LoRA-BERT：一种用于鲁棒且准确预测长链非编码RNA的自然语言处理模型
LoRA-BERT: a Natural Language Processing Model for Robust and Accurate Prediction of long non-coding RNAs
AI总结：
针对长链非编码RNA序列较长导致现有分类方法缺乏鲁棒性和准确性的问题，提出预训练模型LoRA-BERT，通过捕获核苷酸级信息提升预测性能，在准确率和效率上超越常用工具，达到最先进水平，为相关疾病检测提供潜力。
摘要：
长链非编码RNA（lncRNAs）在众多生物过程中充当关键的调节因子。尽管它们与信使RNA（mRNAs）具有序列相似性，但lncRNAs发挥着完全不同的作用，为生物学研究提供了新途径。下一代测序技术的出现极大地推进了lncRNA转录本的检测与鉴定，并且基于深度学习的方法已被引入用于对长链非编码RNAs（lncRNAs）进行分类。这些先进的方法显著提高了鉴定lncRNAs的效率。然而，由于所涉及序列的长度较长，许多此类方法缺乏鲁棒性和准确性。为了解决这个问题，我们引入了一种名为LoRA-BERT的新型预训练双向编码器表示。LoRA-BERT旨在捕获序列分类过程中核苷酸级信息的重要性，从而产生更鲁棒且令人满意的结果。在与常用序列预测工具的综合比较中，我们证明了LoRA-BERT在准确性和效率方面均优于它们。我们的结果表明，当利用transformer模型时，LoRA-BERT在预测人类和小鼠物种的lncRNAs和mRNAs方面达到了最先进的性能。通过利用LoRA-BERT，我们获得了关于lncRNAs和mRNAs特征的宝贵见解，为辅助理解和检测人类中与lncRNAs相关的疾病提供了潜力。
英文摘要：
Long non-coding RNAs (lncRNAs) serve as crucial regulators in numerous biological processes. Although they share sequence similarities with messenger RNAs (mRNAs), lncRNAs perform entirely different roles, providing new avenues for biological research. The emergence of next-generation sequencing technologies has greatly advanced the detection and identification of lncRNA transcripts and deep learning-based approaches have been introduced to classify long non-coding RNAs (lncRNAs). These advanced methods have significantly enhanced the efficiency of identifying lncRNAs. However, many of these methods are devoid of robustness and accuracy due to the extended length of the sequences involved. To tackle this issue, we have introduced a novel pre-trained bidirectional encoder representation called LoRA-BERT. LoRA-BERT is designed to capture the importance of nucleotide-level information during sequence classification, leading to more robust and satisfactory outcomes. In a comprehensive comparison with commonly used sequence prediction tools, we have demonstrated that LoRA-BERT outperforms them in terms of accuracy and efficiency. Our results indicate that, when utilizing the transformer model, LoRA-BERT achieves state-of-the-art performance in predicting both lncRNAs and mRNAs for human and mouse species. Through the utilization of LoRA-BERT, we acquire valuable insights into the traits of lncRNAs and mRNAs, offering the potential to aid in the comprehension and detection of diseases linked to lncRNAs in humans.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
