scholarly journals Learning and Feature Selection Using the Set Covering Machine with Data-Dependent Rays on Gene Expression Profiles

2006 ◽  
pp. 286-297
Author(s):  
Hans A. Kestler ◽  
Wolfgang Lindner ◽  
André Müller
Keyword(s):  
Gene Expression ◽  
Feature Selection ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Set Covering

scholarly journals A Robust Gene selection Method for Microarray-based Cancer Classification

2010 ◽  
Vol 9 ◽  
pp. CIN.S3794 ◽  
Author(s):  
Xiaosheng Wang ◽  
Osamu Gotoh
Keyword(s):  
Gene Expression ◽  
Feature Selection ◽  
Gene Selection ◽  
Information Gain ◽  
Expression Profiles ◽  
Feature Selection Method ◽  
Gene Expression Profiles ◽  
Molecular Classification ◽  
Selection Method ◽  
Chi Square

Gene selection is of vital importance in molecular classification of cancer using high-dimensional gene expression data. Because of the distinct characteristics inherent to specific cancerous gene expression profiles, developing flexible and robust feature selection methods is extremely crucial. We investigated the properties of one feature selection approach proposed in our previous work, which was the generalization of the feature selection method based on the depended degree of attribute in rough sets. We compared the feature selection method with the established methods: the depended degree, chi-square, information gain, Relief-F and symmetric uncertainty, and analyzed its properties through a series of classification experiments. The results revealed that our method was superior to the canonical depended degree of attribute based method in robustness and applicability. Moreover, the method was comparable to the other four commonly used methods. More importantly, the method can exhibit the inherent classification difficulty with respect to different gene expression datasets, indicating the inherent biology of specific cancers.

Improved Feature Selection by Incorporating Gene Similarity into the LASSO

2012 ◽  
Vol 3 (1) ◽  
pp. 1-22 ◽  
Author(s):  
Christopher E. Gillies ◽  
Xiaoli Gao ◽  
Nilesh V. Patel ◽  
Mohammad-Reza Siadat ◽  
George D. Wilson
Keyword(s):  
Gene Expression ◽  
Feature Selection ◽  
Personalized Medicine ◽  
Objective Function ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Genetic Profile ◽  
Data Set ◽  
Coordinate Descent Algorithm ◽  
Gene Similarity

Personalized medicine is customizing treatments to a patient’s genetic profile and has the potential to revolutionize medical practice. An important process used in personalized medicine is gene expression profiling. Analyzing gene expression profiles is difficult, because there are usually few patients and thousands of genes, leading to the curse of dimensionality. To combat this problem, researchers suggest using prior knowledge to enhance feature selection for supervised learning algorithms. The authors propose an enhancement to the LASSO, a shrinkage and selection technique that induces parameter sparsity by penalizing a model’s objective function. Their enhancement gives preference to the selection of genes that are involved in similar biological processes. The authors’ modified LASSO selects similar genes by penalizing interaction terms between genes. They devise a coordinate descent algorithm to minimize the corresponding objective function. To evaluate their method, the authors created simulation data where they compared their model to the standard LASSO model and an interaction LASSO model. The authors’ model outperformed both the standard and interaction LASSO models in terms of detecting important genes and gene interactions for a reasonable number of training samples. They also demonstrated the performance of their method on a real gene expression data set from lung cancer cell lines.

Correlation-based Gene Selection and Classification Using Taguchi-BPSO

2010 ◽  
Vol 49 (03) ◽  
pp. 254-268 ◽  
Author(s):  
C.-S. Yang ◽  
K.-C. Wu ◽  
C.-H. Yang ◽  
L.-Y. Chuang
Keyword(s):  
Gene Expression ◽  
Feature Selection ◽  
Microarray Data ◽  
Error Rate ◽  
Gene Expression Analysis ◽  
Gene Selection ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Classification Error ◽  
Classification Error Rate

Summary Background: Microarray data with reference to gene expression profiles have provided some valuable results related to a variety of problems, and contributed to advances in clinical medicine. Microarray data characteristically have a high dimension and small sample size, which makes it difficult for a general classification method to obtain correct data for classification. However, not every gene is potentially relevant for distinguishing the sample class. Thus, in order to analyze gene expression profiles correctly, feature (gene) selection is crucial for the classification process, and an effective gene extraction method is necessary for eliminating irrelevant genes and decreasing the classification error rate. Objective: The purpose of gene expression analysis is to discriminate between classes of samples, and to predict the relative importance of each gene for sample classification. Method: In this paper, correlation-based feature selection (CFS) and Taguchi-binary particle swarm optimization (TBPSO) were combined into a hybrid method, and the K-nearest neighbor (K-NN) with leave-one-out cross-validation (LOOCV) method served as a classifier for ten gene expression profiles. Results: Experimental results show that this hybrid method effectively simplifies feature selection by reducing the number of features needed. The classification error rate obtained by the proposed method had the lowest classification error rate for all of the ten gene expression data set problems tested. For six of the gene expression profile data sets a classification error rate of zero could be reached. Conclusion: The introduced method outperformed five other methods from the literature in terms of classification error rate. It could thus constitute a valuable tool for gene expression analysis in future studies.

scholarly journals Identification of the Gene Expression Rules That Define the Subtypes in Glioma

2018 ◽  
Vol 7 (10) ◽  
pp. 350 ◽  
Author(s):  
Yu-Dong Cai ◽  
Shiqi Zhang ◽  
Yu-Hang Zhang ◽  
Xiaoyong Pan ◽  
KaiYan Feng ◽  
...  
Keyword(s):  
Gene Expression ◽  
Feature Selection ◽  
Single Cell ◽  
Anaplastic Astrocytoma ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Diffuse Astrocytoma ◽  
Feature List ◽  
Cell Gene Expression ◽  
Cell Gene

As a common brain cancer derived from glial cells, gliomas have three subtypes: glioblastoma, diffuse astrocytoma, and anaplastic astrocytoma. The subtypes have distinctive clinical features but are closely related to each other. A glioblastoma can be derived from the early stage of diffuse astrocytoma, which can be transformed into anaplastic astrocytoma. Due to the complexity of these dynamic processes, single-cell gene expression profiles are extremely helpful to understand what defines these subtypes. We analyzed the single-cell gene expression profiles of 5057 cells of anaplastic astrocytoma tissues, 261 cells of diffuse astrocytoma tissues, and 1023 cells of glioblastoma tissues with advanced machine learning methods. In detail, a powerful feature selection method, Monte Carlo feature selection (MCFS) method, was adopted to analyze the gene expression profiles of cells, resulting in a feature list. Then, the incremental feature selection (IFS) method was applied to the obtained feature list, with the help of support vector machine (SVM), to extract key features (genes) and construct an optimal SVM classifier. Several key biomarker genes, such as IGFBP2, IGF2BP3, PRDX1, NOV, NEFL, HOXA10, GNG12, SPRY4, and BCL11A, were identified. In addition, the underlying rules of classifying the three subtypes were produced by Johnson reducer algorithm. We found that in diffuse astrocytoma, PRDX1 is highly expressed, and in glioblastoma, the expression level of PRDX1 is low. These rules revealed the difference among the three subtypes, and how they are formed and transformed. These genes are not only biomarkers for glioma subtypes, but also drug targets that may switch the clinical features or even reverse the tumor progression.

Informative Gene Selection Based on Cost-Sensitive Fast Correlation-Based Feature Selection

2021 ◽  
Vol 16 ◽  
Author(s):  
Yueling Xiong ◽  
Qingqing Li ◽  
Peipei Wang ◽  
Mingquan Ye
Keyword(s):  
Gene Expression ◽  
Feature Selection ◽  
Candidate Gene ◽  
Gene Selection ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Informative Gene ◽  
Gene Subset ◽  
Correlation Based Feature Selection ◽  
Selection Algorithms

Background: Informative gene selection is an essential step in performing tumor classification. However, it is difficult to select informative genes related to tumors from large-scale gene expression profiles because of their characteristics, such as high dimensionality, relatively small samples, and class imbalance, and some genes being superfluous and irrelevant. Objective: Many researchers analyze and process gene expression data to obtain classified gene subsets by using machine learning methods. However, the gene expression profiles of tumors often have massive computational challenges. In addition, when improving feature importance and classification accuracy, cost estimation is often ignored in traditional feature selection algorithms, which makes tumor classification more difficult. Method: In this study, a novel informative gene selection method based on cost-sensitive fast correlation-based feature selection (CS-FCBF) is proposed. Results: First, the symmetric uncertainty index is used to evaluate the correlation between informative genes and class labels, and then a large number of irrelevant and redundant genes are quickly filtered according to importance. Thereby, a candidate gene subset is generated. Second, cost-sensitive learning, which introduces the misclassification cost matrix and support vector machine attribute evaluation, is used to obtain the top-ranked gene subset with minimum misclassification loss. Finally, the candidate gene subset is optimized. Conclusion: This experiment was verified in eight independent tumor datasets. By comparing and analyzing CS-FCBF with another three hybrids of typical gene selection algorithms combined with cost-sensitive learning, we found that the method proposed in this study exhibited a better classification performance with fewer selected genes, which might provide guidance in tumor diagnosis and research.

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