Clustering and Candidate Motif Detection in Exosomal miRNAs by Application of Machine Learning Algorithms
Clustering and Candidate Motif Detection in Exosomal miRNAs by Application of Machine Learning Algorithms
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DOI：
10.1007/s12539-017-0253-4
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发表时间：
2019-06-01
影响因子：
4.8
通讯作者：
Chaturvedi, Anoop
中科院分区：
文献类型：
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作者：
Gaur, Pallavi;Chaturvedi, Anoop
背景聚类模式和主题提供了关于任何生物数据的大量信息。本文描述了机器学习算法在从外体衍生的miRNAs中的聚类和候选基序检测中的应用。最近在外体研究领域的进展，尤其是关于外体miRNAs的进展导致了许多基于生物信息学的研究的出现。有关来自外体的miRNAs中聚集模式和候选基序的信息将有助于分析外体中现有的和新发现的miRNAs。在获得外体miRNAs的聚类模式和候选基序的同时，阐述了机器学习算法在各种编程语言/平台上的有效性，并对数据进行了聚类，成功地检测到了序列候选基序。实验结果与BLASTN和MEME Suite等网络工具进行了比较和验证。结论针对上述目标的机器学习算法是成功的。本工作详细阐述了机器学习算法和语言平台在外体miRNAs中实现聚类和候选基序检测的实用价值。有了关于上述目标的信息，将对新发现的被认为是循环生物标记物的外体中的miRNAs进行更深入的分析。此外，机器学习算法在各种语言平台上的执行为用户提供了更大的灵活性，可以根据自己的需求尝试多次迭代。这种方法也可以应用于其他生物数据挖掘任务。
BackgroundThe clustering pattern and motifs give immense information about any biological data. An application of machine learning algorithms for clustering and candidate motif detection in miRNAs derived from exosomes is depicted in this paper. Recent progress in the field of exosome research and more particularly regarding exosomal miRNAs has led much bioinformatic-based research to come into existence. The information on clustering pattern and candidate motifs in miRNAs of exosomal origin would help in analyzing existing, as well as newly discovered miRNAs within exosomes. Along with obtaining clustering pattern and candidate motifs in exosomal miRNAs, this work also elaborates the usefulness of the machine learning algorithms that can be efficiently used and executed on various programming languages/platforms.ResultData were clustered and sequence candidate motifs were detected successfully. The results were compared and validated with some available web tools such as BLASTN' and MEME suite'.ConclusionThe machine learning algorithms for aforementioned objectives were applied successfully. This work elaborated utility of machine learning algorithms and language platforms to achieve the tasks of clustering and candidate motif detection in exosomal miRNAs. With the information on mentioned objectives, deeper insight would be gained for analyses of newly discovered miRNAs in exosomes which are considered to be circulating biomarkers. In addition, the execution of machine learning algorithms on various language platforms gives more flexibility to users to try multiple iterations according to their requirements. This approach can be applied to other biological data-mining tasks as well.