微RNA研究中的机器学习策略:将基因组连接到现象
Sonet Daniel Thomas1,2, Krithika Vijayakumar1, Levin John1
1Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed to Be University), Manglore, Karnataka, India.
Omics : a journal of integrative biology
|May 16, 2024
概括
机器学习 (ML) 工具正在通过解决复杂的基因调节来彻底改变微RNA (miRNA) 研究. 这些方法提高了对miRNA生物发生,向相互作用和疾病关联的理解,以改善人类健康洞察力.
科学领域:
- 基因组学和生物信息学
- 分子生物学分子生物学
- 计算生物学 计算生物学
背景情况:
- 微RNAs (miRNAs) 是基因表达的关键调节者,从生化到目标相互作用呈现分析复杂性.
- 微RNA功能的复杂性需要先进的计算工具来进行全面的分析.
研究的目的:
- 审查目前用于微RNA (miRNA) 研究的机器学习 (ML) 工具和方法.
- 突出ML和miRNA研究之间的协同作用,以了解基因调节和疾病.
主要方法:
- 在miRNA研究中使用的100多种机器学习工具的系统审查.
- ML应用的分类,包括预测miRNA前体,成熟形式,标和与其他RNA的相互作用.
主要成果:
- ML有效地解决了miRNA分析中的高维数据挑战.
- 工具涵盖多个领域:促进者预测,前体/成熟miRNA识别,目标预测,RNA相互作用,表达概况,模块检测,疾病关联和基本性.
结论:
- 机器学习为剖析复杂的miRNA调节网络提供了强大的解决方案.
- 这种协同作用促进了对微RNA在人类健康和疾病中的作用的理解.
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