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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Artificial Intelligence in the Non-Invasive Detection of Melanoma by Banu İsmail Mendi, Kivanc Kose, Lauren Fleshner, Richard Adam, Bijan Safai, Banu Farabi, Mehmet Fatih Atak

“…The use of artificial intelligence (AI)-based technologies in dermatology has emerged in recent years to assist in the diagnosis of melanoma that may be more accessible to all patients and more accurate than current methods of screening. …”
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Machine learning-based risk prediction model for pertussis in children: a multicenter retrospective study by Juan Xie, Run-wei Ma, Yu-jing Feng, Yuan Qiao, Hong-yan Zhu, Xing-ping Tao, Wen-juan Chen, Cong-yun Liu, Tan Li, Kai Liu, Li-ming Cheng

“…The model was constructed using machine learning techniques based on multicenter data and screened for key features. …”
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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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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. …”
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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. …”
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Exploring shared pathogenic mechanisms and biomarkers in hepatic fibrosis and inflammatory bowel disease through bioinformatics and machine learning by Shangkun Li, Haoyu Li, Mingran Qi

“…The key diagnostic biomarkers were determined via a protein-protein interaction (PPI) network combined with two machine learning algorithms. The logistic regression model was subsequently developed based on these key genes. …”
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NUTRI-ONCOCARE: New integral nutrition care model to prevent and treat malnutrition in cancer patients by Mariola Sirvent-Ochando, Ana Murcia-Lopez, Cristina Sangrador-Pelluz, Sara Espla, Margarita Garrido-Siles, Jimena Abiles

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Comparison between Logistic Regression and K-Nearest Neighbour Techniques with Application on Thalassemia Patients in Mosul by Mohammed Al jbory, Hutheyfa Taha

“…The data was divided into 70% for training and 30% for screening. The experimental results showed that the logistic regression model performed better than the nearest neighbor algorithm with a precision of 96%, recall of 98%, and F1- score of 97% in the thalassemia intermedia category, while it had a precision of 97%, recall of 95%, and F1- score of 96% in the thalassemia major category, indicating that logistic regression performed well in distinguishing between these two categories. it has been shown that logistic regression is more effective than the K-nearest neighbor algorithm in classifying thalassemia patients, especially those with thalassemia major. …”
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TAL-SRX: an intelligent typing evaluation method for KASP primers based on multi-model fusion by Xiaojing Chen, Xiaojing Chen, Jingchao Fan, Jingchao Fan, Shen Yan, Longyu Huang, Longyu Huang, Longyu Huang, Guomin Zhou, Guomin Zhou, Jianhua Zhang, Jianhua Zhang

“…To address the above problems, we proposed a typing evaluation method for KASP primers by integrating deep learning and traditional machine learning algorithms, called TAL-SRX. First, three algorithms are used to optimize the performance of each model in the Stacking framework respectively, and five-fold cross-validation is used to enhance stability. …”
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An mRNA Vaccine for Herpes Zoster and Its Efficacy Evaluation in Naïve/Primed Murine Models by Linglei Jiang, Wenshuo Zhou, Fei Liu, Wenhui Li, Yan Xu, Zhenwei Liang, Man Cao, Li Hou, Pengxuan Liu, Feifei Wu, Aijun Shen, Zhiyuan Zhang, Xiaodi Zhang, Haibo Zhao, Xinping Pan, Tengjie Wu, William Jia, Yuntao Zhang

“…<b>Methods:</b> Various mRNA constructs were designed based on intracellular organelle-targeting strategies and AI algorithm-guided high-throughput automation platform screening and were then synthesized by in vitro transcription and encapsulated with four-component lipid nanoparticles (LNPs). …”
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Fuzzy-Based Fusion Model for β-Thalassemia Carriers Prediction Using Machine Learning Technique by Muhammad Ibrahim, Sagheer Abbas, Areej Fatima, Taher M. Ghazal, Muhammad Saleem, Meshal Alharbi, Fahad Mazaed Alotaibi, Muhammad Adnan Khan, Muhammad Waqas, Nouh Elmitwally

“…An efficient method of beta thalassemia is prenatal screening after couples have received counselling. …”
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An XGBoost-SHAP Model for Energy Demand Prediction With Boruta&#x2013;Lasso Feature Selection by Yiwen Wang, Weibin Cheng, Yuting Jin, Jifei Li, Yantian Yang, Shaobing Hu

“…This study proposes an interpretable ML framework for energy demand prediction based on the Boruta-Lasso two-stage feature selection model, extreme gradient boosting (XGBoost) regression model, grid search optimization algorithm, and Shapley additive explanations (SHAP) algorithm. …”
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Molecular biomarkers in salivary diagnostic materials: Point-of-Care solutions — PoC-Diagnostics and -Testing by Ziyad S. Haidar

“…Recent advancements in nanomaterials and fabrication techniques, coupled with emerging computational approaches such as artificial intelligence (AI), machine learning, and deep learning, have revolutionized high-throughput screening and laboratory automation. AI-driven algorithms now process and analyze salivary proteomic data with remarkable accuracy, identifying patterns and biomarkers associated with diseases such as oral cancer at an early stage. …”
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Advancing Precision Medicine for Hypertensive Nephropathy: A Novel Prognostic Model Incorporating Pathological Indicators by Yunlong Qin, Jin Zhao, Yan Xing, Zixian Yu, Panpan Liu, Yuwei Wang, Anjing Wang, Yueqing Hui, Wei Zhao, Mei Han, Meng Liu, Xiaoxuan Ning, Shiren Sun

“…RSF and Cox regression were used to establish a renal prognosis prediction model based on the factors screened by the RSF algorithm. …”
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A Novel Strategy Coupling Optimised Sampling with Heterogeneous Ensemble Machine-Learning to Predict Landslide Susceptibility by Yongxing Lu, Honggen Xu, Can Wang, Guanxi Yan, Zhitao Huo, Zuwu Peng, Bo Liu, Chong Xu

“…The stacking ensemble machine-learning model outperformed those three baseline models. Notably, the accuracy of the hybrid OS–Stacking model is most promising, up to 97.1%. …”
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Development of an Efficient and Generalized MTSCAM Model to Predict Liquid Chromatography Retention Times of Organic Compounds by Mengdie Fan, Chenhui Sang, Hua Li, Yue Wei, Bin Zhang, Yang Xing, Jing Zhang, Jie Yin, Wei An, Bing Shao

“…The results demonstrate that this model achieves an R2 of 0.98 and an average prediction error of 23 s, outperforming currently published models. …”
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The modeling of two-dimensional vortex flows in a cylindrical channel using parallel calculations on a supercomputer by I. G. Lebo, I. V. Obruchev

“…The methods of mathematical modeling were used. A parallel algorithm for solving two-dimensional equations of gas dynamics in cylindrical coordinates (r, z, t) was developed and a new version of the NUTCY_ps program created. …”
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Risk Prediction of Liver Injury in Pediatric Tuberculosis Treatment: Development of an Automated Machine Learning Model by Zeng Y, Lu H, Li S, Shi QZ, Liu L, Gong YQ, Yan P

“…After the features were screened by univariate risk factor analysis, AutoML technology was used to establish predictive models. …”
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Development of a deep learning model for automated detection of calcium pyrophosphate deposition in hand radiographs by Thomas Hügle, Elisabeth Rosoux, Guillaume Fahrni, Deborah Markham, Tobias Manigold, Fabio Becce

“…The algorithm could be used to screen larger OA or RA databases or electronic medical records for CPPD cases. …”
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