scholarly journals Pathway-Structured Predictive Model for Cancer Survival Prediction: A Two-Stage Approach

Genetics ◽  
2016 ◽  
Vol 205 (1) ◽  
pp. 89-100 ◽  
Author(s):  
Xinyan Zhang ◽  
Yan Li ◽  
Tomi Akinyemiju ◽  
Akinyemi I. Ojesina ◽  
Phillip Buckhaults ◽  
...  
Keyword(s):  
Predictive Model ◽  
Cancer Survival ◽  
Survival Prediction ◽  
Two Stage

A tree ensemble-based two-stage model for advanced-stage colorectal cancer survival prediction

2019 ◽  
Vol 474 ◽  
pp. 106-124 ◽  
Author(s):  
Yuyan Wang ◽  
Dujuan Wang ◽  
Xin Ye ◽  
Yanzhang Wang ◽  
Yunqiang Yin ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Cancer Survival ◽  
Survival Prediction ◽  
Advanced Stage ◽  
Stage Model ◽  
Two Stage ◽  
Colorectal Cancer Survival

scholarly journals Pathway-Structured Predictive Model for Cancer Survival Prediction: A Two-Stage Approach

2016 ◽  
Author(s):  
Xinyan Zhang ◽  
Yan Li ◽  
Tomi Akinyemiju ◽  
Akinyemi Ojesina ◽  
Phillip Buckhaults ◽  
...  
Keyword(s):  
Gene Expression ◽  
Predictive Model ◽  
Cox Model ◽  
The Cancer Genome Atlas ◽  
Prognostic Scores ◽  
Survival Prediction ◽  
Two Stage ◽  
Pathway Information ◽  
Persistent Problem ◽  
Cancer Data

Heterogeneity in terms of tumor characteristics, prognosis, and survival among cancer patients has been a persistent problem for many decades. Currently, prognosis and outcome predictions are made based on clinical factors and/or by incorporating molecular profiling data. However, inaccurate prognosis and prediction may result by using only clinical or molecular information directly. One of the main shortcomings of past studies is the failure to incorporate prior biological information into the predictive model, given strong evidence of pathway-based genetic nature of cancer, i.e. the potential for oncogenes to be grouped into pathways based on biological functions such as cell survival, proliferation and metastatic dissemination. To address this problem, we propose a two-stage procedure to incorporate pathway information into the prognostic modeling using large-scale gene expression data. In the first stage, we fit all predictors within each pathway using penalized Cox model (Lasso, Ridge and Elastic Net) and Bayesian hierarchical Cox model. In the second stage, we combine the cross-validated prognostic scores of all pathways obtained in the first stage as new predictors to build an integrated prognostic model for prediction. We apply the proposed method to analyze breast cancer data from The Cancer Genome Atlas (TCGA), predicting overall survival using clinical data and gene expression profiling. The data includes ~20000 genes mapped into 109 pathways for 505 patients. The results show that the proposed approach not only improves survival prediction compared with the alternative analysis that ignores the pathway information, but also identifies significant biological pathways.

scholarly journals PD-0496: Multi-centric learning with a federated IT infrastructure: application to 2-year lung-cancer survival prediction

2013 ◽  
Vol 106 ◽  
pp. S193-S194 ◽  
Author(s):  
A. Dekker ◽  
G. Nalbantov ◽  
C. Oberije ◽  
W. Wiessler ◽  
M. Eble ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Survival ◽  
Survival Prediction ◽  
It Infrastructure ◽  
Lung Cancer Survival

scholarly journals Multiple Linear Regression Analysis of lncRNA–Disease Association Prediction Based on Clinical Prognosis Data

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Bo Wang ◽  
Jing Zhang
Keyword(s):  
Prostate Cancer ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Cancer Survival ◽  
Disease Association ◽  
The Body ◽  
Survival Prediction ◽  
Clinical Prognosis ◽  
Disease Associations ◽  
Auc Value

Long noncoding RNAs (lncRNAs) have an important role in various life processes of the body, especially cancer. The analysis of disease prognosis is ignored in current prediction on lncRNA–disease associations. In this study, a multiple linear regression model was constructed for lncRNA–disease association prediction based on clinical prognosis data (MlrLDAcp), which integrated the cancer data of clinical prognosis and the expression quantity of lncRNA transcript. MlrLDAcp could realize not only cancer survival prediction but also lncRNA–disease association prediction. Ultimately, 60 lncRNAs most closely related to prostate cancer survival were selected from 481 alternative lncRNAs. Then, the multiple linear regression relationship between the prognosis survival of 176 patients with prostate cancer and 60 lncRNAs was also given. Compared with previous studies, MlrLDAcp had a predominant survival predictive ability and could effectively predict lncRNA–disease associations. MlrLDAcp had an area under the curve (AUC) value of 0.875 for survival prediction and an AUC value of 0.872 for lncRNA–disease association prediction. It could be an effective biological method for biomedical research.

A Cancer Survival Prediction Method Based on Graph Convolutional Network

2020 ◽  
Vol 19 (1) ◽  
pp. 117-126
Author(s):  
Chunyu Wang ◽  
Junling Guo ◽  
Ning Zhao ◽  
Yang Liu ◽  
Xiaoyan Liu ◽  
...  
Keyword(s):  
Cancer Survival ◽  
Prediction Method ◽  
Survival Prediction ◽  
Convolutional Network

scholarly journals Multi-View Partial Multi-Label Learning with Graph-Based Disambiguation

2020 ◽  
Vol 34 (04) ◽  
pp. 3553-3560 ◽  
Author(s):  
Ze-Sen Chen ◽  
Xuan Wu ◽  
Qing-Guo Chen ◽  
Yao Hu ◽  
Min-Ling Zhang
Keyword(s):  
Predictive Model ◽  
Clustering Analysis ◽  
Experimental Studies ◽  
Ground Truth ◽  
Two Stage ◽  
Feature Vectors ◽  
Similarity Graph ◽  
Training Examples ◽  
Guided Clustering

In multi-view multi-label learning (MVML), each training example is represented by different feature vectors and associated with multiple labels simultaneously. Nonetheless, the labeling quality of training examples is tend to be affected by annotation noises. In this paper, the problem of multi-view partial multi-label learning (MVPML) is studied, where the set of associated labels are assumed to be candidate ones and only partially valid. To solve the MVPML problem, a two-stage graph-based disambiguation approach is proposed. Firstly, the ground-truth labels of each training example are estimated by disambiguating the candidate labels with fused similarity graph. After that, the predictive model for each label is learned from embedding features generated from disambiguation-guided clustering analysis. Extensive experimental studies clearly validate the effectiveness of the proposed approach in solving the MVPML problem.

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