Single-Cell Sequencing and Machine Learning Integration to Identify Candidate Biomarkers in Psoriasis: INSIG1 by Zhou X, Ning J, Cai R, Liu J, Yang H, Bai Y

“…In addition, tetrandrine shows promise as a potential treatment for the condition.Keywords: psoriasis, single-cell RNA sequencing, machine learning, INSIG1, pseudotime analysis…”
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Identification of progression-related genes and construction of prognostic model for chronic kidney disease by machine learning by Bingkun Zhou, Hu Zhou, Xiaodong Huang, Shijie Liu

“…And the reliability of human CKD transcriptomic analysis and the feasibility of functional studies were validated in a mouse UUO model.ResultsCombining WGCNA and differential gene analysis, 9 genes positively associated with CKD occurrence and development and 20 genes negatively associated with that were identified. …”
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Prediction of Flexural Ultimate Capacity for Reinforced UHPC Beams Using Ensemble Learning and SHAP Method by Zhe Zhang, Xuemei Zhou, Ping Zhu, Zhaochao Li, Yichuan Wang

“…The optimal model functionality may be accomplished by properly considering the effects of database subset distribution on the performance prediction and model stability. …”
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STIED: a deep learning model for the spatiotemporal detection of focal interictal epileptiform discharges with MEG by Raquel Fernández-Martín, Alfonso Gijón, Odile Feys, Elodie Juvené, Alec Aeby, Charline Urbain, Xavier De Tiège, Vincent Wens

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Statistical analysis and machine learning of TMS-induced MEPs for predicting poststroke motor impairment and performance by Chun-Ren Phang, Shintaro Uehara, Sachiko Kodera, Akiko Yuasa, Shin Kitamura, Yohei Otaka, Akimasa Hirata

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AlphaMissense Predictions and ClinVar Annotations: A Deep Learning Approach to Uveal Melanoma by David J. Taylor Gonzalez, MD, Mak B. Djulbegovic, MD, MSc, Meghan Sharma, MD, MPH, Michael Antonietti, BS, Colin K. Kim, BS, Vladimir N. Uversky, PhD, DSc, Carol L. Karp, MD, Carol L. Shields, MD, Matthew W. Wilson, MD

“…We explore the use of a novel deep learning tool to assess the functional impact of genetic mutations in UM. …”
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Multi-Source Rainfall Data Assimilation based on Broad Learning System over Yunnan Province by Yuanyuan ZHOU, Xiaohui YANG, Tiangui XIAO

“…The accurate estimation of rainfall is always a topic of concern， given its pivotal role in accurately predicting rainfall-related disasters.This study proposed a multi-source rainfall assimilation technology based on a broad learning system （BLS） to improve the accuracy of rainfall estimation.Yunnan Province， located in China's low-latitude plateau， was chosen as the geographical area of interest to establish a multi-source rainfall assimilation model within this region.In particular， the model utilizes five satellite-derived rainfall datasets （3B42V7， IMERG， GSMaP， CMORPH， PERSIANN） and the latitude and longitude information as the source data， and the ground-based rainfall gauge data serves as the reference data.The time span of all the datasets is from April 2014 to December 2017.A leave-one-year-out cross-validation （LOYOCV） method was applied to verify the performance of the established assimilation model， where statistical indicators including Pearson’s correlation coefficient （CC）， root-mean square error （RMSE）， mean absolute error （MAE）， Nash efficiency coefficient （NSE） and Kling-Gupta efficiency （KGE） were used to quantify the accuracy of assimilation rainfall at different spatiotemporal scales.Concurrently， assimilation models based on support vector machine （SVM） and deep neural network （DNN） were established to highlight the accuracy and efficiency of the BLS， respectively.Additionally， the effectiveness of the latitude and longitude information within the proposed assimilation model was examined.The results show that the daily average statistical index of assimilation rainfall based on BLS is better than that of the other five satellite-based products in LOYOCV.At the temporal scale， the proposed assimilation technique effectively reflects the temporal variations observed in gauge-recorded rainfall.Moreover， it can accurately estimate the rainfall amounts during rainstorms in Yunnan Province throughout 2017.It is worth noting that the rainfall data generated through the BLS method outperforms the CMORPH product （the most accurate one among the five satellite-derived rainfall products） in both rainy and dry seasons （May to October and November to April of next year， respectively）.At the spatial scale， BLS-based rainfall results in most areas of Yunnan Province showed higher CC and NSE as well as smaller RMSE and MAE than the satellite-based products.The evaluation of the assimilation models based on BLS， SVM， and DNN highlights that the BLS exhibits superior functional mapping capabilities compared to SVM and demands fewer computational resources than DNN.It is reasonable to conclude that the multi-source rainfall assimilation approach utilizing the BLS while incorporating latitude and longitude information can enhance the precision of rainfall estimates in Yunnan Province.The proposed method presents practical significance in multi-source rainfall data assimilation.…”
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GAN-enhanced deep learning for improved Alzheimer's disease classification and longitudinal brain change analysis by Purushottam Pandey, Surbhi Bhatia Khan, Surbhi Bhatia Khan, Surbhi Bhatia Khan, Jyoti Pruthi, Eid Albalawi, Ali Algarni, Ahlam Almusharraf

“…Advancements in artificial intelligence (AI), specifically machine learning (ML) and deep learning (DL), offer promising solutions to these challenges. …”
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A Monocyte-Driven Prognostic Model for Multiple Myeloma: Multi-Omics and Machine Learning Insights by Xie L, Gao M, Tan S, Zhou Y, Liu J, Wang L, Li X

“…Through multi-omics analyses and machine learning algorithms, we established a robust monocyte-related prognostic signature. …”
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Analisis Penerimaan Learning Management System Institut Teknologi Garut Menggunakan Technology Acceptance Model by Asri Mulyani, Dede Kurniadi, Mita Hidayani Putri

“…It is hoped that the results of this research can be used as a reference for developers to continue to optimize the functionality of the Learning Management System so that it can be used optimally. …”
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Contrastive learning with transformer for adverse endpoint prediction in patients on DAPT post-coronary stent implantation by Fang Li, Zenan Sun, Ahmed abdelhameed, Tiehang Duan, Laila Rasmy, Xinyue Hu, Jianping He, Yifang Dang, Jingna Feng, Jianfu Li, Yichen Wang, Tianchen Lyu, Naomi Braun, Si Pham, Michael Gharacholou, DeLisa Fairweather, Degui Zhi, Jiang Bian, Cui Tao

“…Meanwhile, the model was holistically optimized using multiple loss functions, to ensure the predicted results closely align with the ground-truth values from various perspectives. …”
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Identification of Clusters in a Population With Obesity Using Machine Learning: Secondary Analysis of The Maastricht Study by Maik JM Beuken, Melanie Kleynen, Susy Braun, Kees Van Berkel, Carla van der Kallen, Annemarie Koster, Hans Bosma, Tos TJM Berendschot, Alfons JHM Houben, Nicole Dukers-Muijrers, Joop P van den Bergh, Abraham A Kroon, Iris M Kanera

“…For cluster 3 (n=1149), the most significant continuous variable was overall higher cognitive functioning (mean 0.2349, SD 0.5702 vs mean –0.3088, SD 0.7212; P<.001), and educational level was the most significant categorical variable (P<.001). …”
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An Attention‐Aware Multi‐Task Learning Framework Identifies Candidate Targets for Drug Repurposing in Sarcopenia by Md Selim Reza, Chuan Qiu, Xu Lin, Kuan‐Jui Su, Anqi Liu, Xiao Zhang, Yun Gong, Zhe Luo, Qing Tian, Martin Nwadiugwu, Shaung Liang, Hui Shen, Hong‐Wen Deng

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Machine learning-derived diagnostic model of epithelial ovarian cancer based on gut microbiome signatures by Cheng Chen, Chengyuan Deng, Yanwen Li, Shuguang He, Yunhong Liu, Shuwen Pan, Wenqian Xu, Lu Fang, Yixi Zhu, Yingying Wang, Xiaoxin Jiang

“…The clinical data and pathological characteristics were comprehensively recorded for further analysis, PICRUSt2 was utilized to conduct an analysis of microbial functional predictions, WGCNA networks were constructed by integrating microbiome and clinical data. …”
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Integrating CT radiomics and clinical data with machine learning to predict fibrosis progression in coalworker pneumoconiosis by Xiaobing Li, Xiaobing Li, Xiaobing Li, Xiaobing Li, Xiaobing Li, Qian Li, Qian Li, Qian Li, Qian Li, Xinyi Xie, Wei Wang, Xuemei Li, Tingqiang Zhang, Tingqiang Zhang, Tingqiang Zhang, Tingqiang Zhang, Li Zhang, Li Zhang, Li Zhang, Li Zhang, Yongsheng Liu, Yongsheng Liu, Yongsheng Liu, Yongsheng Liu, Li Wang, Li Wang, Li Wang, Li Wang, Wutao Xie

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Effects of TrkB-related induced metaplasticity within the BLA on anxiety, extinction learning, and plasticity in BLA-modulated brain regions by Joyeeta Dutta Hazra, Kuldeep Shrivastava, Lisa-Sophie Wüstner, Rachel Anunu, Erez Chervinsky, Somoday Hazra, Simone Beuter, Martin Kriebel, Mouna Maroun, Hansjuergen Volkmer, Gal Richter-Levin

“…Conclusions Taken together, these findings reveal the dissociative involvement of BLA function, on the one hand, in anxiety, which is affected by the knockdown of TrkB, and, on the other hand, in extinction learning, which is more significantly affected by the combination of intra-BLA-induced metaplasticity and exposure to emotional trauma.…”
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An effective method for anomaly detection in industrial Internet of Things using XGBoost and LSTM by Zhen Chen, ZhenWan Li, Jia Huang, ShengZheng Liu, HaiXia Long

“…Finally, combining the optimal threshold and loss function, we propose a model named MIX_LSTM for anomaly detection in IIoT. …”
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Improving the Skills of Demand Function Counting and Demand Curve Drawing Using Drill Method and Think Pair Share (TPS) by Semi Mulyani, Ani Sofiani

“…<p>This research aims to determine whether there is an increased of demand function counting and demand curve drawing using drill method and think pair share. …”
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THE PROBLEM OF STANDARDS MEASURABILITY IN HIGHER VOCATIONAL EDUCATION by Y. A. Shikhov, O. F. Shikhova, A. A. Kasatkin

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Anonymous whistleblowers reply scheme based on secret sharing by HE Kun, HUANG Yajing, DU Ruiying, SHI Min, LI Siqin, CHEN Jing

“…With the technology of distributed point functions and secret sharing, the message was stored in two separate mailbox databases of non-colluding servers, so that the identity of the data receiver was hidden from the attacker. …”
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