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生物序列表示方法和最近的进展:一篇综述
Hongwei Zhang1, Yan Shi2, Yapeng Wang1
1Faculty of Applied Sciences, Macao Polytechnic University, Macau 999078, China.
Biology
|September 27, 2025
概括
生物序列表示方法正在从计算和词嵌入技术演变为先进的大型语言模型 (LLM). 这些基于LLM的方法增强了基因组学,药物发现和疾病预测的机器学习.
科学领域:
- 计算生物学是一种计算生物学.
- 生物信息学是一种生物信息学.
- 机器学习在生物学中的应用
背景情况:
- 生物序列表示对于计算生物学中的机器学习至关重要.
- 方法转换核酸和蛋白质序列,用于增强的预测建模.
研究的目的:
- 审查和分类生物序列表示方法.
- 详细介绍不同方法的原理,应用和局限性.
- 概述该领域的未来方向.
主要方法:
- 分类为基于计算,基于词嵌入和基于LLM的方法.
- 对k-mer计数,PSSM,Word2Vec,GloVe和变压器架构 (ESM3,RNAErnie) 的分析.
主要成果:
- 计算方法捕获统计/进化模式.
- 词嵌入方法捕捉上下文关系.
- 基于LLM的方法可以模拟长距离的依赖关系,以在RNA结构预测等任务中获得更高的准确性.
结论:
- 挑战包括计算复杂性和可解释性.
- 未来的工作重点是多式联运数据集成和可解释的AI.
- 进步承诺在药物发现,疾病预测和基因组学中进行变革性应用.
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