The research on enhancing LA estimation accuracy across domains for small sample data based on data augmentation and data transfer integration optimization system by Ai-Dong Wang, Rui-Jie Li, Xiang-Qian Feng, Zi-Qiu Li, Wei-Yuan Hong, Hua-Xing Wu, Dan-Ying Wang, Song Chen

“…A comprehensive comparison of six algorithms (linear regression, support vector regression, random forest, XGBoost, CatBoost, and K-nearest neighbors) is conducted, assessing their performance under a combined strategy of data augmentation (noise injection, generative adversarial networks, Gaussian mixture model, variational autoencoders) and transfer learning (random, clustering, and hierarchical parameter transfer). …”
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Machine learning-based in-silico analysis identifies signatures of lysyl oxidases for prognostic and therapeutic response prediction in cancer by Qingyu Xu, Ling Ma, Alexander Streuer, Eva Altrock, Nanni Schmitt, Felicitas Rapp, Alessa Klär, Verena Nowak, Julia Obländer, Nadine Weimer, Iris Palme, Melda Göl, Hong-hu Zhu, Wolf-Karsten Hofmann, Daniel Nowak, Vladimir Riabov

“…We performed comprehensive machine learning-based bioinformatics analyses, including unsupervised consensus clustering, a total of 10 machine-learning algorithms for prognostic prediction and the Connectivity map tool for drug sensitivity prediction. …”
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Research on Quantitative Evaluation of Defects in Ferromagnetic Materials Based on Electromagnetic Non-Destructive Testing by Xiangyi Hu, Ruijie Xie, Ruotian Wang, Jiapeng Wang, Haichao Cai, Xiaoqiang Wang, Xiang Li, Qingzhu Guan, Jianhua Zhang

“…., inaccurate quantification, over-reliance on algorithms, and non-intuitive result presentation, among others) in quantitative defect evaluation. …”
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Comprehensive Transcriptome Sequencing and Analysis of <i>Euspira gilva</i>: Insights into Aquaculture and Conservation by Zhixing Su, Jiayuan Xu, Xiaokang Lv, Xuefeng Song, Yanming Sui, Benjian Wang, Xiaoshan Wang, Bianbian Zhang, Baojun Tang, Liguo Yang

“…Further, 530 long non-coding RNAs (lncRNAs) were identified through the application of the CPC2, CNCI, Pfam, and PLEK algorithms. The highest overall sequence similarity in the NR database was observed with <i>Pomacea canaliculata</i>, a freshwater species, but with a similarity of only 36.6%, indicating a unique genetic makeup of <i>E. gilva</i>. …”
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The Clinical Significance of Abnormal Electroencephalography (EEG) Patterns in Patients with Neuropsychiatric Disorders Due to Anti-NMDA Receptor Encephalitis: A Comparative Study by Alvaro Moreno-Avellán, Arely Juarez-Jaramillo, Maria del Carmen Fernandez Gonzalez-Aragon, Gerardo Quiñones-Pesqueira, Luz Maria Pineda-Centeno, Mariana Espinola-Nadurille, Victoria Martinez-Angeles, Francisco Martinez-Carrillo, Veronica Rivas-Alonso, Daniel San-Juan, Jose Flores-Rivera, Jesus Ramirez-Bermudez

“…These findings support the integration of EEG into diagnostic algorithms to guide early detection and management of autoimmune encephalitis.…”
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Identification of lactylation-related biomarkers in osteoporosis from transcriptome and single-cell data by Jiafeng Peng, Hongxing Zhang, Huaize Wang, Ting Jiang, Minglei Gao, Xingfu Ma, Yingzong Xiong, Yingchun Li, Ran Xu, Junchen Zhu

“…First, the biomarkers associated with OP were identified through differential gene expression analysis, machine learning algorithms, expression validation, and receiver operating characteristic (ROC) curve analysis. …”
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Integrating deep learning in public health: a novel approach to PICC-RVT risk assessment by Yue Li, Yue Li, Shengxiao Nie, Lei Wang, Dongsheng Li, Shengmiao Ma, Ting Li, Hong Sun

“…Existing models often assess PICC-RVT risk as static and discrete outcomes, which may limit their practical application.ObjectivesThis study aims to evaluate the effectiveness of seven diverse machine learning algorithms, including three deep learning and four traditional machine learning models, that incorporate time-series data to assess PICC-RVT risk. …”
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DEPDC1B, CDCA2, APOBEC3B, and TYMS are potential hub genes and therapeutic targets for diagnosing dialysis patients with heart failure by Wenwu Tang, Wenwu Tang, Zhixin Wang, Xinzhu Yuan, Liping Chen, Haiyang Guo, Zhirui Qi, Ying Zhang, Xisheng Xie

“…In addition, we further explored potential mechanism and function of hub genes in HF of patients with MHD through GSEA, immune cell infiltration analysis, drug analysis and establishment of molecular regulatory network.ResultsTotally 23 candidate genes were screened out by overlapping 673 differentially expressed genes (DEGs) and 147 key module genes, of which four hub genes (DEPDC1B, CDCA2, APOBEC3B and TYMS) were obtained by two machine learning algorithms. Through GSEA analysis, it was found that the four genes were closely related to ribosome, cell cycle, ubiquitin-mediated proteolysis. …”
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Using Machine Learning to Predict Outcomes Following Thoracic and Complex Endovascular Aortic Aneurysm Repair by Ben Li, Naomi Eisenberg, Derek Beaton, Douglas S. Lee, Badr Aljabri, Leen Al‐Omran, Duminda N. Wijeysundera, Ori D. Rotstein, Thomas F. Lindsay, Charles de Mestral, Muhammad Mamdani, Graham Roche‐Nagle, Mohammed Al‐Omran

“…We developed machine learning algorithms to predict outcomes following TEVAR and complex EVAR. …”
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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

“…Prognostic modeling of PC was developed using 14 machine learning algorithms, with performance validated through long-term survival metrics, functional enrichment, immune infiltration analysis, and drug sensitivity profiling. …”
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Enhanced Metabolic Control in a Pediatric Population with Type 1 Diabetes Mellitus Using Hybrid Closed-Loop and Predictive Low-Glucose Suspend Insulin Pump Treatments by Irina Bojoga, Sorin Ioacara, Elisabeta Malinici, Victor Chiper, Olivia Georgescu, Anca Elena Sirbu, Simona Fica

“…Background: Insulin pumps coupled with continuous glucose monitoring sensors use algorithms to analyze real-time blood glucose levels. …”
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Epidemiological, molecular, and evolutionary characteristics of G1P[8] rotavirus in China on the eve of RotaTeq application by Rui Peng, Rui Peng, Mengxuan Wang, Saleha Shahar, Guangping Xiong, Qing Zhang, Lili Pang, Hong Wang, Xiangyu Kong, Dandi Li, Zhaojun Duan

“…Neutralizing epitope, amino acid selection pressure, and evolution dynamics analyses on VP7 and VP4 were performed using BioEdit v.7.0.9.0 and PyMOL v.2.5.2, four algorithms (MEME, SLAC, FEL, and FUBAR) in the Datamonkey online software, and the MCMC model in BEAST v. 1.10.4, respectively. …”
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Neurofibromatosis-Noonan syndrome: a prospective monocentric study of 26 patients and literature review by Didier Bessis, Dominique Vidaud, Pierre Meyer, Laurence Pacot, de La Villeon G, Adeline Alice Bonnard, Yline Capri, Christine Coubes, Fanchon Herman, Didier Lacombe, Nicolas Molinari, Laura Poujade, Agathe Roubertie, Julien Van Gils, Alain Verloes, David Geneviève, Hélène Cavé, Marjolaine Willems

“…Secondary objectives include evaluating inter-rater diagnostic agreement among experienced clinicians and assessing the utility of deep-learning algorithms (Face2Gene® [F2G]). Additionally, we assess the prevalence of congenital heart malformations (CHM) in NF-NS compared to ‘classic’ neurofibromatosis type 1 (NF1). …”
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Diagnosing prostate cancer in the PSA gray zone through machine learning and transrectal ultrasound video by Qin Wu, Chengyi Wu, Maoliang Zhang, Jie Yang, Junxiang Zhang, Yun Jin, Yanhong Du, Xingbo Sun, Liyuan Jin1, Kai Wang, Zhengbiao Hu, Xiaoyang Qi1, Jincao Yao, Zhengping Wang, Dong Xu

“…The selected features were employed to construct radiomics models based on four machine learning algorithms support vector machine (SVM), random forest (RF), adaptive boosting (ADB) and gradient boosting machine (GBM). …”
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AI Efficiency in Dentistry: Comparing Artificial Intelligence Systems with Human Practitioners in Assessing Several Periodontal Parameters by Oana-Maria Butnaru, Monica Tatarciuc, Ionut Luchian, Teona Tudorici, Carina Balcos, Dana Gabriela Budala, Ana Sirghe, Dragos Ioan Virvescu, Danisia Haba

“…However, further validation in clinical settings is necessary to address limitations such as algorithmic bias and atypical cases. AI integration in dentistry can enhance diagnostic precision and patient outcomes while reducing variability in clinical assessments.…”
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Heat transfer with magnetic force and slip velocity on non-Newtonian fluid flow through a porous medium by Muhammad Ramzan, Muhammad Shahryar, Shajar Abbas, Muhammad Amir, Shaxnoza Ravshanbekovna Saydaxmetova, Rashid Jan, Afnan Al Agha, Hakim AL Garalleh

“…Model validation is achieved by comparing results obtained through algorithmic solutions, confirming the robustness of the fractional approach by taking the values of fractional parameter γ in the range 0.2≤γ≤1, whereas the values of slip parameter λ lies in the range 0≤λ≤0.8. …”
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IKZF1 as a potential therapeutic target for dendritic cell-mediated immunotherapy in IgA nephropathy by Fei Peng, Chunjia Sheng, Jiayi He, Yena Zhou, Yilun Qu, Shuwei Duan, Yinghua Zhao, Jikai Xia, Jie Wu, Guangyan Cai, Lingling Wu, Chuyue Zhang, Xiangmei Chen

“…Receiver operating characteristic (ROC) curve analysis and machine learning algorithms were employed to screen for DC-related diagnostic biomarkers from the dataset. …”
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Predicting the risk of postoperative avascular necrosis in patients with talar fractures based on an interpretable machine learning model by Jian Zhang, Jian Zhang, Jian Zhang, Jihai Xu, Jihai Xu, Jiapei Yu, Jiapei Yu, Jiapei Yu, Hong Chen, Hong Chen, Xin Hong, Songou Zhang, Xin Wang, Xin Wang, Chengchun Shen, Chengchun Shen, Chengchun Shen

“…Six machine learning algorithms were employed to construct the prediction models. …”
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Soybean Yield Estimation Using Improved Deep Learning Models With Integrated Multisource and Multitemporal Remote Sensing Data by Jian Li, Junrui Kang, Ji Qi, Jian Lu, Hongkun Fu, Baoqi Liu, Xinglei Lin, Jiawei Zhao, Hengxu Guan, Jing Chang, Zhihan Liu

“…This TransBiHGRU-PSO algorithmic framework, combined with multisource and multitemporal remote sensing data, offers a valuable exploration for large-scale soybean yield estimation.…”
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A machine learning model for predicting lymph node positivity in ovarian cancer: development, validation, and clinical application by QingYong Guo, Jinji Wang, Ru Chen, LiPing Hu, Wenqiang You

“…We developed a machine learning model incorporating multiple algorithms, with XGBoost demonstrating superior performance. …”
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