Related references
Note: Only part of the references are listed.SALMON: Survival Analysis Learning With Multi-Omics Neural Networks on Breast Cancer
Zhi Huang et al.
FRONTIERS IN GENETICS (2019)
Simultaneous Interrogation of Cancer Omics to Identify Subtypes With Significant Clinical Differences
Aodan Xu et al.
FRONTIERS IN GENETICS (2019)
A Translational Pipeline for Overall Survival Prediction of Breast Cancer Patients by Decision-Level Integration of Multi-Omics Data
Jonathan Mitchel et al.
2019 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM) (2019)
Deep Multimodal Representation Learning: A Survey
Wenzhong Guo et al.
IEEE ACCESS (2019)
LinkedOmics: analyzing multi-omics data within and across 32 cancer types
Suhas V. Vasaikar et al.
NUCLEIC ACIDS RESEARCH (2018)
DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network
Jared L. Katzman et al.
BMC MEDICAL RESEARCH METHODOLOGY (2018)
Min-redundancy and max-relevance multi-view feature selection for predicting ovarian cancer survival using multi-omics data
Yasser EL-Manzalawy et al.
BMC MEDICAL GENOMICS (2018)
Deep Learning-Based Multi-Omics Integration Robustly Predicts Survival in Liver Cancer
Kumardeep Chaudharyl et al.
CLINICAL CANCER RESEARCH (2018)
Data integration and predictive modeling methods for multi-omics datasets
Minseung Kim et al.
MOLECULAR OMICS (2018)
Dimension reduction techniques for the integrative analysis of multi-omics data
Chen Meng et al.
BRIEFINGS IN BIOINFORMATICS (2016)
On the C-statistics for evaluating overall adequacy of risk prediction procedures with censored survival data
Hajime Uno et al.
STATISTICS IN MEDICINE (2011)