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arXiv 1708.04019q-bio.GN
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利用人工智能方法确定非蛋白编码DNA序列是否存在复杂的相互作用关系

Determining whether the non-protein-coding DNA sequences are in a complex interactive relationship by using an artificial intelligence method

Kerim Arioglu, Umut Eser

摘要:

人类基因组的非蛋白质编码区包含许多复杂的调控细胞活性的模式。研究人类基因组是有限的,缺乏了解其特点和复杂的相互作用。然而,人工智能研究的最新进展已经能够使用深度神经网络自动学习高维复杂数据的表示,而无需特征工程。因此,在本文中,我们证明了卷积神经网络可以在不指定任何基序或模式的情况下学习DNA序列的表示,从而能够预测DNA序列是天然的还是人工的。训练后的模型可以区分加扰长度为2 bp,10 bp,50 bp甚至100 bp的加扰与真实DNA序列,其准确率显著高于线性支持向量机。通过这项研究,我们发现非蛋白质编码DNA的区域可能在甚至超过100 bp的距离上具有有意义的相互作用,即使它们不编码蛋白质。

英文摘要:

Non protein coding regions of the human genome contain many complex patterns which regulate the cellular activity. Studying the human genome is limited by the lack of understanding of its features and their complex interactions. However, recent advances in AI research have enabled automatically learning representations of high dimensional complex data without feature engineering, using deep neural networks. Therefore, in this paper, we demonstrate that a convolutional neural network can learn a representation of DNA sequence without specifying any motifs or patterns, such that it becomes capable of predicting whether a DNA sequence is natural or artificial. The trained model could distinguish scrambled vs real DNA sequences for scrambling lengths of 2 bp, 10 bp, 50 bp and even 100 bp, with a significantly higher accuracy than linear SVMs. With this study, we have discovered that regions of non protein coding DNA might have meaningful interactions at even longer than 100 bp distances even though they do not code proteins.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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