Monitoring soil salinization in Arid cotton fields using Unmanned Aerial Vehicle hyperspectral imagery by Jinming Zhang, Jianli Ding, Jiao Tan, Jinjie Wang, Zihan Zhang, Zeyuan Wang, Xiangyu Ge

“…The results indicate: (1) SG smoothing and FOD technology effectively enhanced the spectral characteristics of cotton field soils and crop canopies. (2) The soil salinity prediction model constructed using 0.7-order FOD combined with the BOSS algorithm yields the best performance, with an R2 of 0.92, RMSE of 0.15 dS m−1, and RPD of 3.54 on the test set. (3) The soil salinity distribution map generated from the optimal model clearly revealed the spatial distribution characteristics of salinity in the 0–10 cm topsoil layer, with significant differences observed under different cover conditions. …”
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Quantitative Analysis of Structural Parameters Importance of Helical Temperature Microfiber Sensor by Artificial Neural Network by Juan Liu, Minghui Chen, Hang Yu, Jinjin Han, Hongyi Jia, Zhili Lin, Zhijun Wu, Jixiong Pu, Xining Zhang, Hao Dai

“…The relative output intensities of HMF sensor at different temperatures are predicted by the BPNN with the HMF’s structural parameters as the input variables. …”
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Multimodal marvels of deep learning in medical diagnosis using image, speech, and text: A comprehensive review of COVID-19 detection by Md Shofiqul Islam, Khondokar Fida Hasan, Hasibul Hossain Shajeeb, Humayan Kabir Rana, Md. Saifur Rahman, Md. Munirul Hasan, AKM Azad, Ibrahim Abdullah, Mohammad Ali Moni

“…Motivated by the success of artificial intelligence applications during the COVID-19 pandemic, this research aims to uncover the capabilities of DL in disease screening, prediction, and classification, and to derive insights that enhance the resilience, sustainability, and inclusiveness of science, technology, and innovation systems. …”
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Integrated Bioinformatics and Experimental Validation Reveal Macrophage Polarization-Related Biomarkers for Osteoarthritis Diagnosis by He Q, Liu L, Hu X, Lin L, Song Z, Xia Y, Lin Q, Wei J, Li S

“…Least absolute shrinkage and selection operator (LASSO), random forest (RF), and support vector machine recursive feature elimination (SVM-RFE) algorithms were used to identify hub genes and construct a diagnostic model validated through internal datasets and multiple external bulk RNA-seq and single-cell RNA-seq data. …”
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Acoustic Features for Identifying Suicide Risk in Crisis Hotline Callers: Machine Learning Approach by Zhengyuan Su, Huadong Jiang, Ying Yang, Xiangqing Hou, Yanli Su, Li Yang

“…ObjectiveIn this study, we investigated the feasibility of using acoustic features to predict suicide risk in crisis hotline callers. We also adopted a machine learning approach to analyze the complex acoustic features of hotline callers, with the aim of developing suicide risk prediction models. …”
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Development of a Diagnostic Model for Focal Segmental Glomerulosclerosis: Integrating Machine Learning on Activated Pathways and Clinical Validation by Ge Y, Liu X, Shu J, Jiang X, Wu Y

“…We then developed a highly accurate diagnostic model by integrating nine machine learning algorithms into 101 combinations, achieving near-perfect AUC values across training, validation, and external cohorts. …”
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Rethinking the Paradigm of Using Ps for Diagnosing Compartment Syndrome by Yasser Bouklouch, BSc, MPH, July Agel, MA, ATC, William T. Obremskey, MD, MPH, MMHC, Andrew H. Schmidt, MD, Kathy Liu, MB, ChB, Jerald R. Westberg, MPH, Matthew Zakariah, BSc, Eli Bunzel, MD, Greer Henry, MSc, Andres Fidel Diaz, MD, Thierry Bégué, MD, Mitchell Bernstein, MD, Edward J. Harvey, MDCM, MSc

“…The combinations were tested for predictive power using 2 machine learning algorithms. …”
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Diagnostic potential of salivary microbiota in persistent pulmonary nodules: identifying biomarkers and functional pathways using 16S rRNA sequencing and machine learning by Xiao Zeng, Qiong Ma, Chun-Xia Huang, Jun-Jie Xiao, Xi Fu, Yi-Feng Ren, Yu-Li Qu, Hong-Xia Xiang, Mao Lei, Ru-Yi Zheng, Yang Zhong, Ping Xiao, Xiang Zhuang, Feng-Ming You, Jia-Wei He

“…Additionally, the PICRUSt2 algorithm was used to predict the microbial functions. …”
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Early Diagnosis of Knee Osteoarthritis With a Natural Language Processing–Driven Approach Based on Clinician Notes: Development and Validation Study by Narathip Thanyakunsajja, Kulsawasd Jitkajornwanich, Shan Xu, Donghee Shin, Pattama Charoenporn

“…The findings indicate the feasibility of using text data (symptom descriptions reported by patients and recorded by doctors) to predict knee OA. Medical notes of symptom reports can be considered a valuable data source for predicting whether a particular knee is likely to experience OA progression.…”
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ATP6V0A4 as a novel prognostic biomarker and potential therapeutic target in oral squamous cell carcinoma by Xiaopu Gao, Jiamin Zhou, Yu Qiao, Chuyin Lin, Guanxiong Zhang, Qiuyu Wu, Zhikang Su, Qianji Zhang, Songkai Huang

“…Further, immunotherapy response prediction models and drug sensitivity analysis were utilized to define therapeutic potential. …”
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Clinical implications of deep learning based image analysis of whole radical prostatectomy specimens by Tae-Yeong Kwak, Chan Ho Lee, Won Young Park, Ja Yoon Ku, Chang Wook Jeong, Eu Chang Hwang, Seock Hwan Choi, Joonyoung Cho, Hyeyoon Chang, Kyung Hwan Kim, Byeong Jin Kang, Sun Woo Kim, Hong Koo Ha

“…We also evaluated their prognostic performance in predicting biochemical progression-free survival (BPFS). …”
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Innate immune cell barrier-related genes inform precision prognosis in pancreatic cancer by Qiang Luo, Qiang Luo, Tingting Jiang, Tingting Jiang, Dacheng Xie, Xiaojia Li, Xiaojia Li, Keping Xie, Keping Xie

“…This study aimed to identify prognostic biomarkers, develop a predictive model, and uncover novel targets for personalized therapy.MethodsInnate immune cell barrier-related genes were curated from KEGG, ImmPort, MSigDB, and InnateDB. …”
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Exploring the Potential Regulatory Mechanisms of Mitophagy in Ischemic Cardiomyopathy by Li Z, Kong J, Xi S, Jin Z, Yang F, Zhu Z, Liu L

“…Finally, 45 drugs targeting the biomarkers were predicted, such as Salmeterol, Histamine, Rotavirus vaccine, etc. …”
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Research on the spatial layout of the elderly care industry based on explainable machine learning: A case study of Hangzhou(基于可解释性机器学习的养老产业空间布局研究)... by 曾笑奇(ZENG Xiaoqi), 赵秋皓(ZHAO Qiuhao), 冯友建(FENG Youjian)

“…However, existing research has not sufficiently explored the factors influencing the spatial layout of elderly care facilities or how to optimally predict these layouts. This study uses Hangzhou's urban area as a case study to fill this gap. …”
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Incorporating Deep Learning Into Hydrogeological Modeling: Advancements, Challenges, and Future Directions by Zhenxue Dai, Chuanjun Zhan, Huichao Yin, Junjun Chen, Lulu Xu, Yuzhou Xia, Songlin Yang, Wei Chen, Mingxu Cao, Zhengyang Du, Xiaoying Zhang, Bicheng Yan, Yue Ma, Hao Wang, Farzad Moeini, Mohamad Reza Soltanian, Hung Vo Thanh, Kenneth C. Carroll

“…Deep learning (DL) has emerged as a promising tool, offering significant improvements in accuracy and efficiency for tasks such as time series prediction, spatial data analysis, and inverse modeling. …”
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Enhancing liver disease diagnosis with hybrid SMOTE-ENN balanced machine learning models—an empirical analysis of Indian patient liver disease datasets by Ritu Rani, Garima Jaiswal, Nancy, Lipika, Shashi Bhushan, Fasee Ullah, Prabhishek Singh, Manoj Diwakar, Manoj Diwakar

“…Immediate action is necessary for timely diagnosis of the ailment before irreversible damage is done.MethodsThe work aims to evaluate some of the traditional and prominent machine learning algorithms, namely, Logistic Regression, K-Nearest Neighbor, Support Vector Machine, Gaussian Naïve Bayes, Decision Tree, Random Forest, AdaBoost, Extreme Gradient Boosting, and Light GBM for diagnosing and predicting chronic liver disease. …”
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Genetic variants of the DLK1, KISS1R, MKRN3 genes in girls with precocious puberty by E. A. Sazhenova, O. Yu. Vasilyeva, E. A. Fonova, M. B. Kankanam Pathiranage, A. Yu. Sambyalova, E. E. Khramova, L. V. Rychkova, S. A. Vasilyev, I. N. Lebedev

“…The pathogenicity of identified genetic variants and the functional significance of the protein synthesized by them were analyzed according to recommendations for interpretation of NGS analysis results using online algorithms for pathogenicity prediction (Variant Effect Predictor, Franklin, Varsome, and PolyPhen2). …”
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Habitat Analysis in Tumor Imaging: Advancing Precision Medicine Through Radiomic Subregion Segmentation by Wu LX, Ding N, Ji YD, Zhang YC, Li MJ, Shen JC, Hu HT, Jin L, Yin SN

“…By analyzing many literatures, the commonly used K-means algorithm and other algorithms such as hierarchical clustering and consensus clustering are summarized. …”
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A genetic association study of serum acute-phase C-reactive protein levels in rheumatoid arthritis: implications for clinical interpretation. by Benjamin Rhodes, Marilyn E Merriman, Andrew Harrison, Michael J Nissen, Malcolm Smith, Lisa Stamp, Sophia Steer, Tony R Merriman, Timothy J Vyse

“…CRP is increasingly being incorporated into clinical algorithms to compare disease activity between patients and to predict future clinical events: our findings impact on the use of these algorithms. …”
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Validation of chronic obstructive pulmonary disease recording in the Clinical Practice Research Datalink (CPRD-GOLD) by John R Hurst, Jennifer K Quint, Hana Müllerova, Liam Smeeth, Harriet Forbes, Susan Eaton, Rachael L DiSantostefano, Kourtney Davis

“…All information received was reviewed independently by two respiratory physicians whose opinion was taken as the gold standard.Primary outcome measure The primary measure of accuracy was the positive predictive value (PPV), the proportion of people identified by each algorithm for whom COPD was confirmed.Results 951 questionnaires were sent and 738 (78%) returned. …”
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