This review comprehensively explores the core application of artificial intelligence (AI) in the fields of genomics and bioinformatics, and deeply analyzes how it leads the innovative progress of science. In the cutti...This review comprehensively explores the core application of artificial intelligence (AI) in the fields of genomics and bioinformatics, and deeply analyzes how it leads the innovative progress of science. In the cutting-edge fields of genomics and bioinformatics, the application of AI is propelling a deeper understanding of complex genetic mechanisms and the development of innovative therapeutic approaches. The precision of AI in genomic sequence analysis, coupled with breakthroughs in precise gene editing, such as AI-designed gene editors, significantly enhances our comprehension of gene functions and disease associations . Moreover, AI’s capabilities in disease prediction, assessing individual disease risks through genomic data analysis, provide robust support for personalized medicine. AI applications extend beyond gene identification, gene expression pattern prediction, and genomic structural variant analysis, encompassing key areas such as epigenetics, multi-omics data integration, genetic disease diagnosis, evolutionary genomics, and non-coding RNA function prediction. Despite challenges including data privacy, algorithm transparency, and bioethical issues, the future of AI is expected to continue revolutionizing genomics and bioinformatics, ushering in a new era of personalized medicine and precision treatments.展开更多
基因与表型间的关联分析对揭示生物的内在遗传关联具有重要意义.随机游走算法可以融合多组学数据,聚合一阶或高阶邻居的标签信息,对网络中不同节点间关联信息进行补全,提高关联预测的准确度,进而发现基因和表型间潜在的遗传关联.但现有...基因与表型间的关联分析对揭示生物的内在遗传关联具有重要意义.随机游走算法可以融合多组学数据,聚合一阶或高阶邻居的标签信息,对网络中不同节点间关联信息进行补全,提高关联预测的准确度,进而发现基因和表型间潜在的遗传关联.但现有随机游走算法通常平等地对待每个节点,忽略了不同节点的重要性,使非重要节点过度传播,降低了模型性能.为此,本文提出了一种基于多组学数据融合的个性化随机游走算法(individual Multiple Random Walks,iMRW),在由基因、miRNA及表型节点构建的多组学异质网络上,基于网络拓扑结构,设计个性化多元随机游走策略,为不同重要程度的节点分配不同的游走步长,并结合高斯相互作用属性核相似性与随机游走,对网络不同节点及节点间关联信息进行补全,最终实现多源基因-表型关联矩阵的融合,准确获取基因-表型关联预测矩阵.在不同实验设置下,与主流算法的对比实验结果均显示iMRW能够取得更优的预测性能.在玉米光合作用能力和淀粉含量表型的实验分析结果也进一步证实了iMRW在识别潜在的基因-表型关联的实用性与有效性.展开更多
文摘This review comprehensively explores the core application of artificial intelligence (AI) in the fields of genomics and bioinformatics, and deeply analyzes how it leads the innovative progress of science. In the cutting-edge fields of genomics and bioinformatics, the application of AI is propelling a deeper understanding of complex genetic mechanisms and the development of innovative therapeutic approaches. The precision of AI in genomic sequence analysis, coupled with breakthroughs in precise gene editing, such as AI-designed gene editors, significantly enhances our comprehension of gene functions and disease associations . Moreover, AI’s capabilities in disease prediction, assessing individual disease risks through genomic data analysis, provide robust support for personalized medicine. AI applications extend beyond gene identification, gene expression pattern prediction, and genomic structural variant analysis, encompassing key areas such as epigenetics, multi-omics data integration, genetic disease diagnosis, evolutionary genomics, and non-coding RNA function prediction. Despite challenges including data privacy, algorithm transparency, and bioethical issues, the future of AI is expected to continue revolutionizing genomics and bioinformatics, ushering in a new era of personalized medicine and precision treatments.
文摘基因与表型间的关联分析对揭示生物的内在遗传关联具有重要意义.随机游走算法可以融合多组学数据,聚合一阶或高阶邻居的标签信息,对网络中不同节点间关联信息进行补全,提高关联预测的准确度,进而发现基因和表型间潜在的遗传关联.但现有随机游走算法通常平等地对待每个节点,忽略了不同节点的重要性,使非重要节点过度传播,降低了模型性能.为此,本文提出了一种基于多组学数据融合的个性化随机游走算法(individual Multiple Random Walks,iMRW),在由基因、miRNA及表型节点构建的多组学异质网络上,基于网络拓扑结构,设计个性化多元随机游走策略,为不同重要程度的节点分配不同的游走步长,并结合高斯相互作用属性核相似性与随机游走,对网络不同节点及节点间关联信息进行补全,最终实现多源基因-表型关联矩阵的融合,准确获取基因-表型关联预测矩阵.在不同实验设置下,与主流算法的对比实验结果均显示iMRW能够取得更优的预测性能.在玉米光合作用能力和淀粉含量表型的实验分析结果也进一步证实了iMRW在识别潜在的基因-表型关联的实用性与有效性.
