A novel riboswitch classification based on imbalanced sequences achieved by machine learning
A novel riboswitch classification based on imbalanced sequences achieved by machine learning
复制标题
通过机器学习实现的基于不平衡序列的新型核糖开关分类
DOI：
10.1371/journal.pcbi.1007760
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发表时间：
2020-07-01
影响因子：
4.3
通讯作者：
Chen, Ming
中科院分区：
文献类型：
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作者：
Beyene, Solomon Shiferaw;Ling, Tianyi;Chen, Ming
核糖开关是调节mRNA（长度50-250nt）的一部分，主要有两类：适体和表达平台。核糖开关分类过程中提出的主要挑战之一是数据不平衡。这是一种情况，其中一组序列的记录与其他组相比非常小。这种情况导致分类器忽略少数群体而强调多数群体，从而导致分类出现偏差。我们考虑了十六个包含不平衡序列的核糖开关家族，以符合最近的核糖开关分类工作。使用新开发的管道将序列分为训练集和测试集。根据 WEKA 3.8 中的 CfsSubsetEval 和 BestFirst 函数，从生成的 5460 个 k-mers（k 值 1 到 6）中计算出 156 个特征。统计检验结果在平衡序列和不平衡序列之间存在显着差异（p < 0.05）。此外，每种算法在两组中使用时在灵敏度、特异性、准确性和宏观 F 分数方面也显示出显着差异 (p < 0.05)。发现从热图中聚集的几个 k-mers 在不同位置（如内部环、末端环和螺旋）具有生物功能和基序。它们被验证具有生物学功能，其中一些是核糖开关基序。该分析发现了解决多数偏差分析和过度拟合挑战的重要性。所提出的结果是平衡和不平衡模型的广义评估，这意味着它们的分类能力，对新型核糖开关进行分类。
Riboswitch, a part of regulatory mRNA (50-250nt in length), has two main classes: aptamer and expression platform. One of the main challenges raised during the classification of riboswitch is imbalanced data. That is a circumstance in which the records of a sequences of one group are very small compared to the others. Such circumstances lead classifier to ignore minority group and emphasize on majority ones, which results in a skewed classification. We considered sixteen riboswitch families, to be in accord with recent riboswitch classification work, that contain imbalanced sequences. The sequences were split into training and test set using a newly developed pipeline. From 5460 k-mers (k value 1 to 6) produced, 156 features were calculated based on CfsSubsetEval and BestFirst function found in WEKA 3.8. Statistically tested result was significantly difference between balanced and imbalanced sequences (p < 0.05). Besides, each algorithm also showed a significant difference in sensitivity, specificity, accuracy, and macro F-score when used in both groups (p < 0.05). Several k-mers clustered from heat map were discovered to have biological functions and motifs at the different positions like interior loops, terminal loops and helices. They were validated to have a biological function and some are riboswitch motifs. The analysis has discovered the importance of solving the challenges of majority bias analysis and overfitting. Presented results were generalized evaluation of both balanced and imbalanced models, which implies their ability of classifying, to classify novel riboswitches.