Deep learning-based survival prediction using DNA methylation-derived 3D genomic information
Yang, J.; Lee, D.; Koo, B.; Kim, S.
Show abstract
Three-dimensional (3D) genome states are closely related to cancer development. Nonetheless, the 3D genome information has not been clinically utilized to the best of our knowledge, due to the costly production of Hi-C data which is a manifest source of 3D genome information. Therefore, there is a need for a novel metric computable from a 3D genome-related data which is more easily accessible for the clinical utilization of 3D genome information. We here propose a method to extract 3D genome-aware epigenetic features from DNA methylation data and use these features for a deep learning-based survival prediction. These features are derived from the 3D genome structures which are rebuilt from the DNA methylation data in an individual level. The results showed that usage of 3D genome-aware features contributed to more accurate risk prediction across seven cancer types, suggesting the effectiveness of the knowledge about 3D genome structure embedded in these features. The deeper biological investigation revealed that altered DNA methylation level in risk-high group could be related to the anomalously activated genes involved in cancer-related pathways. Altogether, the risks predicted from 3D genome-aware epigenetic features showed its significance as a survival predictor in seven cancer types, along with its biological importance.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Learning interpretable cellular embedding for inferring biological mechanisms underlying single-cell transcriptomics 95%
- Enhancing single-cell cellular state inference by incorporating molecular network features 95%
- A universal approach for integrating super large-scale single-cell transcriptomes by exploring gene rankings 94%
Similar papers in this journal
- Integrative Network Analysis of Differentially Methylated and Expressed Genes for Biomarker Identification in Leukemia 96%
- Novel cancer subtyping method based on patient-specific gene regulatory network 94%
- Interpretable Deep Learning for Improving Cancer Patient Survival Based on Personal Transcriptomes 94%
Similar papers in this journal
- Gra-CRC-miRTar: The pre-trained nucleotide-to-graph neural networks to identify potential miRNA targets in colorectal cancer 94%
- Finding new cancer epigenetic and genetic biomarkers from cell-free DNA by combining SALP-seq and machine learning：esophageal cancer as an example 94%
- DeepCORE: An interpretable multi-view deep neural network model to detect co-operative regulatory elements 94%
Similar papers in this journal
Similar papers in this journal
- NanoLoop: A deep learning framework leveraging Nanopore sequencing for chromatin loop prediction 94%
- Transfer Learning and Permutation-Invariance improving Predicting Genome-wide, Cell-Specific and Directional Interventions Effects of Complex Systems 94%
- Predicting MammaPrint Recurrence Risk from Breast Cancer Pathological Images Using a Weakly Supervised Transformer 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.