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从生物生成到深度建模:对miRNA疾病预测计算方法的整体审查与实验比较
1Faculty of Applied Sciences, Macao Polytechnic University, Macao 999078, SAR, China.
识别微RNA-疾病关联 (MDA) 对疾病诊断和治疗至关重要. 本综述汇编和分析了用于预测MDAs的计算方法,为未来的研究和个性化医学提供了见解.
科学领域:
- 生物医学信息学 生物医学信息学
- 基因组学就是基因组学.
- 计算生物学 计算生物学
背景情况:
- 微RNA (miRNA) 失调与各种疾病有关.
- 在疾病的发病,诊断和治疗中,miRNAs至关重要.
- 识别miRNA疾病关联 (MDA) 有助于发现生物标志物和向疗法.
研究的目的:
- 审查用于预测miRNA-疾病关联 (MDA) 的计算方法.
- 分析MDA研究的数据资源和相似度量化方法.
- 提供现有计算方法的全面概述和比较分析.
主要方法:
- 在miRNA疾病关联研究中使用的数据资源的分析.
- 引入用于量化miRNA-疾病关联之间的相似性方法.
- 对66种计算预测方法进行了全面的审查和分类,将其分为五类.
- 对选定的预测方法进行比较实验分析.
主要成果:
- 对MDA预测的计算方法的结构化概述.
- 66种方法被分为五个不同的类别.
- 比较实验结果,突出各种方法的性能.
- 在计算MDA预测中确定未来的研究方向.
结论:
- 计算方法为MDA预测提供了传统湿实验室实验的有效替代方案.
- 本综述为该领域的研究人员提供了必要的背景知识.
- 通过GitHub存储库来增强对总结方法的可访问性,用于未来的MDA预测研究.
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