GR-m6A: Prediction of N6-methyladenosine sites in mammals with molecular graph and residual network

Shi, Qingping; Liu, Renxin; Liang, Ying

doi:10.1016/j.compbiomed.2023.107202

Cited by 3 publications

(4 citation statements)

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“…We used six evaluation metrics (precision, recall, accuracy, F1 score, AUC, and AUPR (Shen et al, 2022;Liu et al, 2023;Qiu et al, 2023a)) to assess the performance of LDA-SABC and four other LDA prediction algorithms (SDLDA, LDNFSGB, IPCARF, and LDASR) under three different fivefold cross validations. The three CVs are fivefold CV on lncRNAs (CV l ), five-fold CV on diseases (CV d ), and fivefold CV on LDPs (CV ld ).…”

Section: Comparison With Four Classical Lda Prediction Methodsmentioning

confidence: 99%

Finding potential lncRNA–disease associations using a boosting-based ensemble learning model

Zhou,

Peng,

Zeng

et al. 2024

Front. Genet.

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Introduction: Long non-coding RNAs (lncRNAs) have been in the clinical use as potential prognostic biomarkers of various types of cancer. Identifying associations between lncRNAs and diseases helps capture the potential biomarkers and design efficient therapeutic options for diseases. Wet experiments for identifying these associations are costly and laborious.Methods: We developed LDA-SABC, a novel boosting-based framework for lncRNA–disease association (LDA) prediction. LDA-SABC extracts LDA features based on singular value decomposition (SVD) and classifies lncRNA–disease pairs (LDPs) by incorporating LightGBM and AdaBoost into the convolutional neural network.Results: The LDA-SABC performance was evaluated under five-fold cross validations (CVs) on lncRNAs, diseases, and LDPs. It obviously outperformed four other classical LDA inference methods (SDLDA, LDNFSGB, LDASR, and IPCAF) through precision, recall, accuracy, F1 score, AUC, and AUPR. Based on the accurate LDA prediction performance of LDA-SABC, we used it to find potential lncRNA biomarkers for lung cancer. The results elucidated that 7SK and HULC could have a relationship with non-small-cell lung cancer (NSCLC) and lung adenocarcinoma (LUAD), respectively.Conclusion: We hope that our proposed LDA-SABC method can help improve the LDA identification.

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Section: Comparison With Four Classical Lda Prediction Methodsmentioning

confidence: 99%

Finding potential lncRNA–disease associations using a boosting-based ensemble learning model

Zhou,

Peng,

Zeng

et al. 2024

Front. Genet.

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“…The data is then updated using the latest Ensembl version (v109) (57), and the corresponding sequences are obtained. Each datapoint was represented with a 41-nucleotide sequence centred on m6A sites (Figure S1), as similar to previous human m6A predictors (40-44). To improve the specificity of our dataset, only m6A sites that are located within DRACH motifs are included.…”

Section: Methodsmentioning

confidence: 99%

“…Machine learning models offer several advantages for addressing biological problems, primarily owing to their capability to recognise complex patterns from a large dataset (25). More than ten machine learning models have been proposed in the last decade to predict human m6A methylation sites (26-44). A broad overview of each model is summarised in Table S1.…”

Section: Introductionmentioning

confidence: 99%

“…In addition to concerns about model performance, the accessibility of models poses a significant challenge. The analysis of the 19 models presented in Table S1 reveals that nine models are not accessible (26,28,29,31,32,36-38,41), five models are exclusively available in source code (30,34,39,42,44), two models provide pre-calculated prediction results (33,35), and only three models can be accessed via web servers (27,40,43). Establishing user-friendly web servers is crucial in order to enhance the accessibility of the models.…”

Section: Introductionmentioning

confidence: 99%

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AI-m6ARS: Machine learning-driven m6A RNA methylation site discovery with integrated sequence, conservation, and geographical descriptors

Uthayopas,

de Sá,

Ascher

2024

Preprint

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N6-Methyladenosine (m6A) is a predominant type of human RNA methylation, regulating diverse biochemical processes and being associated with the development of several diseases. Despite its significance, an extensive experimental examination across diverse cellular and transcriptome contexts is still lacking due to time and cost constraints. Computational models have been proposed to prioritise potential m6A methylation sites, although having limited predictive performance due to inadequate characterisation and modelling of m6A sites. This work presents AI-m6ARS, a novel model that utilises integrated sequence, conservation, and geographical descriptive features to predict human m6A methylation sites. The model was trained using the Light Gradient Boosting Machine (LightGBM) algorithm, which was coupled with comprehensive feature selection to improve the data quality. AI-m6RS demonstrates strong predictive capabilities, achieving an impressive area under the receiver operating characteristic curve of 0.87 on cross-validation. Consistent results on unseen transcripts in a blind test highlight the AI-m6ARS generalisability. AI-m6ARS also demonstrates comparable performance to state-of-the-art models, but offers two significant benefits: the model interpretability and the availability of a user-friendly web server. The AI-m6ARS web server offers valuable insights into the distribution of m6A sites within the human genome, thereby facilitating progress in medical applications.GRAPHICAL ABSTRACT

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BLAM6A-Merge: Leveraging Attention Mechanisms and Feature Fusion Strategies to Improve the Identification of RNA N6-Methyladenosine Sites

Xia,

Zhang,

Liu

et al. 2024

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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GR-m6A: Prediction of N6-methyladenosine sites in mammals with molecular graph and residual network

Cited by 3 publications

References 46 publications

Finding potential lncRNA–disease associations using a boosting-based ensemble learning model

Finding potential lncRNA–disease associations using a boosting-based ensemble learning model

AI-m6ARS: Machine learning-driven m6A RNA methylation site discovery with integrated sequence, conservation, and geographical descriptors

BLAM6A-Merge: Leveraging Attention Mechanisms and Feature Fusion Strategies to Improve the Identification of RNA N6-Methyladenosine Sites

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