Combining artificial intelligence: deep learning with Hi-C data to predict the functional effects of non-coding variants.

Journal: Bioinformatics (Oxford, England)
PMID: 33196774

Abstract

MOTIVATION: Although genome-wide association studies (GWASs) have identified thousands of variants for various traits, the causal variants and the mechanisms underlying the significant loci are largely unknown. In this study, we aim to predict non-coding variants that may functionally affect translation initiation through long-range chromatin interaction.

Authors

  • Xiang-He Meng
    Centers of System Biology, Data Information and Reproductive Health, School of Basic Medical Science, Central South University, Changsha, Hunan 410008, China.
  • Hong-Mei Xiao
    Centers of System Biology, Data Information and Reproductive Health, School of Basic Medical Science, Central South University, Changsha, Hunan 410008, China.
  • Hong-Wen Deng
    Center for Bioinformatics and Genomics, Department of Global Biostatistics and Data Science, Tulane University, New Orleans, LA 70112, USA.

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