In:
Future Oncology, Future Medicine Ltd, Vol. 18, No. 2 ( 2022-01), p. 215-230
Kurzfassung:
Aims: This study presents a survival stratification model based on multi-omics integration using bidirectional deep neural networks (BiDNNs) in gastric cancer. Methods: Based on the survival-related representation features yielded by BiDNNs through integrating transcriptomics and epigenomics data, K-means clustering analysis was performed to cluster tumor samples into different survival subgroups. The BiDNNs-based model was validated using tenfold cross-validation and in two independent confirmation cohorts. Results: Using the BiDNNs-based survival stratification model, patients were grouped into two survival subgroups with log-rank p-value = 9.05E-05. The subgroups classification was robustly validated in tenfold cross-validation (C-index = 0.65 ± 0.02) and in two confirmation cohorts (E-GEOD-26253, C-index = 0.609; E-GEOD-62254, C-index = 0.706). Conclusion: We propose and validate a robust and stable BiDNN-based survival stratification model in gastric cancer.
Materialart:
Online-Ressource
ISSN:
1479-6694
,
1744-8301
DOI:
10.2217/fon-2021-1059
Sprache:
Englisch
Verlag:
Future Medicine Ltd
Publikationsdatum:
2022
