TY - JOUR
T1 - i-Modern
T2 - Integrated multi-omics network model identifies potential therapeutic targets in glioma by deep learning with interpretability
AU - Pan, Xingxin
AU - Burgman, Brandon
AU - Wu, Erxi
AU - Huang, Jason H.
AU - Sahni, Nidhi
AU - Stephen Yi, S.
N1 - Funding Information:
The authors are grateful to contributions from TCGA Research Network Analysis Working Group, and also acknowledge the Biomedical Research Computing Facility at UT Austin, and Texas Advanced Computing Center (TACC) for assistance. This work was supported by the National Institutes of Health grants GM133658 (to S.Y.) and GM137836 (to N.S.). N.S. is a CPRIT Scholar in Cancer Research with funding from the Cancer Prevention and Research Institute of Texas (CPRIT) New Investigator Grant RR160021 and Research Grant RP220292. S.Y. was also supported by a Scialog program sponsored jointly by Research Corporation for Science Advancement and the Gordon and Betty Moore Foundation (Award# 28418). Xingxin Pan: Investigation, Data curation, Formal analysis, Writing - original draft, Writing - review & editing. Brandon Burgman: Writing - review & editing. Erxi Wu: Writing - review & editing. Jason H. Huang: Writing - review & editing. Nidhi Sahni: Funding acquisition, Project administration, Writing - review & editing. S. Stephen Yi: Funding acquisition, Project administration, Supervision, Writing - original draft, Writing - review & editing.
Funding Information:
This work was supported by the National Institutes of Health grants GM133658 (to S.Y.) and GM137836 (to N.S.). N.S. is a CPRIT Scholar in Cancer Research with funding from the Cancer Prevention and Research Institute of Texas (CPRIT) New Investigator Grant RR160021 and Research Grant RP220292. S.Y. was also supported by a Scialog program sponsored jointly by Research Corporation for Science Advancement and the Gordon and Betty Moore Foundation (Award# 28418).
Publisher Copyright:
© 2022 The Authors
PY - 2022/1
Y1 - 2022/1
N2 - Effective and precise classification of glioma patients for their disease risks is critical to improving early diagnosis and patient survival. In the recent past, a significant amount of multi-omics data derived from cancer patients has emerged. However, a robust framework for integrating multi-omics data types to efficiently and precisely subgroup glioma patients and predict survival prognosis is still lacking. In addition, effective therapeutic targets for treating glioma patients with poor prognoses are in dire need. To begin to resolve this difficulty, we developed i-Modern, an integrated Multi-omics deep learning network method, and optimized a sophisticated computational model in gliomas that can accurately stratify patients based on their prognosis. We built a survival-associated predictive framework integrating transcription profile, miRNA expression, somatic mutations, copy number variation (CNV), DNA methylation, and protein expression. This framework achieved promising performance in distinguishing high-risk glioma patients from those with good prognoses. Furthermore, we constructed multiple fully connected neural networks that are trained on prioritized multi-omics signatures or even only potential single-omics signatures, based on our customized scoring system. Together, the landmark multi-omics signatures we identified may serve as potential therapeutic targets in gliomas.
AB - Effective and precise classification of glioma patients for their disease risks is critical to improving early diagnosis and patient survival. In the recent past, a significant amount of multi-omics data derived from cancer patients has emerged. However, a robust framework for integrating multi-omics data types to efficiently and precisely subgroup glioma patients and predict survival prognosis is still lacking. In addition, effective therapeutic targets for treating glioma patients with poor prognoses are in dire need. To begin to resolve this difficulty, we developed i-Modern, an integrated Multi-omics deep learning network method, and optimized a sophisticated computational model in gliomas that can accurately stratify patients based on their prognosis. We built a survival-associated predictive framework integrating transcription profile, miRNA expression, somatic mutations, copy number variation (CNV), DNA methylation, and protein expression. This framework achieved promising performance in distinguishing high-risk glioma patients from those with good prognoses. Furthermore, we constructed multiple fully connected neural networks that are trained on prioritized multi-omics signatures or even only potential single-omics signatures, based on our customized scoring system. Together, the landmark multi-omics signatures we identified may serve as potential therapeutic targets in gliomas.
KW - Data integration
KW - Deep learning model
KW - Glioma
KW - Multi-omics
KW - Patient stratification
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U2 - 10.1016/j.csbj.2022.06.058
DO - 10.1016/j.csbj.2022.06.058
M3 - Article
C2 - 35860408
AN - SCOPUS:85133918189
SN - 2001-0370
VL - 20
SP - 3511
EP - 3521
JO - Computational and Structural Biotechnology Journal
JF - Computational and Structural Biotechnology Journal
ER -