Development of standard fuel models in boreal forests of Northeast China through calibration and validation. by Longyan Cai, Hong S He, Zhiwei Wu, Benard L Lewis, Yu Liang

“…Fuel model parameter sensitivity was analyzed by the Morris screening method. …”
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Orga-Dete: An Improved Lightweight Deep Learning Model for Lung Organoid Detection and Classification by Xuan Huang, Qin Gao, Hanwen Zhang, Fuhong Min, Dong Li, Gangyin Luo

“…Lung organoids play a crucial role in modeling drug responses in pulmonary diseases. However, their morphological analysis remains hindered by manual detection inefficiencies and the high computational cost of existing algorithms. …”
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Construction and Validation of Predictive Model to Identify Critical Genes Associated with Advanced Kidney Disease by Guangda Xin, Guangyu Zhou, Wenlong Zhang, Xiaofei Zhang

“…Differential expressed genes (DEGs) were identified and functional enrichment analysis. Machine learning algorithm-based prediction model was constructed to identify crucial functional feature genes related to ESRD. …”
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T cell receptor signaling pathway subgroups and construction of a novel prognostic model in osteosarcoma by Huan Xu, Huimin Tao

“…Two hundred and seventy-two Differential expressed TCRGs were screened between two subclusters. A robust prognostic model were constructed. …”
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Establishment of interpretable cytotoxicity prediction models using machine learning analysis of transcriptome features by You Wu, Ke Tang, Chunzheng Wang, Hao Song, Fanfan Zhou, Ying Guo

“…In summary, the models established in this research exhibit superior capacity to those of previous studies; these models enable accurate high-safety substance screening via cytotoxicity prediction across cell lines. …”
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Constructing a fall risk prediction model for hospitalized patients using machine learning by Cheng-Wei Kang, Zhao-Kui Yan, Jia-Liang Tian, Xiao-Bing Pu, Li-Xue Wu

“…Univariate analysis and least absolute shrinkage and selection operator (LASSO) regression were used to analyze and screen variables. Predictive models were constructed by integrating key clinical features, and eight machine learning algorithms were evaluated to identify the most effective model. …”
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Development and Validation of a Discrete Element Simulation Model for Pressing Holes in Sowing Substrates by Hongmei Xia, Chuheng Deng, Teng Yang, Runxin Huang, Jianhua Ou, Lingjin Dong, Dewen Tao, Long Qi

“…A neural network model for predicting the angle of repose was constructed, and a genetic algorithm was applied to optimize the significant contact mechanical parameters. …”
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Gait-Based AI Models for Detecting Sarcopenia and Cognitive Decline Using Sensor Fusion by Rocío Aznar-Gimeno, Jose Luis Perez-Lasierra, Pablo Pérez-Lázaro, Irene Bosque-López, Marina Azpíroz-Puente, Pilar Salvo-Ibáñez, Martin Morita-Hernandez, Ana Caren Hernández-Ruiz, Antonio Gómez-Bernal, María de la Vega Rodrigalvarez-Chamarro, José-Víctor Alfaro-Santafé, Rafael del Hoyo-Alonso, Javier Alfaro-Santafé

“…<b>Conclusions</b>: The study demonstrates that gait analysis through sensor and CV fusion can effectively screen for sarcopenia and CD. The multimodal approach enhances model accuracy, potentially supporting early disease detection and intervention in home settings.…”
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A prognostic model of 8-T/B cell receptor-related signatures for hepatocellular carcinoma by Xuan Zuo, Hui Li, Shi Xie, Mengfen Shi, Yujuan Guan, Huiyuan Liu, Rong Yan, Anqi Zheng, Xueying Li, Jiabang Liu, Yifan Gan, Haiyan Shi, Keng Chen, Shijie Jia, Guanmei Chen, Min Liao, Zhanhui Wang, Yanyan Han, Baolin Liao

“…Conclusions Together, our study screened a TCR/BCR-related signature prognostic model, which might turn into a beneficial and practical tool to solve the perplexities of the treatment, prognosis prediction and management for HCC patients.…”
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Construction of Diagnostic Model for Regulatory T Cell-Related Genes in Sepsis Based on Machine Learning by Xuesong Wang, Zhe Guo, Xinrui Wang, Zhong Wang

“…Thus, we utilized multiple machine learning algorithms to screen and extract Treg-related genes associated with sepsis diagnosis. …”
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Integrated bioinformatics analysis to develop diagnostic models for malignant transformation of chronic proliferative diseases by Hua Liu, Sheng Lin, Pei-Xuan Chen, Juan Min, Xia-Yang Liu, Ting Guan, Chao-Ying Yang, Xiao-Juan Xiao, De-Hui Xiong, Sheng-Jie Sun, Ling Nie, Han Gong, Xu-Sheng Wu, Xiao-Feng He, Jing Liu

“…Integrated public datasets of PV and AML were analyzed to identify differentially expressed genes (DEGs) and construct a weighted correlation network. Machine-learning algorithms screen genes for potential biomarkers, leading to the development of diagnostic models. …”
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Few-shot hotel industry site selection prediction method based on meta learning algorithms and transportation accessibility by Na Li, Huaishi Wu

“…Then, a transportation accessibility calculation model is constructed using spatial syntax for secondary screening. …”
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Development of prediction models for screening depression and anxiety using smartphone and wearable-based digital phenotyping: protocol for the Smartphone and Wearable Assessment f... by Sujin Kim, Ah Young Kim, Yu-Bin Shin, Seonmin Kim, Min-Sup Shin, Jinhwa Choi, Kyung Lyun Lee, Jisu Lee, Sangwon Byun, Heon-Jeong Lee, Chul-Hyun Cho

“…The Smartphone and Wearable Assessment for Real-Time Screening of Depression and Anxiety study aims to develop prediction algorithms to identify individuals at risk for depressive and anxiety disorders, as well as those with mild-to-severe levels of either condition or both. …”
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Identification of immune-related biomarkers linked to systemic lupus erythematosus and dilated cardiomyopathy through integrated bioinformatics analysis and multiple machine learni... by Gaijie Li, Liwen Lin, Shushu Wang, Kachun Lu, KaMan Szeto, Guiting Zhou, Xianwen Tang, Chuanjin Luo, Chuanjin Luo

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Diagnostic accuracy of artificial intelligence models in detecting congenital heart disease in the second-trimester fetus through prenatal cardiac screening: a systematic review an... by Lies Dina Liastuti, Lies Dina Liastuti, Yosilia Nursakina, Yosilia Nursakina

“…Nevertheless, prospective studies with bigger datasets and more inclusive populations are needed to compare AI algorithms to conventional methods.Systematic Review Registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?…”
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Development and validation of a predictive model for new HIV infection screening among persons 15 years and above in primary healthcare settings in Kenya: a study protocol by Simon Karanja, Amos Otieno Olwendo, Gideon Kikuvi

“…Introduction This study seeks to determine incidence, comorbidities and drivers for new HIV infections to develop, test and validate a risk prediction model for screening for new cases of HIV.Methods and analysis The study has two components: a cross-sectional study to develop the prediction model using the HIV dataset from the Kenya AIDS and STI Control Programme and a 15-month prospective study for the validation of the model. …”
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CSCA-YOLOv8: A lightweight network model for evaluating drought resistance in mung bean. by Dongshan Jiang, Jinyang Liu, Haomiao Zhang, Wenxiang Liang, Ziqiu Luo, Wenlong An, Shicong Li, Xin Chen, Xingxing Yuan, Shangbing Gao

“…We also verified the excellent performance and generalization performance of the model using the collected MDD dataset. The final experimental results show that compared with the YOLOv8s baseline model, the number of parameters of our proposed algorithm is reduced by 24%, the floating point number is reduced by 35%, and the accuracy is improved by 2.52%, which supports the deployment on embedded edge devices with limited computing power. …”
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Development of a Predictive Model for N-Dealkylation of Amine Contaminants Based on Machine Learning Methods by Shiyang Cheng, Qihang Zhang, Hao Min, Wenhui Jiang, Jueting Liu, Chunsheng Liu, Zehua Wang

“…Therefore, the classification model developed in this work can provide methodological support for the high-throughput screening of N-dealkylation of amine pollutants.…”
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Early Warning of Low-Frequency Oscillations in Power System Using Rough Set and Cloud Model by Miao Yu, Jinyang Han, Shuoshuo Tian, Jianqun Sun, Honghao Wu, Jiaxin Yan

“…Compared with the existing methods, we have pioneered a synergistic mechanism of discrete attribute screening and continuous probabilistic feature fusion by combining the dynamic attribute approximation algorithm of rough sets with the cloud model, which effectively solves the loss of information caused by the discretization of continuous data in the traditional methods. …”
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An Updated Systematic Review on Asthma Exacerbation Risk Prediction Models Between 2017 and 2023: Risk of Bias and Applicability by Liu A, Zhang Y, Yadav CP, Chen W

“…We then applied the Prediction Risk of Bias Assessment tool (PROBAST) to assess the risk of bias and applicability of the included models.Results: Of 415 studies screened, 10 met eligibility criteria, comprising 41 prediction models. …”
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