m6AGE: A Predictor for N6-Methyladenosine Sites Identification Utilizing Sequence Characteristics and Graph Embedding-Based Geometrical Information

Wang, Yan; Guo, Rui; Huang, Lan; Yang, Sen; Hu, Xuemei; He, Kai

doi:10.3389/fgene.2021.670852

Cited by 11 publications

(7 citation statements)

References 43 publications

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“…CatBoost is an improved implementation of gradient enhanced decision trees (GDBT) algorithm developed by Yandex. It has demonstrated excellent performance on many classification and regression tasks ( Kang et al, 2021 ; Liu S. et al, 2021 ; Wang Y. et al, 2021 ). CatBoost was performed via the Python package to build a new optimized classification model (CatBoost model).…”

Section: Methodsmentioning

confidence: 99%

Identification and Validation of the Pyroptosis-Related Molecular Subtypes of Lung Adenocarcinoma by Bioinformatics and Machine Learning

Liu

Zhao

et al. 2021

Front. Cell Dev. Biol.

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Lung cancer remains the leading cause of cancer death globally, with lung adenocarcinoma (LUAD) being its most prevalent subtype. Due to the heterogeneity of LUAD, patients given the same treatment regimen may have different responses and clinical outcomes. Therefore, identifying new subtypes of LUAD is important for predicting prognosis and providing personalized treatment for patients. Pyroptosis-related genes play an essential role in anticancer, but there is limited research investigating pyroptosis in LUAD. In this study, 33 pyroptosis gene expression profiles and clinical information were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. By bioinformatics and machine learning analyses, we identified novel subtypes of LUAD based on 10 pyroptosis-related genes and further validated them in the GEO dataset, with machine learning models performing up to an AUC of 1 for classifying in GEO. A web-based tool was established for clinicians to use our clustering model (http://www.aimedicallab.com/tool/aiml-subphe-luad.html). LUAD patients were clustered into 3 subtypes (A, B, and C), and survival analysis showed that B had the best survival outcome and C had the worst survival outcome. The relationships between pyroptosis gene expression and clinical characteristics were further analyzed in the three molecular subtypes. Immune profiling revealed significant differences in immune cell infiltration among the three molecular subtypes. GO enrichment and KEGG pathway analyses were performed based on the differential genes of the three subtypes, indicating that differentially expressed genes (DEGs) were involved in multiple cellular and biological functions, including RNA catabolic process, mRNA catabolic process, and pathways of neurodegeneration-multiple diseases. Finally, we developed an 8-gene prognostic model that accurately predicted 1-, 3-, and 5-year overall survival. In conclusion, pyroptosis-related genes may play a critical role in LUAD, and provide new insights into the underlying mechanisms of LUAD.

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Section: Methodsmentioning

confidence: 99%

Identification and Validation of the Pyroptosis-Related Molecular Subtypes of Lung Adenocarcinoma by Bioinformatics and Machine Learning

Liu

Zhao

et al. 2021

Front. Cell Dev. Biol.

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“…Inspired by previous studies targeting sequence extraction and graph embedding learning (Zheng et al, 2018 ; Wang et al, 2021b ; Hu et al, 2023 ), the newly proposed m5U-GEPred can be divided into two main phases (see Figure 1 ). In phase one, feature extraction involved extracting the sequence-derived information and learning graph embeddings.…”

Section: Methodsmentioning

confidence: 99%

m5U-GEPred: prediction of RNA 5-methyluridine sites based on sequence-derived and graph embedding features

Xu,

Wang,

Meng

et al. 2023

Front. Microbiol.

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5-Methyluridine (m5U) is one of the most common post-transcriptional RNA modifications, which is involved in a variety of important biological processes and disease development. The precise identification of the m5U sites allows for a better understanding of the biological processes of RNA and contributes to the discovery of new RNA functional and therapeutic targets. Here, we present m5U-GEPred, a prediction framework, to combine sequence characteristics and graph embedding-based information for m5U identification. The graph embedding approach was introduced to extract the global information of training data that complemented the local information represented by conventional sequence features, thereby enhancing the prediction performance of m5U identification. m5U-GEPred outperformed the state-of-the-art m5U predictors built on two independent species, with an average AUROC of 0.984 and 0.985 tested on human and yeast transcriptomes, respectively. To further validate the performance of our newly proposed framework, the experimentally validated m5U sites identified from Oxford Nanopore Technology (ONT) were collected as independent testing data, and in this project, m5U-GEPred achieved reasonable prediction performance with ACC of 91.84%. We hope that m5U-GEPred should make a useful computational alternative for m5U identification.

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“… Zhang et al (2021c) developed a novel predictor named M6A-GSMS based on the GBDT (Gradient Boosting Decision Tree) and stacking learning to identify m6A sites ( Zhang et al, 2021c ). At the same time, Wang et al (2021) proposed the m6AGE predictor that combines sequence-derived features and graph embeddings for m6A site prediction. Recently, Rehman et al (2021a) has made use of artificial intelligence to produce an effective model, the m6A-NeuralTool, which can be utilized for speedy and efficient identification of N 6 -methyladenosine sites ( Rehman et al, 2021a ).…”

Section: Methods To Detect Rna Modificationsmentioning

confidence: 99%

Dynamic regulation and key roles of ribonucleic acid methylation

Zou

Liu

Tan

et al. 2022

Front. Cell. Neurosci.

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Ribonucleic acid (RNA) methylation is the most abundant modification in biological systems, accounting for 60% of all RNA modifications, and affects multiple aspects of RNA (including mRNAs, tRNAs, rRNAs, microRNAs, and long non-coding RNAs). Dysregulation of RNA methylation causes many developmental diseases through various mechanisms mediated by N6-methyladenosine (m6A), 5-methylcytosine (m5C), N1-methyladenosine (m1A), 5-hydroxymethylcytosine (hm5C), and pseudouridine (Ψ). The emerging tools of RNA methylation can be used as diagnostic, preventive, and therapeutic markers. Here, we review the accumulated discoveries to date regarding the biological function and dynamic regulation of RNA methylation/modification, as well as the most popularly used techniques applied for profiling RNA epitranscriptome, to provide new ideas for growth and development.

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m6AGE: A Predictor for N6-Methyladenosine Sites Identification Utilizing Sequence Characteristics and Graph Embedding-Based Geometrical Information

Cited by 11 publications

References 43 publications

Identification and Validation of the Pyroptosis-Related Molecular Subtypes of Lung Adenocarcinoma by Bioinformatics and Machine Learning

Identification and Validation of the Pyroptosis-Related Molecular Subtypes of Lung Adenocarcinoma by Bioinformatics and Machine Learning

m5U-GEPred: prediction of RNA 5-methyluridine sites based on sequence-derived and graph embedding features

Dynamic regulation and key roles of ribonucleic acid methylation

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