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. …”
Get full text

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. …”
Get full text

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. …”
Get full text

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. …”
Get full text

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. …”
Get full text

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. …”
Get full text

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. …”
Get full text

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. …”
Get full text

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.…”
Get full text

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.…”
Get full text

Early gestational diabetes mellitus risk predictor using neural network with NearMiss by Min Zhao, Xiaojie Su, Lihong Huang

Get full text

Sparse Temporal Data-Driven SSA-CNN-LSTM-Based Fault Prediction of Electromechanical Equipment in Rail Transit Stations by Jing Xiong, Youchao Sun, Junzhou Sun, Yongbing Wan, Gang Yu

“…The experiments showed that the proposed prediction method improved the RMSE by 0.000699, the MAE by 0.00042, and the R2 index by 0.109779 when predicting the fault rate data of platform screen doors on all of the lines. When predicting the fault rate data of the screen doors on a single line, the performance of the model was better than that of the CNN-LSTM model optimized with the PSO algorithm.…”
Get full text

Identification of ALDH2 as a novel target for the treatment of acute kidney injury in kidney transplantation based on WGCNA and machine learning algorithms and exploration of its p... by Jinpu Peng, Shili Wang, Xingyu Pan, Moudong Wu, Xiong Zhan, Dan Wang, Guohua Zhu, Wei Wang, Hongyu Tang, Nini An, Jun Pei

“…Next, we constructed a rat renal IRI model and explored the role of key genes in renal IRI. …”
Get full text

Examining the empathy levels of medical students using CHAID analysis by Nesrin Hark Söylemez

“…Methods The study was conducted with 322 medical students from a public university in Turkey. A relational screening model was applied, using a “Personal Information Form” and an “Empathy Scale” to gather data. …”
Get full text

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.…”
Get full text

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. …”
Get full text

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. …”
Get full text

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. …”
Get full text

Modeling of biodiesel production using optimization designs from literature: aiming to reduce the laboratory workload by Iver Bergh Hvidsten, Kristian Hovde Liland, Oliver Tomic, Jorge Mario Marchetti

“…GBR, with 1000 estimators and a tree depth of 5, achieved the best performance (R2 = 0.744, RMSE = 10.783). The global GBR model was comprehensively evaluated for accuracy and physical relevance, with proposed applications in component screening and reaction optimization using the DIRECT-l (DIviding RECTangles - locally biased version) algorithm. …”
Get full text

A multi-gene predictive model for the radiation sensitivity of nasopharyngeal carcinoma based on machine learning by Kailai Li, Junyi Liang, Nan Li, Jianbo Fang, Xinyi Zhou, Jian Zhang, Anqi Lin, Peng Luo, Hui Meng

“…By evaluating 113 machine learning algorithm combinations, the glmBoost+NaiveBayes model was selected to construct the NPC-RSS based on 18 key genes, which demonstrated good predictive performance in both public and in-house datasets. …”
Get full text