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Gene expression varies stochastically across genders, racial groups, and health statuses. Deciphering these patterns is crucial for identifying informative genes, classifying samples, and understanding diseases like cancer. This study analyzes 11,252 bulk RNA-seq samples to explore expression patterns of 19,156 genes, including 10,512 cancer tissue samples and 740 normal samples. Additionally, 4,884 single-cell RNA-seq samples are examined. Statistical analysis using 16 probability distributions shows that normal samples display a wider range of distributions compared to cancer samples. Cancer samples tend to favor asymmetric distributions such as generalized extreme value, logarithmic normal, and Gaussian mixture distributions. In contrast, certain genes in normal samples exhibit symmetric distributions. Remarkably, more than 95.5% of genes exhibit non-normal distributions, which challenges traditional assumptions. Furthermore, distributions differ significantly between bulk and single-cell RNA-seq data. Many cancer driver genes exhibit distinct distribution patterns across sample types, suggesting potential for gene selection and classification based on distribution characteristics. A novel skewness-based metric is proposed to quantify distribution variation across datasets, showing genes with significant skewness differences have biological relevance. Finally, an improved naïve Bayes method incorporating gene-specific distributions demonstrates superior performance in simulations over traditional methods. This work enhances understanding of gene expression and its application in omics-based gene selection and sample classification. [ABSTRACT FROM AUTHOR]

Acute kidney injury (AKI) is characterized by impaired renal function that can result in irreversible severe renal impairment or lifelong dependence on renal replacement therapy in some cases. Early intervention can significantly slow down the progression of AKI and reduce mortality. In recent years, electronic early warning systems for patients with AKI have been gaining attention as a potential clinical decision‐support option. This paper presents a review of the application of electronic early warning systems for AKI from four aspects: development process, types of output, influencing factors, and system evaluation. [ABSTRACT FROM AUTHOR]

Feature selection has been extensively applied to identify cancer genes using omics data. Although substantial studies have been conducted to search for cancer genes, the available rich knowledge on various cancers is seldom used as prior information in feature selection. This paper proposes a two-stage prior LASSO (TSPLASSO) method, which represents an early attempt in designing feature selection algorithms using prior information. The first stage performs gene selection via linear regression with LASSO. Candidate genes that are correlated with known cancer genes are retained for subsequent analysis. The second stage establishes a logistic regression model with LASSO to realize final cancer gene selection and sample classification. The key advantages of TSPLASSO include the successive consideration of prior cancer genes and binary sample types as response variables in stages one and two, respectively. In addition, the TSPLASSO performs sample classification and variable selection simultaneously. Compared with six state-of-the-art algorithms, numerical simulations in six real-world datasets show that TSPLASSO can improve the accuracy of variable selection by 5%-400% in the three bulk sequencing datasets and the scRNA-seq dataset; and the performance is robust against data noise and variations of prior cancer genes. The TSPLASSO provides an efficient, stable and practical algorithm for exploring biomedcial and health informatics from omics data.

Background: Ischemic reperfusion injury of kidney is major cause for renal failure, however the involved pathogenesis remains unclear creating an void for its effective treatment. Here we studied involvement of microRNA-424 in renal injury., Methods: For the study, p53 or HIF-1α mice were used, ischemic renal injury was induced using clamping of renal pedicles bilateraly. Proximal kidney tubular cells were used for in vitro studies. Hoechst 33342 analysis was done for apoptosis. Blood urea nitrogen (BUN) and serum creatinine was done for renal function, Hematoxylin-eosin tissue damage and Terminal transferase-dUTP nick-end labeling assay for apoptosis. RT-PCR was done for miRNA and ChIP assay to identify the binding of p53 to miR-424. TargetScan and miRanda data base were scanned to find targets of miR-424. Protein expression was done by western blot analysis., Results: We discovered that, miR-424 was over-expressed in ischemic renal injury mice and in hypoxia exposed renal cells. In cells, miR-424 suppressed the expression levels of death receptor 6 (DR6) and halted the apoptosis mediated by hypoxia. Blocking of miR-424 halted the inhibition of DR6 and caused apoptosis and activation of caspase. In mice, miR-424 mimic inhibited expression of DR6 and attenuated ischemic renal injury. We established that, up-regulation of miR-424 in ischemic reperfusion injury was p53 dependent, also inhibition of p53 caused repression of miR-424 levels in hypoxia induced cells in vitro . The p53 knockout mice showed attenuation in levels of miR-424 confirming role of p53 behind up-regulation of miR-424 in vivo ., Conclusion: The study confirmed p53/miR-424/DR6 as a protective cascade during ischemic-reperfusion injury., Competing Interests: None.