scholarly journals Biomarker discovery for predicting spontaneous preterm birth from gene expression data by regularized logistic regression

2020 ◽  
Vol 18 ◽  
pp. 3434-3446
Author(s):  
Lingyu Li ◽  
Zhi-Ping Liu
Keyword(s):  
Gene Expression ◽  
Logistic Regression ◽  
Preterm Birth ◽  
Gene Expression Data ◽  
Biomarker Discovery ◽  
Expression Data ◽  
Spontaneous Preterm Birth

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

BIOMARKER DISCOVERY AND VISUALIZATION IN GENE EXPRESSION DATA WITH EFFICIENT GENERALIZED MATRIX APPROXIMATIONS

2007 ◽  
Vol 05 (02a) ◽  
pp. 251-279 ◽  
Author(s):  
WENYUAN LI ◽  
YANXIONG PENG ◽  
HUNG-CHUNG HUANG ◽  
YING LIU
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Biomarker Discovery ◽  
Attribute Selection ◽  
Specific Gene ◽  
Expression Data ◽  
Selection Methods ◽  
Data Set ◽  
Generalized Matrix ◽  
Matrix Approximations

In most real-world gene expression data sets, there are often multiple sample classes with ordinals, which are categorized into the normal or diseased type. The traditional feature or attribute selection methods consider multiple classes equally without paying attention to the up/down regulation across the normal and diseased types of classes, while the specific gene selection methods particularly consider the differential expressions across the normal and diseased, but ignore the existence of multiple classes. In this paper, to improve the biomarker discovery, we propose to make the best use of these two aspects: the differential expressions (that can be viewed as the domain knowledge of gene expression data) and the multiple classes (that can be viewed as a kind of data set characteristic). Therefore, we simultaneously take into account these two aspects by employing the 1-rank generalized matrix approximations (GMA). Our results show that GMA cannot only improve the accuracy of classifying the samples, but also provide a visualization method to effectively analyze the gene expression data on both genes and samples. Based on the mechanism of matrix approximation, we further propose an algorithm, CBiomarker, to discover compact biomarker by reducing the redundancy.

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 Learning Parsimonious Classification Rules from Gene Expression Data Using Bayesian Networks with Local Structure

2016 ◽  
Author(s):  
Jonathan Lyle Lustgarten ◽  
Jeya Balaji Balasubramanian ◽  
Shyam Visweswaran ◽  
Vanathi Gopalakrishnan
Keyword(s):  
Gene Expression ◽  
Decision Tree ◽  
Bayesian Networks ◽  
Gene Expression Data ◽  
Local Structure ◽  
Biomarker Discovery ◽  
Predictive Performance ◽  
Global Constraints ◽  
Expression Data ◽  
Worst Case

The comprehensibility of good predictive models learned from high-dimensional gene expression data is attractive because it can lead to biomarker discovery. Several good classifiers provide comparable predictive performance but differ in their abilities to summarize the observed data. We extend a Bayesian Rule Learning (BRL-GSS) algorithm, previously shown to be a significantly better predictor than other classical approaches in this domain. It searches a space of Bayesian networks using a decision tree representation of its parameters with global constraints, and infers a set of IF-THEN rules. The number of parameters and therefore the number of rules are combinatorial to the number of predictor variables in the model. We relax these global constraints to a more generalizable local structure (BRL-LSS). BRL-LSS entails more parsimonious set of rules because it does not have to generate all combinatorial rules. The search space of local structures is much richer than the space of global structures. We design the BRL-LSS with the same worst-case time-complexity as BRL-GSS while exploring a richer and more complex model space. We measure predictive performance using Area Under the ROC curve (AUC) and Accuracy. We measure model parsimony performance by noting the average number of rules and variables needed to describe the observed data. We evaluate the predictive and parsimony performance of BRL-GSS, BRL-LSS and the state-of-the-art C4.5 decision tree algorithm, across 10-fold cross-validation using ten microarray gene-expression diagnostic datasets. In these experiments, we observe that BRL-LSS is similar to BRL-GSS in terms of predictive performance, while generating a much more parsimonious set of rules to explain the same observed data. BRL-LSS also needs fewer variables than C4.5 to explain the data with similar predictive performance. We also conduct a feasibility study to demonstrate the general applicability of our BRL methods on the newer RNA sequencing gene-expression data.

Sign in / Sign up

Export Citation Format

Share Document