摘要翻译：
背景：在生物基因组中，启动子是位于结构基因上游的短DNA序列，具有控制基因转录的功能。启动子大致可分为两类：组成型启动子和诱导型启动子。具有明确功能注释的启动子是实用的合成生物学生物砖。为了预测候选启动子的功能，已经引入了许多统计和机器学习方法。谱特征映射是一种有效的聚类方法，而支持向量机(SVM)是一种强大的机器学习算法，尤其是在数据集较小的情况下。方法：将光谱嵌入和支持向量机两种算法应用于375个原核启动子的同一数据集。对于谱嵌入，利用编辑距离建立拉普拉斯矩阵，然后进行K-均值聚类。这些序列用数值向量表示，作为支持向量机训练的数据集。结果：支持向量机对启动子的转录功能进行了10倍交叉验证，预测准确率达93.07%。基于编辑距离的拉普拉斯本征图（光谱嵌入）可能无法提取该任务的鉴别特征。
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英文标题：
《Comparison of SVM and Spectral Embedding in Promoter Biobricks'
  Categorizing and Clustering》
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作者：
Shangjie Zou
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最新提交年份：
2019
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分类信息：

一级分类：Quantitative Biology        数量生物学
二级分类：Other Quantitative Biology        其他定量生物学
分类描述：Work in quantitative biology that does not fit into the other q-bio classifications
不适合其他q-bio分类的定量生物学工作
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英文摘要：
  Background: In organisms' genomes, promoters are short DNA sequences on the upstream of structural genes, with the function of controlling genes' transcription. Promoters can be roughly divided into two classes: constitutive promoters and inducible promoters. Promoters with clear functional annotations are practical synthetic biology biobricks. Many statistical and machine learning methods have been introduced to predict the functions of candidate promoters. Spectral Eigenmap has been proved to be an effective clustering method to classify biobricks, while support vector machine (SVM) is a powerful machine learning algorithm, especially when dataset is small. Methods: The two algorithms: spectral embedding and SVM are applied to the same dataset with 375 prokaryotic promoters. For spectral embedding, a Laplacian matrix is built with edit distance, followed by K-Means Clustering. The sequences are represented by numeric vector to serve as dataset for SVM trainning. Results: SVM achieved a high predicting accuracy of 93.07% in 10-fold cross validation for classification of promoters' transcriptional functions. Laplacian eigenmap (spectral embedding) based on editing distance may not be capable for extracting discriminative features for this task.
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PDF链接：
https://arxiv.org/pdf/1902.05724