Artificial Intelligence with Great Potential in Medical Informatics: A Brief Review

Lin, Hao

doi:10.47852/bonviewmedin42022204

Cited by 4 publications

(1 citation statement)

References 106 publications

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“…Post-transcriptional modification of RNA plays a crucial role in a variety of biological processes . To date, over 170 types of RNA modifications have been cataloged across various molecules. − Notably, N-7methylguanosine (m7G) modification, a prevalent form of post-transcriptional regulation, has garnered considerable attention in recent research.…”

Section: Introductionmentioning

confidence: 99%

Moss-m7G: A Motif-Based Interpretable Deep Learning Method for RNA N7-Methlguanosine Site Prediction

Zhao,

Jin,

Gao

et al. 2024

J. Chem. Inf. Model.

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N-7methylguanosine (m7G) modification plays a crucial role in various biological processes and is closely associated with the development and progression of many cancers. Accurate identification of m7G modification sites is essential for understanding their regulatory mechanisms and advancing cancer therapy. Previous studies often suffered from insufficient research data, underutilization of motif information, and lack of interpretability. In this work, we designed a novel motif-based interpretable method for m7G modification site prediction, called Moss-m7G. This approach enables the analysis of RNA sequences from a motif-centric perspective. Our proposed word-detection module and motif-embedding module within Moss-m7G extract motif information from sequences, transforming the raw sequences from base-level into motif-level and generating embeddings for these motif sequences. Compared with base sequences, motif sequences contain richer contextual information, which is further analyzed and integrated through the Transformer model. We constructed a comprehensive m7G data set to implement the training and testing process to address the data insufficiency noted in prior research. Our experimental results affirm the effectiveness and superiority of Moss-m7G in predicting m7G modification sites. Moreover, the introduction of the word-detection module enhances the interpretability of the model, providing insights into the predictive mechanisms.

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