Machine learning based identification of anoikis related gene classification patterns and immunoinfiltration characteristics in diabetic nephropathy by Jing Zhang, Lulu Cheng, Shan Jiang, Duosheng Zhu

“…In addition, seven key genes, including PDK4, S100A8, HTRA1, CHI3L1, WT1, CDKN1B, and EGF, were screened by machine learning algorithm. Most of these genes exhibited low expression in renal tissue of DN patients and positive correlation with neutrophils, and their expressions were verified in an external dataset cell model. …”
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GPR65 is a novel immune biomarker and regulates the immune microenvironment in lung adenocarcinoma by Hanxu Zhou, Zhi Chen, Shuang Gao, Chaoqun Lian, Junjie Hu, Jin Lu, Lei Zhang

“…We screened differential genes (DEGs) in the immune and stromal components, and then screened modular genes by the WGCNA algorithm, which were intersected with DEGs and incorporated into the LASSO-COX regression model. …”
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Effect of miR-200c on inducing autophagy and apoptosis of HT22 cells from mouse hippocampal neurons via regulating PRDM1 protein: a bioinformatics analysis by W. Wu, J. Fu, Q. Liu, Q. Wang, S. Gao, X. Deng, C. Shen

“…The Support Vector Machine (SVM) algorithm in the Weka software was used to process, model, and screen the available miRNA data. …”
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Spatial and temporal distribution patterns and factors influencing hepatitis B in China: a geo-epidemiological study by Kang Fang, Na Cheng, Chuang Nie, Wentao Song, Yunkang Zhao, Jie Pan, Qi Yin, Jiwei Zheng, Qinglin Chen, Tianxin Xiang

“…Spatial autocorrelation analysis and spatiotemporal scanning were used to analyze the spatiotemporal distribution characteristics. The random forest algorithm was used to screen the potential influencing factors. …”
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Advancements in the application of artificial intelligence in the field of colorectal cancer by Mengying Zhu, Mengying Zhu, Zhenzhu Zhai, Yue Wang, Fang Chen, Ruibin Liu, Ruibin Liu, Xiaoquan Yang, Guohua Zhao

“…In this context, artificial intelligence (AI) has shown immense potential in revolutionizing CRC management, serving as one of the most effective screening tools. AI, utilizing machine learning (ML) and deep learning (DL) algorithms, improves early detection, diagnosis, and treatment by processing large volumes of medical data, uncovering hidden patterns, and forecasting disease development. …”
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Prediction of mortality risk in patients with severe community-acquired pneumonia in the intensive care unit using machine learning by Jingjing Pan, Tao Guo, Haobo Kong, Wei Bu, Min Shao, Zhi Geng

“…Five machine learning algorithms were used to build predictive models. Models were evaluated through nested cross-validation to select the best one. …”
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Prevalence estimates of trafficking in persons using statistical definitions: a cross-sectional high-risk community survey in Cape Town, South Africa by Rumi Kato Price, Annah K Bender, Floriana H Milazzo, Edna G. Rich, Nicolette V. Roman, Sheldon X Zhang, Erica L Koegler

“…Secondary outcome measures included individual and summary measures from the two screeners.Results Our PRIF algorithm yielded a TIP lifetime prevalence rate of 17.0% and past 12-month rate of 2.9%. …”
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Validation of a deep learning computer aided system for CT based lung nodule detection, classification, and growth rate estimation in a routine clinical population. by John T Murchison, Gillian Ritchie, David Senyszak, Jeroen H Nijwening, Gerben van Veenendaal, Joris Wakkie, Edwin J R van Beek

“…<h4>Objective</h4>In this study, we evaluated a commercially available computer assisted diagnosis system (CAD). The deep learning algorithm of the CAD was trained with a lung cancer screening cohort and developed for detection, classification, quantification, and growth of actionable pulmonary nodules on chest CT scans. …”
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A beginner’s approach to deep learning applied to VS and MD techniques by Stijn D’Hondt, José Oramas, Hans De Winter

“…There are many ways in which DL can be applied to these molecular modelling techniques to achieve more accurate results in a more efficient manner or expedite the data analysis of the acquired results. …”
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Artificial intelligence-driven label-free detection of chronic myeloid leukemia cells using ghost cytometry by Kohjin Suzuki, Naoki Watanabe, Yutaka Tsukune, Tadaaki Inano, Shintaro Kinoshita, Sayuri Tomoda, Kohei Yamada, Yusuke Konishi, Takuya Kuwana, Takeshi Sugiyama, Kenji Fukada, Kazuhiro Yamada, Miki Ando, Tomoiku Takaku

“…The AI model accurately detected CML cells and a strong correlation between AI-detected CML cells and actual BCR::ABL1 IS mRNA levels was observed. …”
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Predictive Study on the Cutting Energy Efficiency of Dredgers Based on Specific Cutting Energy by Junlang Yuan, Ke Yang, Taiwei Yang, Haoran Xu, Ting Xiong, Shidong Fan

“…Based on the machine learning framework, a model framework for predicting the specific cutting energy according to the relevant parameters of the suction-lifting system is constructed. …”
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The Hajj legacy and Saudi Arabia’s exemplary response to COVID-19 by Ghadah Alsaleh, Bander Balkhi, Bander Balkhi, Ahmed Alahmari, Anas Khan

“…The Hajj legacy strengthened laboratory diagnostics and surge staffing, informed border screening algorithms, and guided large-event risk assessments. …”
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Assessing CO2 separation performances of IL/ZIF-8 composites using molecular features of ILs by Hasan Can Gulbalkan, Alper Uzun, Seda Keskin

“…In this study, we developed a comprehensive computational approach integrating Conductor-like Screening Model for Realistic Solvents (COSMO-RS) calculations, density functional theory (DFT) calculations, Grand Canonical Monte Carlo (GCMC) simulations, and machine learning (ML) algorithms to evaluate a wide variety of IL-incorporated ZIF-8 composites for CO2 separations. …”
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Randomization-Driven Hybrid Deep Learning for Diabetic Retinopathy Detection by A. M. Mutawa, G. R. Hemalakshmi, N. B. Prakash, M. Murugappan

“…We enhance the model&#x2019;s diagnostic capability through complex image preprocessing techniques, such as improved noise reduction and morphological approaches. …”
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Data augmentation of time-series data in human movement biomechanics: A scoping review. by Christina Halmich, Lucas Höschler, Christoph Schranz, Christian Borgelt

“…These challenges make it difficult to train models that perform reliably across individuals, tasks, and settings. …”
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Utilizing Multi-omics analysis to elucidate the role of mitochondrial gene defects in Gastric cancer progression. by Jie Chu, Hanying Song, Kemin Fu, Wei Xiao, Jiudong Jiang, Qixin Gan, Bo Deng

“…Additionally, both the ssGSEA algorithm and the CIBERSORT algorithm were utilized to evaluate changes and effects in immunological characteristics during gastric cancer pathogenesis.…”
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RAD-Seq-derived SSR markers: a new paradigm for genetic analysis and construction of genetically improved production populations in Pinus koraiensis by Pingyu Yan, Wanying Zhang, Junfei Hao, Xiaotian Miao, Jun Wu, Zixiong Xie, Zhixin Li, Lei Zhang, Hanguo Zhang

“…A production population of 20 individuals was constructed via the simulated annealing algorithm, which exhibited a more reasonable mating system (F=−0.028) and demonstrated superior cone production compared with that of the plus tree population, with an increase of 79.6%. …”
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Artificial intelligence technology in ophthalmology public health: current applications and future directions by ShuYuan Chen, Wen Bai, Wen Bai

“…Key issues include interoperability with electronic health records (EHR), data security and privacy, data quality and bias, algorithm transparency, and ethical and regulatory frameworks. …”
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Cathepsin G promotes arteriovenous fistula maturation by positively regulating the MMP2/MMP9 pathway by Lemei Hu, Changqing Zheng, Ying Kong, Zhiqing Luo, Fengzhang Huang, Zhigang Zhu, Quhuan Li, Ming Liang

“…We screened 84 differentially expressed genes (DEGs) and performed the functional enrichment analysis. …”
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Individual differences in response to alcohol exposure in zebrafish (Danio rerio). by Heloysa Araujo-Silva, Jaquelinne Pinheiro-da-Silva, Priscila F Silva, Ana C Luchiari

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