High-dimensional pseudo-logistic regression and classification with applications to gene expression data

2007 ◽  
Vol 52 (1) ◽  
pp. 452-470 ◽  
Author(s):  
Chunming Zhang ◽  
Haoda Fu ◽  
Yuan Jiang ◽  
Tao Yu
Keyword(s):  
Gene Expression ◽  
Logistic Regression ◽  
Gene Expression Data ◽  
High Dimensional ◽  
Expression Data

scholarly journals On the Performance of Variable Selection and Classification via Rank-Based Classifier

Mathematics ◽  
2019 ◽  
Vol 7 (5) ◽  
pp. 457 ◽  
Author(s):  
Md Sarker ◽  
Michael Pokojovy ◽  
Sangjin Kim
Keyword(s):  
Gene Expression ◽  
Logistic Regression ◽  
Gene Expression Data ◽  
Gene Selection ◽  
Laplacian Matrix ◽  
Adaptive Lasso ◽  
High Dimensional ◽  
Expression Data ◽  
Correlation Pattern ◽  
Future Outcomes

In high-dimensional gene expression data analysis, the accuracy and reliability of cancer classification and selection of important genes play a very crucial role. To identify these important genes and predict future outcomes (tumor vs. non-tumor), various methods have been proposed in the literature. But only few of them take into account correlation patterns and grouping effects among the genes. In this article, we propose a rank-based modification of the popular penalized logistic regression procedure based on a combination of ℓ 1 and ℓ 2 penalties capable of handling possible correlation among genes in different groups. While the ℓ 1 penalty maintains sparsity, the ℓ 2 penalty induces smoothness based on the information from the Laplacian matrix, which represents the correlation pattern among genes. We combined logistic regression with the BH-FDR (Benjamini and Hochberg false discovery rate) screening procedure and a newly developed rank-based selection method to come up with an optimal model retaining the important genes. Through simulation studies and real-world application to high-dimensional colon cancer gene expression data, we demonstrated that the proposed rank-based method outperforms such currently popular methods as lasso, adaptive lasso and elastic net when applied both to gene selection and classification.

scholarly journals Robust Sparse Logistic Regression With the $L_{q}$ ($0 < \text{q} < 1$ ) Regularization for Feature Selection Using Gene Expression Data

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 68586-68595 ◽  
Author(s):  
Ziyi Yang ◽  
Yong Liang ◽  
Hui Zhang ◽  
Hua Chai ◽  
Bowen Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Logistic Regression ◽  
Feature Selection ◽  
Gene Expression Data ◽  
Expression Data ◽  
Sparse Logistic Regression

scholarly journals LRpath: a logistic regression approach for identifying enriched biological groups in gene expression data

2008 ◽  
Vol 25 (2) ◽  
pp. 211-217 ◽  
Author(s):  
Maureen A. Sartor ◽  
George D. Leikauf ◽  
Mario Medvedovic
Keyword(s):  
Gene Expression ◽  
Logistic Regression ◽  
Gene Expression Data ◽  
Expression Data ◽  
Regression Approach
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