2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 2019
DOI: 10.1109/bibm47256.2019.8983243
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A Translational Pipeline for Overall Survival Prediction of Breast Cancer Patients by Decision-Level Integration of Multi-Omics Data

Abstract: Breast cancer is the most prevalent and among the most deadly cancers in females. Patients with breast cancer have highly variable survival rates, indicating a need to identify prognostic biomarkers. By integrating multi-omics data (e.g., gene expression, DNA methylation, miRNA expression, and copy number variations (CNVs)), it is likely to improve the accuracy of patient survival predictions compared to prediction using single modality data. Therefore, we propose to develop a machine learning pipeline using d… Show more

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Cited by 10 publications
(7 citation statements)
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References 15 publications
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“…Among the four -omics data, miRNA expression is the most predictive for overall survival, followed by DNA methylation and gene expression. Moreover, CNVs are the least predictive for breast cancer overall survival, which is consistent with our previous findings [20]. The best single-omics survival analysis performance is a C-index of 0.616 ± 0.057, achieved by miRNA data with PCA features.…”
Section: Multi-modality Integration For Breast Cancer Survival Analysissupporting
confidence: 91%
See 1 more Smart Citation
“…Among the four -omics data, miRNA expression is the most predictive for overall survival, followed by DNA methylation and gene expression. Moreover, CNVs are the least predictive for breast cancer overall survival, which is consistent with our previous findings [20]. The best single-omics survival analysis performance is a C-index of 0.616 ± 0.057, achieved by miRNA data with PCA features.…”
Section: Multi-modality Integration For Breast Cancer Survival Analysissupporting
confidence: 91%
“…In our previous study [20], we have built a transnational pipeline for overall survival prediction of breast cancer patients by decision-level integration of multiomics data (e.g., gene expression, DNA methylation, miRNA expression, and copy number variations (CNVs)). However, many right-censored samples have been discarded to enable binary classification.…”
Section: Introductionmentioning
confidence: 99%
“…The present study used a multi-layer perceptron neural network (MLPNN). Despite the increased complexity of interpreting neural network outputs compared to outputs of other statistical models, the ANN model has been widely used in various medical fields [ 13 , 14 ].…”
Section: Methodsmentioning
confidence: 99%
“…While in this work we focused on tumor transcriptome data which can be measured with high precision over a wide dynamic range of transcript abundances by RNA-seq, we note that TCGA datasets of tumor somatic mutations and copy number alteration events are also available (Hutter and Zenklusen, 2018). Given the voluminous literature on the use of tumor somatic genomic data for precision cancer diagnosis (Mitchel et al , 2019; Zhang et al , 2020; Lee et al , 2019), tumor DNA datasets are fertile ground for developing a semi-supervised, multi-omics model for predicting response to chemotherapy.…”
Section: Discussionmentioning
confidence: 99%