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Premature ovarian insufficiency (POI) is a reproductive endocrine disorder characterized by infertility and perimenopausal syndrome, with a highly heterogeneous genetic etiology and its mechanism is not fully understood. Therefore, we utilized Oxford Nanopore Technology (ONT) for the first time to characterize the full-length transcript profile, and revealed biomarkers, pathway and molecular mechanisms for POI by bioinformatics analysis and machine learning. Ultimately, we identified 272 differentially expressed genes, 858 core genes, and 25 hub genes by analysis of differential expression, gene set enrichment, and protein–protein interactions. Seven candidate genes were identified based on the intersection features of the random forest and Boruta algorithm. qRT-PCR results indicated that COX5A, UQCRFS1, LCK, RPS2 and EIF5A exhibited consistent expression trends with sequencing data and have potential as biomarkers. Additionally, GSEA analysis revealed that the pathophysiology of POI is closely associated with inhibition of the PI3K-AKT pathway, oxidative phosphorylation and DNA damage repair, as well as activation of inflammatory and apoptotic pathways. Furthermore, we emphasize that downregulation of respiratory chain enzyme complex subunits and inhibition of oxidative phosphorylation pathways play crucial roles in the pathophysiology of POI. In conclusion, our utilization of long-read sequencing has refined the annotation information within the POI transcriptional profile. This valuable data provides novel insights for further exploration into molecular regulatory networks and potential biomarkers associated with POI. [ABSTRACT FROM AUTHOR]

This correction notice addresses an error in the original article titled "Exploring biomarkers of premature ovarian insufficiency based on oxford nanopore transcriptional profile and machine learning." The error involved the order of the author names, which has been corrected to Zhaoyang Yu, Mujun Li, and Weilong Peng. Additionally, an incorrect email address for author Mujun Li was provided, and the correct email address is lmj0081@163.com. The original article has been updated to reflect these corrections. [Extracted from the article]

Anomalies often experience distribution drift over time in anomaly detection. Traditional anomaly detection methods detect anomalies with the same distribution effectively, but it is difficult to detect anomalies with changing distributions. In this paper, we propose a m eta d omain g eneralization based a nomaly d etection (MDGAD) framework to detect anomalies when distribution drifts. The framework first divides the data into a series of subsets. We measure the domain shift between sets with a class-sensitive distance metric, and merge similar sets, thereby generating different source domains to enhance domain diversity. We utilize the distribution shifts that existed on these source domains to simulate the distribution shifts that would be encountered when the model was applied. Meta learning is employed during training. First, the network is updated once on the meta-training set, and then another update is performed on the meta-verification set. These two gradients together determine the update direction of the network. At the same time, our framework will complete the domain alignment task on multiple source domains to extract domain invariant features and enhance the generalization of feature learning. We use Devnet as the base model and test our framework on 5 simulated data sets. The results confirm that our MDGAD outperforms current popular algorithms in detecting unknown anomalies and has better robustness. In addition, we also test our framework on real financial datasets to demonstrate the effectiveness. [ABSTRACT FROM AUTHOR]