Integrating fractional-order derivatives of soil and leaf hyperspectral reflectance for improved estimation of mangrove soil organic carbon by Yibo Luo, Chunlin Li, Jinhong Huang, Chengcheng Dong, Junjie Wang

“…Incorporating key soil and terrain variables (e.g. soil iron, clay content, pH, salinity, redox potential, and elevation) into the spectra-based SOC estimation model significantly enhanced prediction accuracy, highlighting the complementary roles of spectral signals, soil characteristics, and topographic features in SOC modeling. …”
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GIS Analysis Model Integration and Service Composition Prospects by L. Ding, P. Cai, W. Huang, H. Zhang, F. Ding, W. Zhao, D. Tang, Z. Wang

“…Key algorithms are systematically integrated to optimize outcomes in urban planning, disaster management, and precision agriculture. …”
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Electrical discharge machining: Recent advances and future trends in modeling, optimization, and sustainability by Muhamad Taufik Ulhakim, Sukarman, Khoirudin, Dodi Mulyadi, Hendri Susilo, Rohman, Muji Setiyo

“…Optimization approaches, including machine learning-based algorithms, multi-objective optimization, and hybrid methods, have enhanced key performance indicators, such as material removal rate (MRR), surface quality, and tool wear, thereby increasing process efficiency and reducing machining time. …”
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Early diagnosis of acute myocardial infarction via hub genes identified by integrated weighted gene co-expression network analysis by Kun Huang, Feng Wen, Jingyi Li, Wenhao Niu, Hui Chen, Shilei Wan, Fupeng Yang, Yihong Chen, Chun Liang

“…A total of 276 intersecting genes were markedly associated with AMI in the pink and turquoise modules. Based on multiple machine learning algorithms and independent validation, six genes including LILRA1, CCL20, IL1R2, TYROBP, CXCL16 and NFKBIA were identified as hub genes and showed satisfactory diagnostic efficiency both in the discovery cohort and validation cohort. …”
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Integrating machine learning and single-cell sequencing to identify shared biomarkers in type 1 diabetes mellitus and clear cell renal cell carcinoma by Yi Li, Rui Zeng, Rui Zeng, Yuhua Huang, Yumin Zhuo, Jun Huang

“…Subsequently, the LASSO and SVM algorithms were employed to identify shared hub genes between the two diseases. …”
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Comprehensive multi-omics integration uncovers mitochondrial gene signatures for prognosis and personalized therapy in lung adenocarcinoma by Wenjia Zhang, Lei Zhao, Tiansheng Zheng, Lihong Fan, Kai Wang, Guoshu Li

“…By leveraging an ensemble of machine learning algorithms, we developed an Artificial Intelligence-Derived Prognostic Signature (AIDPS) model based on mitochondrial-related genes and validated its prognostic accuracy across multiple independent datasets. …”
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Machine learning unveils key Redox signatures for enhanced breast Cancer therapy by Tao Wang, Shu Wang, Zhuolin Li, Jie Xie, Kuiying Du, Jing Hou

“…Future work will focus on clinical validation and exploring the mechanistic roles of identified genes in cancer biology.…”
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Use of ICT to Confront COVID-19 by Yousry Saber El Gamal

“…The pandemic highlighted the crucial role played by Information and Communication Technology in keeping businesses running and societies functional in times of lockdowns and quarantines. …”
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Integrative analysis of semaphorins family genes in colorectal cancer: implications for prognosis and immunotherapy by Jiahao Zhu, Benjie Xu, Zhixing Wu, Zhixing Wu, Zhiwei Yu, Shengjun Ji, Jie Lian, Haibo Lu

“…BackgroundSemaphorins (SEMAs), originally identified as axon guidance factors, have been found to play crucial roles in tumor growth, invasiveness, neoangiogenesis, and the modulation of immune responses. …”
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Unveiling new insights into migraine risk stratification using machine learning models of adjustable risk factors by Yu-Chen Liu, Ye-Hai Liu, Hai-Feng Pan, Wei Wang

“…Second, we trained ensemble machine learning (ML) algorithms that incorporated these factors, with Shapley Additive exPlanations (SHAP) value analysis quantifying predictor importance. …”
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SUMOylation-related genes define prognostic subtypes in stomach adenocarcinoma: integrating single-cell analysis and machine learning analyses by Kaiping Luo, Kaiping Luo, Donghui Xing, Donghui Xing, Xiang He, Yixin Zhai, Yanan Jiang, Hongjie Zhan, Zhigang Zhao

“…A SUMOylation Risk Score (SRS) model was developed using 69 machine learning models across 10 algorithms, with performance evaluated by C-index and AUC. …”
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Shared gene signatures and molecular mechanisms link ankylosing spondylitis and rheumatoid arthritis by Boli Qin, Xiaopeng Qin, Jie Ma, Chenxing Zhou, Tianyou Chen, Jichong Zhu, Chengqian Huang, Shaofeng Wu, Rongqing He, Songze Wu, Sitan Feng, Jiarui Chen, Jiang Xue, Wendi Wei, Tengxiang Long, Quan Pan, Kechang He, Zhendong Qin, Tiejun Zhou, Jiayan Jiang, Xinli Zhan, Chong Liu

“…The CBC data of 23,289 patients were collected, and six machine learning algorithms were applied to develop disease prediction models for AS and RA. …”
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Machine Learning-Driven Transcriptome Analysis of Keratoconus for Predictive Biomarker Identification by Shao-Hsuan Chang, Lung-Kun Yeh, Kuo-Hsuan Hung, Yen-Jung Chiu, Chia-Hsun Hsieh, Chung-Pei Ma

“…Selected feature genes were further analyzed through Gene Ontology (GO) enrichment to explore their roles in biological processes and cellular functions. …”
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Integrating bioinformatics and machine learning to identify biomarkers of branched chain amino acid related genes in osteoarthritis by Xiao-Zhi ZhaYang, Yan-Xiong Chen, Wen-Da Hua, Zheng-Lin Bai, Yun-Peng Jin, Xing-Wen Zhao, Quan-Fu Liu, Zeng-Dong Meng

“…Subsequently, by combining three machine learning algorithms to identify genes with highly correlated OA features. …”
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Collaborative management of water-agriculture-energy-ecology nexus for increasing carbon sequestration to sustainable development: A case study in inland river Northwest China by Xiaoyu Tang, Yue Huang, Xiaohui Pan, Yunan Ling, Chanjuan Zan, Jiabin Peng, Xi Chen, Tie Liu

“…This study proposes a WAEE nexus co-optimization model based on mixed-coding multi-objective evolutionary algorithms (EMCMO). …”
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Clinician Attitudes and Perceptions of Point-of-Care Information Resources and Their Integration Into Electronic Health Records: Qualitative Interview Study by Marlika Marceau, Sevan Dulgarian, Jacob Cambre, Pamela M Garabedian, Mary G Amato, Diane L Seger, Lynn A Volk, Gretchen Purcell Jackson, David W Bates, Ronen Rozenblum, Ania Syrowatka

“…MethodsSemistructured interviews were conducted with 10 clinicians from various roles and specialties between December 2021 and January 2022 at Brigham and Women’s Hospital in Boston, Massachusetts. …”
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Optimized Landing Site Selection at the Lunar South Pole: A Convolutional Neural Network Approach by Yongjiu Feng, Haoteng Li, Xiaohua Tong, Pengshuo Li, Rong Wang, Shurui Chen, Mengrong Xi, Jingbo Sun, Yuhao Wang, Huaiyu He, Chao Wang, Xiong Xu, Huan Xie, Yanmin Jin, Sicong Liu

“…Potential landing sites identified comprise less than 1% of the total study area, with factors such as visibility, volatile distribution, topography, and geological characteristics playing crucial roles. By applying operational constraints, we delineate sites suitable for direct landings and further refine this subset for base construction based on stringent requirements for resource utilization and energy sustainability. …”
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Research Status and Prospects of Key Technologies for Rice Smart Unmanned Farms by YU Fenghua, XU Tongyu, GUO Zhonghui, BAI Juchi, XIANG Shuang, GUO Sien, JIN Zhongyu, LI Shilong, WANG Shikuan, LIU Meihan, HUI Yinxuan

“…Rice yield estimation technology is mainly used to predict yield by combining multi-source data and algorithms, but there are still problems such as the difficulty of integrating multi-source data, which requires further research. …”
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