk models by Xiao Zhang, Xiao Zhang, Sanoji Wijenayake, Shakhawat Hossain, Qian Liu, Qian Liu

“…Significant risk factors included demographic features (age, sex) and genes significant for progression in transcriptomics data. …”
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Natural language processing to identify suicidal ideation and anhedonia in major depressive disorder by L. Alexander Vance, Leslie Way, Deepali Kulkarni, Emily O. C. Palmer, Abhijit Ghosh, Melissa Unruh, Kelly M. Y. Chan, Amey Girdhari, Joydeep Sarkar

“…A novel transformer architecture-based NLP model was trained on clinical notes to recognize linguistic patterns and contextual cues. …”
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A comparative study of kinetic balance and static balance among football, volleyball, basketball, and handball team game players using the Balance Error Scoring System test by Nihad Ayyub Qadir, Nawazad Hussien Darwesh, Awara Sabr Hamad, Marian Cretu

“…Tailoring training to these sport-specific demands is crucial for optimizing athlete performance and well-being.…”
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Predicting indoor temperature of solar green house by machine learning algorithms: A comparative analysis and a practical approach by Wenhe Liu, Tao Han, Cong Wang, Feng Zhang, Zhanyang Xu

“…GRU, by virtue of its more concise gating mechanism (featuring only update gates and reset gates), not only ensured high precision but also significantly improved training efficiency compared to LSTM. …”
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Detecting faults in electronic combustion engine control systems by acoustic parameters by V. D. Gabidulin, V. N. Dobromirov

“…One of the key evolution areas of modern power plants is the introduction of automated control and monitoring systems based on the use of electronic and microprocessor technologies. …”
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Identification of circulating tumor cells marker genes as prognostic signature in triple-negative breast cancer by Jia Hu, Kai-Ming Zhang, Xi Wang

“…The TCGA database served as the training cohort for the development of a prognostic CTCs signature model, while the METABRIC dataset was utilized as the validation cohort to assess the robustness of the CTCs signature model. …”
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Prediction of microvascular invasion in hepatocellular carcinoma with conventional ultrasound, Sonazoid-enhanced ultrasound, and biochemical indicator: a multicenter study by Dan Lu, Li-Fan Wang, Hong Han, Lin-Lin Li, Wen-Tao Kong, Qian Zhou, Bo-Yang Zhou, Yi-Kang Sun, Hao-Hao Yin, Ming-Rui Zhu, Xin-Yuan Hu, Qing Lu, Han-Sheng Xia, Xi Wang, Chong-Ke Zhao, Jian-Hua Zhou, Hui-Xiong Xu

“…Univariate and multivariate logistic regression analyses on clinical information, biochemical indicator, and US imaging features were performed in the training set to seek independent predictors for MVI-positive. …”
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Gadoxetic acid-enhanced MRI for identifying cholangiocyte phenotype hepatocellular carcinoma by interpretable machine learning: individual application of SHAP by Wei Liu, Zhiping Cai, Yifan Chen, Xingqun Guan, Jieying Feng, Haixiong Chen, Baoliang Guo, Fusheng OuYang, Chun Luo, Rong Zhang, Xinjie Chen, Xiaohong Li, Cuiru Zhou, Shaomin Yang, Ziwei Liu, Qiugen Hu

“…Five machine learning models were constructed based on these features. A Kaplan–Meier survival analysis aims to compare prognostic differences between cholangiocyte phenotype-positive HCC groups and classical (cholangiocyte phenotype-negative) HCC groups, and was conducted to explore the prognostic information of the optimal model. …”
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Nomogram model using serum Club cell secretory protein 16 to predict prognosis and acute exacerbation in patients with idiopathic pulmonary fibrosis by Yaqiong Tian, Xuan Zhou, Mi Tian, Lijun Ren, Ruyi Zou, Hanyi Jiang, Miaomiao Xie, Mei Huang, Jingjing Ding, Yin Liu, Jingyu Chen, Min Cao, Hourong Cai

“…All patients were randomly divided into training and testing sets. COX regression and LASSO algorithm were used to screen featured characteristics. …”
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Deep learning and radiomics fusion for predicting the invasiveness of lung adenocarcinoma within ground glass nodules by Qian Sun, Lei Yu, Zhongquan Song, Can Wang, Wei Li, Wang Chen, Juan Xu, Shuhua Han

“…In this retrospective study, 252 pathologically confirmed cases of ground-glass nodules (GGNs) were included, with 177 allocated to the training set and 75 to the testing set. Radiomics, 2D deep learning, and 3D deep learning models were constructed based on CT images. …”
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Methods to improve teaching effciency for students from the People’s Republic of China at technical universities by K. P. Baslyk, V. P. Pechnikov, N. A. Tukhtarova

“…The research materials include printed works for which the appropriate methods of analysis of scientific texts are used: ● excerpts from the development programs of space industry and joint educational programs of Russia and China – aspect and diachronic method of analysis of priority areas of development of the higher school of China and promising areas of cooperation; ● the curricula of professional education in various areas and specialties of Bauman Moscow State Technical University – aspect analysis of the general provisions of engineering training programs; ● manuals on the “Basics of Rocket and Space Technology” (BRST) of Russia (USSR) and China – critical, comparative and conceptual analysis of the content features, methods and style of presentation of the material; ● BRST – aspect and phenomenological analysis of the structure and content of a special technical discipline; ● scientific works on pedagogy and psychology – aspect and system analysis of features of the education system, educational behavior and bilingual opportunities of students from China.The paper applied the problem analysis method to tackle the task in the context of publications’ shortage on teaching special technical disciplines to foreigners. …”
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Volumetric atlas of the rat inner ear from microCT and iDISCO+ cleared temporal bones by Daniele Cossellu, Elisa Vivado, Laura Batti, Ivana Gantar, Roberto Pizzala, Paola Perin

“…These can be used for teaching, localizing cells or other features within the ear, modeling auditory and vestibular sensory physiology and training of automated segmentation machine learning tools.…”
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Predicting 14-day readmission in middle-aged and elderly patients with pneumonia using emergency department data: a multicentre retrospective cohort study with a survival machine l... by Ta-Chien Chan, Jiunn-Horng Kang, Tian-Shin Yeh, Nguyen Thanh Nhu, Jer-Hwa Chang, Yu-Tien Tzeng, Chia-Chieh Wu, Carlos Lam

“…Input features included demographics, comorbidities, clinical events, vital signs, laboratory results and medical interventions. …”
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Disease activity and treatment response in early rheumatoid arthritis: an exploratory metabolomic profiling in the NORD-STAR cohort by Tahzeeb Fatima, Yuan Zhang, Georgios K. Vasileiadis, Araz Rawshani, Ronald van Vollenhoven, Jon Lampa, Bjorn Gudbjornsson, Espen A. Haavardsholm, Dan Nordström, Gerdur Gröndal, Kim Hørslev-Petersen, Kristina Lend, Marte S. Heiberg, Merete Lund Hetland, Michael Nurmohamed, Mikkel Østergaard, Till Uhlig, Tuulikki Sokka-Isler, Anna Rudin, Cristina Maglio

“…Machine learning models for treatment response were constructed using random forest, logistic regression, support vector machine and extreme gradient boosting algorithms based on selected features. Results We identified 278 metabolites, of which 39 were associated with baseline disease activity, including several acylcarnitines and amino acids. …”
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戶外教育課程評估指標建構之研究 Indicators for Evaluating Outdoor Education Curricula by 鄭智明 Chi Meng Cheang, 陳美燕 Mei-Yen Chen, 郭雄軍 Husing-Chun Kuo

“…The goal was to develop a basic model for the integration of outdoor education with school-based education. This study was conducted in two steps. …”
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Machine Learning Models Derived from [¹⁸F]FDG PET/CT for the Prediction of Recurrence in Patients with Thymomas by Angelo Castello, Luigi Manco, Margherita Cattaneo, Riccardo Orlandi, Lorenzo Rosso, Giorgio Alberto Croci, Luigia Florimonte, Giovanni Scribano, Alessandro Turra, Stefano Ferrero, Mario Nosotti, Gianpaolo Carrafiello, Massimo Castellani, Paolo Mendogni

“…The dataset was split into training (70%) and validation (30%) sets. Two ML models (PET- and CT-based, respectively), each with three classifiers—Random Forest (RF), Support-Vector-Machine, and Tree—were trained and internally validated using RFts and clinico-metabolic signatures. …”
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A Machine Learning Model for Prostate Cancer Prediction in Korean Men by Sukjung Choi, Beomgi So, Shane Oh, Hongzoo Park, Sang Wook Lee, Geehyun Song, Jong Min Lee, Jung Ki Jo, Seon Hyeok Kim, Si Eun Lee, Eun-Bi Cho, Jae Hung Jung, Jeong Hyun Kim

“…Features were selected based on their contributions to model performance, leading to the inclusion of 15 features. …”
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Agenda setting for health equity assessment through the lenses of social determinants of health using machine learning approach: a framework and preliminary pilot study by Maryam Ramezani, Mohammadreza Mobinizadeh, Ahad Bakhtiari, Hamid R. Rabiee, Maryam Ramezani, Hakimeh Mostafavi, Alireza Olyaeemanesh, Ali Akbar Fazaeli, Alireza Atashi, Saharnaz Sazgarnejad, Efat Mohamadi, Amirhossein Takian

“…Additionally, CHAID in numeric models was the best for predicting the actual value of life expectancy based on various features. These models highlighted the importance of features like current health expenditure, domestic general government health expenditure, and GDP in predicting life expectancy. …”
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Objective assessment of gait and posture symptoms in Parkinson’s disease using wearable sensors and machine learning by Lingyan Ma, Lingyan Ma, Shinuan Lin, Shinuan Lin, Jianing Jin, Jianing Jin, Zhan Wang, Zhan Wang, Xuemei Wang, Xuemei Wang, Zhonglue Chen, Zhonglue Chen, Yun Ling, Yun Ling, Fei Zhang, Fei Zhang, Kang Ren, Kang Ren, Tao Feng, Tao Feng, Tao Feng

“…Among these, key features with significant clinical relevance were identified. …”
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GBM-Reservoir: Brain tumor (Glioblastoma Multiforme) MRI dataset collection with ground truth segmentation masksfigshare by Naida Solak, André Ferreira, Gijs Luijten, Behrus Puladi, Victor Alves, Jan Egger

“…This dataset can be utilized for various tasks, such as developing fully automated segmentation algorithms for new, unseen brain tumor cases, particularly through deep learning-based approaches, since ground truth is provided for each sample.…”
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