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首页> 外文会议>Brazilian symposium on bioinformatics >S~2FS: Single Score Feature Selection Applied to the Problem of Distinguishing Long Non-coding RNAs from Protein Coding Transcripts
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S~2FS: Single Score Feature Selection Applied to the Problem of Distinguishing Long Non-coding RNAs from Protein Coding Transcripts

机译：S〜2FS：单分数特征选择应用于区分蛋白质编码转录本中的长非编码RNA的问题

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The task of distinguishing long non-coding RNAs (IncRNAs) from protein coding transcripts (PCTs) has been previously addressed with machine learning (ML) algorithms using hundreds of features. However, the use of a large number of features can negatively affect the predictive performance of these algorithms since it can lead to problems like overfitting due to a phenomenon known as the curse of dimensionality. In order to deal with these problems, dimensionality reduction techniques have been proposed, among them, feature selection. This work proposes and experimentally evaluates a simple and fast feature selection technique, called Single Score Feature Selection - S~2FS. For such, initially, frequencies of 2-mers, 3-mers and 4-mers were extracted from public databases of PCTs and IncRNAs of Homo sapiens, resulting in a dataset composed of two groups of RNA sequences, one for PCTs and the other for IncRNAs, and a large number of features. To reduce the number of features, S~2FS was applied to the dataset. Experimental results showed that relevant features were selected, keeping the predictive accuracy, with a lower processing cost than some existing feature selection techniques.
机译：以前已经使用数百种功能通过机器学习（ML）算法解决了区分长的非编码RNA（IncRNA）与蛋白质编码转录本（PCT）的任务。但是，使用大量功能可能会对这些算法的预测性能产生负面影响，因为由于称为维数诅咒的现象，它可能导致诸如过度拟合之类的问题。为了解决这些问题，提出了降维技术，其中包括特征选择。这项工作提出并通过实验评估了一种简单快速的特征选择技术，称为单分数特征选择-S〜2FS。为此，最初，从智人的PCT和IncRNA的公共数据库中提取2聚体，3聚体和4聚体的频率，得到的数据集由两组RNA序列组成，一组用于PCT，另一组用于PCT。 IncRNA和许多功能。为了减少特征数量，将S〜2FS应用于数据集。实验结果表明，与现有的某些特征选择技术相比，选择了相关特征可以保持预测的准确性，并降低了处理成本。

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