An efficient medical data encryption scheme using selective shuffling and inter-intra pixel diffusion IoT-enabled secure E-healthcare framework by Dhivya Ravichandran, W. Sylvia Lilly Jebarani, Hemalatha Mahalingam, Padmapriya Velupillai Meikandan, Padmapriya Pravinkumar, Rengarajan Amirtharajan

“…The CCS generates the chaotic key sequences to shuffle the medical image row-wise and column-wise. …”
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The SPN Network for Digital Audio Data Based on Elliptic Curve Over a Finite Field by Ijaz Khalid, Tariq Shah, Khalid Ali Almarhabi, Dawood Shah, Muhammad Asif, M. Usman Ashraf

“…For the diffusion property, the scheme generates pseudo-random number sequences used for block permutation and achieves the property of diffusion. …”
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HSTC: hybrid traffic scheduling mechanism in time-sensitive networking by Changchuan YIN, Yanjue LI, Hailong ZHU, Xinxin HE, Wenxuan HAN

“…The maximum difference between scheduling success rate of our scheme and the scheme which mapped according to DDL was 20%. ④Compared with the suboptimal random slot injection scheme, the slot sequencing scheme in HSTC can increase network scheduling success rate by up to 0.77, and the limit bandwidth utilization can reach 88%.⑤Overall performance comparison: we compared with two mechanisms which were proposed in two representative existing literatures under the same simulation parameter configuration. …”
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HMOX1 as a potential drug target for upper and lower airway diseases: insights from multi-omics analysis by Enhao Wang, Shazhou Li, Yang Li, Tao Zhou

“…Candidate genes were further screened using Gene Set Enrichment Analysis (GSEA) and Random Forest (RF) algorithms. Causal inference between candidate genes and upper and lower airway diseases (CRSwNP, allergic rhinitis (AR), and asthma (AS)) was conducted using bidirectional two-sample Mendelian randomization (TwoSampleMR) analysis. …”
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Predictive value of dendritic cell-related genes for prognosis and immunotherapy response in lung adenocarcinoma by Zihao Sun, Mengfei Hu, Xiaoning Huang, Minghan Song, Xiujing Chen, Jiaxin Bei, Yiguang Lin, Size Chen

“…Methods DC-related biological functions and genes were identified using single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing. DCs-related gene signature (DCRGS) was constructed using integrated machine learning algorithms. …”
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Novel genes involved in vascular dysfunction of the middle temporal gyrus in Alzheimer’s disease: transcriptomics combined with machine learning analysis by Meiling Wang, Aojie He, Yubing Kang, Zhaojun Wang, Yahui He, Kahleong Lim, Chengwu Zhang, Li Lu

“…Finally, combining bulk RNA sequencing data and two machine learning algorithms (least absolute shrinkage and selection operator and random forest), four characteristic Alzheimer’s disease feature genes were identified: somatostatin (SST), protein tyrosine phosphatase non-receptor type 3 (PTPN3), glutinase (GL3), and tropomyosin 3 (PTM3). …”
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Identification of serum tRNA-derived small RNAs biosignature for diagnosis of tuberculosis by Zikun Huang, Qing Luo, Cuifen Xiong, Haiyan Zhu, Chao Yu, Jianqing Xu, Yiping Peng, Junming Li, Aiping Le

“…By utilizing cross-validation with a random forest algorithm approach, the training cohort achieved a sensitivity of 100% and specificity of 100%. …”
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Characterization of NOD-like receptor-based molecular heterogeneity in glioma and its association with immune micro-environment and metabolism reprogramming by Chunlin Lu, Huihao Ma, Jie Wang, Fei Sun, Fei Sun, Mingyang Fei, Ying Li, Jing Liu, Bin Dong

“…The random forest algorithm identified the crucial role of TRIP6 gene in NLR pathway. …”
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Comparative analysis of the human microbiome from four different regions of China and machine learning-based geographical inference by Yinlei Lei, Min Li, Han Zhang, Yu Deng, Xinyu Dong, Pengyu Chen, Ye Li, Suhua Zhang, Chengtao Li, Shouyu Wang, Ruiyang Tao

“…Individuals from the four regions could be distinguished and predicted based on a model constructed using the random forest algorithm, with the predictive effect of palmar microbiota being better than that of oral and nasal cavities. …”
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The value of multiparametric MRI radiomics and machine learning in predicting preoperative Ki-67 expression level in breast cancer by Yan Lu, Long Jin, Ning Ding, Mengjuan Li, Shengnan Yin, Yiding Ji

“…Materials and methods A total of 120 patients with pathologically confirmed breast cancer were retrospectively enrolled and randomly divided into a training set (n = 84) and a validation set (n = 36). …”
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Integrative analysis of cuproptosis-related lncRNAs: Unveiling prognostic significance, immune microenvironment, and copper-induced mechanisms in prostate cancer by Haitao Zhong, Yiming Lai, Wenhao Ouyang, Yunfang Yu, Yongxin Wu, Xinxin He, Lexiang Zeng, Xueen Qiu, Peixian Chen, Lingfeng Li, Jie Zhou, Tianlong Luo, Hai Huang

“…Results: Data from 492 patients with PCa were randomized into two groups at a 1:1 ratio. Prognostic modeling was successfully established using MLA-GNN. …”
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