Advanced Classifiers and Feature Reduction for Accurate Insomnia Detection Using Multimodal Dataset by Ameya Chatur, Mostafa Haghi, Nagarajan Ganapathy, Nima TaheriNejad, Ralf Seepold, Natividad Martinez Madrid

“…Our findings emphasize the importance of tailoring feature sets and employing appropriate reduction techniques for optimal predictive modeling in sleep-related studies. Our results demonstrate that the ensemble classifiers generalize well on the dataset regardless of the feature count, while other algorithms are hindered by the curse of dimensionality.…”
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Accurate and rapid single nucleotide variation detection in PCSK9 gene using nanopore sequencing by Ilaria Massaiu, Vincenza Valerio, Valentina Rusconi, Valentina Rusconi, Francesca Bertolini, Donato De Giorgi, Veronika A. Myasoedova, Paolo Poggio, Paolo Poggio

“…Twelve subjects were analyzed using different sequencing flow cells, basecalling models, and SNV calling algorithms. Sanger sequencing served as the reference for performance validation. …”
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The Application and Ethical Implication of Generative AI in Mental Health: Systematic Review by Xi Wang, Yujia Zhou, Guangyu Zhou

“…Studies on diagnosis and assessment (37/79, 47%) primarily used GenAI models to detect depression and suicidality through text data. …”
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Biomarker-driven drug repurposing for NAFLD-associated hepatocellular carcinoma using machine learning integrated ensemble feature selection by Subhajit Ghosh, Sukhen Das Mandal, Subarna Thakur

“…Seven machine learning algorithms were assessed for disease stage classification. …”
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Use of ICT to Confront COVID-19 by Yousry Saber El Gamal

“…ML models can be used to compare the viral genome with known genomes and identify existing similarities. …”
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Shared pathogenic mechanisms linking obesity and idiopathic pulmonary fibrosis revealed by bioinformatics and in vivo validation by Linjie Chen, Haojie Chen, Zinan Chen, Kunyi Zhang, Hongsen Zhang, Jiahe Xu, Tongsheng Chen

“…Functional enrichment (GO/KEGG), protein-protein interaction (PPI) networks, and machine learning algorithms were applied to screen hub genes, validated by ROC curves. …”
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Is body mass index associated with outcomes of mechanically ventilated adult patients in intensive critical units? A systematic review and meta-analysis. by Yonghua Zhao, Zhiqiang Li, Tao Yang, Meiping Wang, Xiuming Xi

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Prediction and validation of anoikis-related genes in neuropathic pain using machine learning. by Yufeng He, Ye Wei, Yongxin Wang, Chunyan Ling, Xiang Qi, Siyu Geng, Yingtong Meng, Hao Deng, Qisong Zhang, Xiaoling Qin, Guanghui Chen

“…We also used rats to construct an NP model and validated the analyzed hub genes using hematoxylin and eosin (H&E) staining, real-time polymerase chain reaction (PCR), and Western blotting assays.…”
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Prognostic, oncogenic roles, and pharmacogenomic features of AMD1 in hepatocellular carcinoma by Youliang Zhou, Yi Zhou, Jiabin Hu, Yao Xiao, Yan Zhou, Liping Yu

“…Univariate Cox regression analysis and Pearson correlation were used to screen for AMD1-related genes (ARGs). Multidimensional bioinformatic algorithms were utilized to establish a risk score model for ARGs. …”
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Identification of glucocorticoid-related genes in systemic lupus erythematosus using bioinformatics analysis and machine learning. by Yinghao Ren, Weiqiang Chen, Yuhao Lin, Zeyu Wang, Weiliang Wang

“…Furthermore, we utilized least absolute shrinkage and selection operator (LASSO) regression and Random Forest (RF) algorithms to screen for hub genes. We then validated the expression of these hub genes and constructed nomograms for further validation. …”
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Color and Grey-Level Co-Occurrence Matrix Analysis for Predicting Sensory and Biochemical Traits in Sweet Potato and Potato by Judith Ssali Nantongo, Edwin Serunkuma, Gabriela Burgos, Mariam Nakitto, Joseph Kitalikyawe, Thiago Mendes, Fabrice Davrieux, Reuben Ssali

“…With instrumental color and texture parameters as predictors, low to moderate accuracy was detected in the machine learning models developed to predict sensory panel traits. …”
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Uncovering Hippo pathway-related biomarkers in acute myocardial infarction via scRNA-seq binding transcriptomics by Xingda Li, Xueqi He, Yu Zhang, Xinyuan Hao, Anqi Xiong, Jiayu Huang, Biying Jiang, Zaiyu Tong, Haiyan Huang, Lian Yi, Wenjia Chen

“…Three machine-learning algorithms prioritized five biomarkers (NAMPT, CXCL1, CREM, GIMAP6, and GIMAP7), validated through multi-dataset analyses and cellular expression profiling. qRT-PCR and Western blot confirmed differential expression patterns between AMI and controls across experimental models. …”
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Purine metabolism-associated key genes depict the immune landscape in gout patients by Lin-na Li, Hao Wang, Lu-shan Xiao, Wei-nan Lai

“…Using RNA-seq data of peripheral blood mononuclear cells (PBMCs) from gout patients, we screened the differentially expressed genes (DEGs) of gout patients and found that they were closely involved in purine metabolism. …”
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Identification of aging-related biomarkers and immune infiltration analysis in renal stones by integrated bioinformatics analysis by Yuanzhao Wang, Nana Chen, Bangqiu Zhang, Pingping Zhuang, Bingtao Tan, Changlong Cai, Niancai He, Hao Nie, Songtao Xiang, Chiwei Chen

“…Using logistic regression, SVM, and LASSO regression algorithms, a successful early-diagnosis model for RS was developed, yielding 7 key genes: CNR1, KIT, HTR2A, DES, IL33, UCP2, and PPT1. …”
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Identification of potential metabolic biomarkers and immune cell infiltration for metabolic associated steatohepatitis by bioinformatics analysis and machine learning by Haoran Xie, Junjun Wang, Qiuyan Zhao

“…Protein-Protein Interaction (PPI) network and machine learning algorithms, including Least Absolute Shrinkage and Selection Operator (LASSO) regression, Support Vector Machine-Recursive Feature Elimination (SVM-RFE), and Random Forest (RF), were applied to screen for signature MRDEGs. …”
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Novel Approaches for the Early Detection of Glaucoma Using Artificial Intelligence by Marco Zeppieri, Lorenzo Gardini, Carola Culiersi, Luigi Fontana, Mutali Musa, Fabiana D’Esposito, Pier Luigi Surico, Caterina Gagliano, Francesco Saverio Sorrentino

“…By automating standard screening procedures, these models have demonstrated promise in distinguishing between glaucomatous and healthy eyes, forecasting the course of the disease, and possibly lessening the workload of physicians. …”
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Advancement of artificial intelligence based treatment strategy in type 2 diabetes: A critical update by Aniruddha Sen, Palani Selvam Mohanraj, Vijaya Laxmi, Sumel Ashique, Rajalakshimi Vasudevan, Afaf Aldahish, Anupriya Velu, Arani Das, Iman Ehsan, Anas Islam, Sabina Yasmin, Mohammad Yousuf Ansari

“…At the same time, the rapidly increasing role of AI in diabetes care is woven into the story, mainly targeting how insulin therapy can be modified and personalized through algorithms and predictive modelling. It leaves a deep review of their pre-existing synergies, which helps understand how collaborative opportunities will unlock the future of T2DM care. …”
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Ferroptosis-related hub genes and immune cell dynamics as diagnostic biomarkers in age-related macular degeneration by Jinquan Chen, Zhao Long, Dandan Shi, Qian Zhang, H. Peng

“…Consequently, the macular was selected as the primary focus of the study. Subsequent screening of these 19 genes using LASSO regression, Support Vector Machine (SVM), and Random Forest algorithms identified four hub genes: FADS1, TFAP2A, AKR1C3, and TTPA. …”
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Transforming heart transplantation care with multi-omics insights by Zhengbang Zou, Jianing Han, Zhiyuan Zhu, Shanshan Zheng, Xinhe Xu, Sheng Liu

“…Single–cell omics technologies and machine learning algorithms further resolve cellular heterogeneity and improve predictive modeling, thereby enhancing the clinical translatability of multi-omics data. …”
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Problems and perspectives of family doctors training on the undergraduate stage by Yu. M. Kolesnik, V. D. Syvolap, N. S. Mikhaylovskaya, T.O. Kulinich

“…For working on practical part of family doctors basic skills it is planned to organize educational and training center at the family ambulatory, and its equipment with the necessary visual means, phantoms, models, simulators, diagnostic, medical apparatus and instruments. …”
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