Attention-based multi-label neural networks for integrated prediction and interpretation of twelve widely occurring RNA modifications

解决了同时预测和解释多种RNA修饰的挑战。通过利用深度学习技术，介绍了一个名为MultiRM的模型，能够从原始RNA序列预测十二种类型的RNA修饰。该模型成功实现了最先进的预测准确性。

ConsRM: an integrated construction and analysis platform of disease-related RNA modifications

开发了一个新颖的评分系统ConsRM，用于量化单个m6A位点的保守性，揭示高度保守的m6A位点更可能具有功能性重要性并与疾病机制相关。这种方法为研究及潜在治疗靶标的m6A位点优先排序提供了宝贵资源。

Deciphering RNA N6-methyladenosine modification by machine learning based on sequence and structure features

探索了使用考虑RNA的序列和结构特征的机器学习方法对m6A修饰位点进行预测建模。这种建模方法对于推进我们理解涉及RNA修饰的复杂调控网络至关重要。

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