Computer Science ›› 2020, Vol. 47 ›› Issue (6A): 70-74.doi: 10.11896/JsJkx.190900065

• Artificial Intelligence • Previous Articles     Next Articles

Improving Hi-C Data Resolution with Deep Convolutional Neural Networks

CHENG Zhe, BAI Qian, ZHANG Hao, WANG Shi-pu and LIANG Yu   

  1. School of Software,Yunnan University,Kunming 650000,China
  • Published:2020-07-07
  • About author:CHENG Zhe, born in 1994, postgra-duate.His main research interests include deep learning, computer vision and bioinformatics.

Abstract: Hi-C technology measures the frequency of all paired-interaction in the entire genome.It has become one of the most popular tools for studying the 3D structure of genomes.In general,Hi-C data-based studies require sequencing of a large number of Chromosome data,while Hi-C data with lower sequencing depth,although less expensive,is not sufficient to provide sufficient biological information for subsequent studies.Since the Hi-C data contains similar sub-patterns and has data continuity within a certain area,it can be predicted.This paper explored an improved method based on convolutional neural network model.It predicts the core Hi-C values in a larger range and extends the depth and receptive field of the convolutional neural network,predicts the original sequencing reading of Hi-C by 1/16 of the original sequencing readings.The experimental results were measured by the Pearson correlation coefficient and the Spearman correlation coefficient,and the apparent interaction pairs were analyzed using Fit-Hi-C,and the state analyses of 12 chrom HMM-marked chromatin with ChromHMM were called.The experimental results show that the prediction results are not only close to the numerical distribution,but also more reliable than the low-resolution Hi-C data in terms of site interaction information and chromatin state.

Key words: Hi-C technology, Super-resolution, Convolutional neural network, Bioinformatics, Deep learning

CLC Number: 

  • TP391
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