.png){kind=icon}
.png){kind=icon}
Return
DeepC: predicting 3D genome folding using megabase-scale transfer learning
.png){kind=icon}
OA
AI
DOI：10.1038/s41592-020-0960-3.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
DeepC uses transfer learning-based deep neural networks for predicting genome folding from megabase-scale DNA sequence. Predicting the impact of noncoding genetic variation requires interpreting it in the context of three-dimensional genome architecture. We have developed deepC, a transfer-learning-based deep neural network that accurately predicts genome folding from megabase-scale DNA sequence. DeepC predicts domain boundaries at high resolution, learns the sequence determinants of genome folding and predicts the impact of both large-scale structural and single base-pair variations.
Keywords:
DOMAINS
CELL
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
32.1
Papers:
7.2K
Citations:
12.7W