非编码RNA功能预测的整合分析策略 -生物科学专业

摘要

非编码RNA（ncRNA）在基因调控网络中发挥着关键作用，然而其功能多样性和复杂性为研究带来了挑战。本研究旨在通过整合分析策略预测ncRNA的功能，以克服单一方法的局限性并提高预测准确性。为此，我们构建了一种基于多组学数据和机器学习算法的综合框架，该框架结合了序列特征、表达谱数据以及功能注释信息，能够有效挖掘ncRNA与生物学过程之间的潜在关联。通过对多个物种的数据集进行验证，结果表明该方法在功能预测的精确度和覆盖率上显著优于现有技术。这一成果不仅为ncRNA的功能研究提供了新工具，还为进一步探索其在疾病发生发展中的作用奠定了基础。总的来说，本研究提出的整合分析策略具有广泛的应用前景，并为理解ncRNA的生物学意义开辟了新的途径。

关键词：非编码RNA功能预测；整合分析策略；多组学数据；机器学习算法；动态权重分配机制

An Integrative Analysis Strategy for Predicting the Functions of Non-Coding RNAs

英文人名

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Abstract

Non-coding Rnas (Ncrnas) play a key role in gene regulatory networks, but their functional diversity and complexity pose research challenges. This study aims to predict the function of ncRNA by integrating analytical strategies to overcome the limitations of a single method and improve prediction accuracy. To this end, we construct a comprehensive fr amework based on multi-omics data and machine learning algorithms, which combines sequence features, ex pression profile data, and functional annotation information to effectively mine potential associations between Ncrnas and biological processes. Through the validation of multiple species data sets, the results show that the method is significantly superior to the prior art in the accuracy and coverage of functional prediction. This achievement not only provides a new tool for the functional study of Ncrnas, but also lays a foundation for further exploration of their role in the occurrence and development of diseases. Overall, the integrated analysis strategy proposed in this study has broad application prospects and opens up a new way to understand the biological significance of Ncrnas.

Keywords: Non-Coding Rna Function Prediction；Integrated Analysis Strategy；Multi-Omics Data；Machine Learning Algorithm；Dynamic Weight Allocation Mechanism

引言 1

一、非编码RNA功能预测的背景与意义 1

(一)非编码RNA的研究现状 1

(二)功能预测的重要性分析 2

(三)整合分析策略的提出背景 2

二、整合分析策略的核心方法论 3

(一)数据整合的基本原则 3

(二)多组学数据的应用框架 3

(三)机器学习在预测中的作用 4

三、整合分析策略的技术实现路径 4

(一)数据预处理与标准化方法 4

(二)特征选择与模型构建技术 5