NCNet: Deep Learning Network Models for Predicting Function of Non-coding DNA

By Hanyu Zhang, Che-Lun Hung, Meiyuan Liu, Xiaoye Hu, Yi-Yang Lin. 2019

17 citations recorded by the source corpus.

The human genome consists of 98.5% non-coding DNA sequences, and most of them have no known function. However, a majority of disease-associated variants lie in these regions. Therefore, it is critical to predict the function of non-coding DNA. Hence, we propose the NCNet, which integrates deep residual learning and sequence-to-sequence learning networks, to predict the transcription factor (TF) binding sites, which can then be used to predict non-coding functions. In NCNet, deep residual learning networks are used to enhance the identification rate of regulatory patterns of motifs, so that the sequence-to-sequence learning network may make the most out of the sequential dependency between the patterns. With the identity shortcut technique and deep architectures of the networks, NCNet achieves significant improvement compared to the original hybrid model in identifying regulatory markers.

Subfields: Molecular Biology, Natural Language Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

1. Chris Alberti
2. Amr Ahmed
3. Manzil Zaheer

In Natural Language Processing

1. J. Ainslie
2. Anirudh Ravula
3. Li Yang
4. Chris Alberti
5. Amr Ahmed
6. Santiago Ontañón
7. Manzil Zaheer
8. Qifan Wang

Citations in the corpus

• Big Bird: Transformers for Longer Sequences (2020, background): “There has been a recent upsurge in using deep learning for genomics data [87, 107, 13], which has resulted in improved performance on several biologically-significant tasks such as promoter site prediction [71], methylation analysis [55], predicting functional effects of non-coding variant [110],…”