No abstract

Protein ubiquitylation is an important post-translational modification (PTM), which is considered to be one of the most important processes regulating cell function and various diseases. Therefore, accurate prediction of ubiquitylation proteins and their PTM sites is of great significance for the study of basic biological processes and the development of related drugs. Researchers have developed some large-scale computational methods to predict ubiquitylation sites, but there is still much room for improvement. Much of the research related to ubiquitylation is cross-species while the life pattern is diversified, and the prediction method always shows its specificity in practical application. This study just aims to the issue of plants, and has constructed computational methods for identifying ubiquitylation protein and ubiquitylation sites. To better reflect the protein sequence information and obtain better prediction, the KNN scoring matrix model based on functional domain GO annotation and word embedding model (CBOW and Skip-Gram) are used to extract the features, and the light gradient boosting machine (LGBM) is selected as the ubiquitylation proteins prediction engine. As results, accuracy (ACC), precision (precision), recall (recall), F1_score and AUC are respectively 85.12%, 80.96%, 72.80%, 0.7637 and 0.9193 in the 10-fold cross-validations on independent data set. In the ubiquitylation sites prediction model, Skip-Gram, CBOW and EAAC feature extraction codes were used to extract protein sequence fragment features, and the predicted results on training and independent test data have also achieved good performance. In a word, the comparison results demonstrate that our models have a decided advantage in predicting ubiquitylation proteins and sites, and it may provide useful insights for studying the mechanisms and modulation of ubiquitination pathways. The datasets and source codes used in this study are available at: https://github.com/gmywqk/Ub-PS-Fuse.

The early symptoms of lung adenocarcinoma patients are inapparent, and the clinical diagnosis of lung adenocarcinoma is primarily through X-ray examination and pathological section examination, whereas the discovery of biomarkers points out another direction for the diagnosis of lung adenocarcinoma with the development of bioinformatics technology. However, it is not accurate and trustworthy to diagnose lung adenocarcinoma due to omics data with high-dimension and low-sample size (HDLSS) features or biomarkers produced by utilizing only single omics data. To address the above problems, the feature selection methods of biological analysis are used to reduce the dimension of gene expression data (GSE19188) and DNA methylation data (GSE139032, GSE49996). In addition, the Cartesian product method is used to expand the sample set and integrate gene expression data and DNA methylation data. The classification is built by using a deep neural network and is evaluated on K-fold cross validation. Moreover, gene ontology analysis and literature retrieving are used to analyze the biological relevance of selected genes, TCGA database is used for survival analysis of these potential genes through Kaplan-Meier estimates to discover the detailed molecular mechanism of lung adenocarcinoma. Survival analysis shows that COL5A2 and SERPINB5 are significant for identifying lung adenocarcinoma and are considered biomarkers of lung adenocarcinoma.

<abstract><p>In this article, we study complete convergence and complete moment convergence for negatively dependent random variables under sub-linear expectations. The results obtained in sub-linear expectation spaces extend the corresponding ones in probability space.</p></abstract>

In this paper, we study the complete convergence and complete moment convergence of linear processes generated by negatively dependent random variables under sub-linear expectations. The obtained results complement the ones of Meng, Wang, and Wu (Commun. Stat., Theory Methods 52(9):2931–2945, 2023) in the case of negatively dependent random variables under sub-linear expectations.