scholarly journals Informative Gene Selection and Direct Classification of Tumor Based on Chi-Square Test of Pairwise Gene Interactions

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Hongyan Zhang ◽  
Lanzhi Li ◽  
Chao Luo ◽  
Congwei Sun ◽  
Yuan Chen ◽  
...  
Keyword(s):  
Feature Selection ◽  
Gene Selection ◽  
Gene Interactions ◽  
Selection Methods ◽  
Chi Square ◽  
Generalization Performance ◽  
Independent Test ◽  
Chi Square Test ◽  
Leave One Out ◽  
Tumor Gene

In efforts to discover disease mechanisms and improve clinical diagnosis of tumors, it is useful to mine profiles for informative genes with definite biological meanings and to build robust classifiers with high precision. In this study, we developed a new method for tumor-gene selection, the Chi-square test-based integrated rank gene and direct classifier (χ2-IRG-DC). First, we obtained the weighted integrated rank of gene importance from chi-square tests of single and pairwise gene interactions. Then, we sequentially introduced the ranked genes and removed redundant genes by using leave-one-out cross-validation of the chi-square test-based Direct Classifier (χ2-DC) within the training set to obtain informative genes. Finally, we determined the accuracy of independent test data by utilizing the genes obtained above withχ2-DC. Furthermore, we analyzed the robustness ofχ2-IRG-DC by comparing the generalization performance of different models, the efficiency of different feature-selection methods, and the accuracy of different classifiers. An independent test of ten multiclass tumor gene-expression datasets showed thatχ2-IRG-DC could efficiently control overfitting and had higher generalization performance. The informative genes selected byχ2-IRG-DC could dramatically improve the independent test precision of other classifiers; meanwhile, the informative genes selected by other feature selection methods also had good performance inχ2-DC.

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.

scholarly journals Incorporating Pathway Information into Feature Selection towards Better Performed Gene Signatures

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Suyan Tian ◽  
Chi Wang ◽  
Bing Wang
Keyword(s):  
Gene Expression ◽  
Feature Selection ◽  
Gene Expression Data ◽  
Gene Selection ◽  
Selection Process ◽  
Biological Knowledge ◽  
Expression Data ◽  
Selection Methods ◽  
Its Gene ◽  
Active Research

To analyze gene expression data with sophisticated grouping structures and to extract hidden patterns from such data, feature selection is of critical importance. It is well known that genes do not function in isolation but rather work together within various metabolic, regulatory, and signaling pathways. If the biological knowledge contained within these pathways is taken into account, the resulting method is a pathway-based algorithm. Studies have demonstrated that a pathway-based method usually outperforms its gene-based counterpart in which no biological knowledge is considered. In this article, a pathway-based feature selection is firstly divided into three major categories, namely, pathway-level selection, bilevel selection, and pathway-guided gene selection. With bilevel selection methods being regarded as a special case of pathway-guided gene selection process, we discuss pathway-guided gene selection methods in detail and the importance of penalization in such methods. Last, we point out the potential utilizations of pathway-guided gene selection in one active research avenue, namely, to analyze longitudinal gene expression data. We believe this article provides valuable insights for computational biologists and biostatisticians so that they can make biology more computable.

scholarly journals DUBStepR: correlation-based feature selection for clustering single-cell RNA sequencing data

2020 ◽  
Author(s):  
Bobby Ranjan ◽  
Wenjie Sun ◽  
Jinyu Park ◽  
Ronald Xie ◽  
Fatemeh Alipour ◽  
...  
Keyword(s):  
Feature Selection ◽  
Single Cell ◽  
Gene Selection ◽  
Marker Gene ◽  
Feature Space ◽  
General Purpose ◽  
Selection Marker ◽  
Selection Methods ◽  
Sequencing Data ◽  
Data Types

Feature selection (marker gene selection) is widely believed to improve clustering accuracy, and is thus a key component of single cell clustering pipelines. However, we found that the performance of existing feature selection methods was inconsistent across benchmark datasets, and occasionally even worse than without feature selection. Moreover, existing methods ignored information contained in gene-gene correlations. We there-fore developed DUBStepR (Determining the Underlying Basis using Stepwise Regression), a feature selection algorithm that leverages gene-gene correlations with a novel measure of inhomogeneity in feature space, termed the Density Index (DI). Despite selecting a relatively small number of genes, DUB-StepR substantially outperformed existing single-cell feature selection methods across diverse clustering benchmarks. In a published scRNA-seq dataset from sorted monocytes, DUBStepR sensitively detected a rare and previously invisible population of contaminating basophils. DUBStepR is scalable to large datasets, and can be straightforwardly applied to other data types such as single-cell ATAC-seq. We propose DUBStepR as a general-purpose feature selection solution for accurately clustering single-cell data.

scholarly journals The Feature Selection Problem in Computer–Assisted Cytology

2018 ◽  
Vol 28 (4) ◽  
pp. 759-770 ◽  
Author(s):  
Marek Kowal ◽  
Marcin Skobel ◽  
Norbert Nowicki
Keyword(s):  
Feature Selection ◽  
Fine Needle Biopsy ◽  
Cancer Diagnostics ◽  
Cancer Classification ◽  
Computer Assisted ◽  
Forward Selection ◽  
Chi Square ◽  
Feature Selection Problem ◽  
Nuclei Segmentation ◽  
Chi Square Test

Abstract Modern cancer diagnostics is based heavily on cytological examinations. Unfortunately, visual inspection of cytological preparations under the microscope is a tedious and time-consuming process. Moreover, intra- and inter-observer variations in cytological diagnosis are substantial. Cytological diagnostics can be facilitated and objectified by using automatic image analysis and machine learning methods. Computerized systems usually preprocess cytological images, segment and detect nuclei, extract and select features, and finally classify the sample. In spite of the fact that a lot of different computerized methods and systems have already been proposed for cytology, they are still not routinely used because there is a need for improvement in their accuracy. This contribution focuses on computerized breast cancer classification. The task at hand is to classify cellular samples coming from fine-needle biopsy as either benign or malignant. For this purpose, we compare 5 methods of nuclei segmentation and detection, 4 methods of feature selection and 4 methods of classification. Nuclei detection and segmentation methods are compared with respect to recall and the F1 score based on the Jaccard index. Feature selection and classification methods are compared with respect to classification accuracy. Nevertheless, the main contribution of our study is to determine which features of nuclei indicate reliably the type of cancer. We also check whether the quality of nuclei segmentation/detection significantly affects the accuracy of cancer classification. It is verified using the test set that the average accuracy of cancer classification is around 76%. Spearman’s correlation and chi-square test allow us to determine significantly better features than the feature forward selection method.

scholarly journals Deep Learning Classifier for Gene Expression Datasets using a Hybrid LSTM Network

2020 ◽  
Vol 9 (4) ◽  
pp. 1081-1089
Keyword(s):  
Feature Selection ◽  
Deep Learning ◽  
Bayesian Networks ◽  
Selection Process ◽  
Hybrid Approach ◽  
Learning System ◽  
Nearest Neighbour ◽  
Chi Square ◽  
Correlation Degree ◽  
Chi Square Test

A deep learning system Long Short-term memory (LSTM) is incorporated for the classification of differentially expressed genes which causes certain abnormalities in the human body. The LSTM is employed along with the K-Nearest Neighbour (KNN) algorithm so as to achieve the classification to its precision. The feature selection process plays a vital as some of the existing algorithms tend to neglect the features of concern. The classification further leads to enhanced prediction method. The K-Nearest Neighbour method is used to filter the correlation degree between each value with target value. This hybrid algorithm has a clear leverage over the existing methods. This work is well supported by the Feature Selection which includes a hybrid of Principal Component Analysis and the CHI square test. This hybrid approach provides with a good feature selection which aides in the seamless flow of the process towards classification and prediction. The Eigen values and the Eigen vectors are computed which effectively leads to the identification of Principal components. The Chi Square test is implemented for calculating the scores. The features that are obtained are ranked by these scores and the datasets which has the highest scores are further taken for training. The algorithms employed in this work has a clear advantage over the Bayesian networks as the Bayesian networks are prone to errors within the layers which may cause the values to explode or vanish. The accuracy of the classification and the prediction process achieved is unsurpassed when compared to the existing methods.

scholarly journals The Impact of Feature Selection Methods for Classifying Arabic Textual Data

2019 ◽  
Vol 8 (4) ◽  
pp. 1333-1338
Keyword(s):  
Feature Selection ◽  
Text Classification ◽  
Information Gain ◽  
Feature Space ◽  
Support Vector ◽  
Selection Methods ◽  
K Nearest Neighbors ◽  
Chi Square ◽  
Selection Algorithms ◽  
The Impact

Text classification is a vital process due to the large volume of electronic articles. One of the drawbacks of text classification is the high dimensionality of feature space. Scholars developed several algorithms to choose relevant features from article text such as Chi-square (x2 ), Information Gain (IG), and Correlation (CFS). These algorithms have been investigated widely for English text, while studies for Arabic text are still limited. In this paper, we investigated four well-known algorithms: Support Vector Machines (SVMs), Naïve Bayes (NB), K-Nearest Neighbors (KNN), and Decision Tree against benchmark Arabic textual datasets, called Saudi Press Agency (SPA) to evaluate the impact of feature selection methods. Using the WEKA tool, we have experimented the application of the four mentioned classification algorithms with and without feature selection algorithms. The results provided clear evidence that the three feature selection methods often improves classification accuracy by eliminating irrelevant features.

A Novel Hybrid Filter/Wrapper Feature Selection Approach based on Improved Fruit Fly Optimization Algorithm and Chi-square Test for High Dimensional Microarray Data

2020 ◽  
Vol 15 ◽  
Author(s):  
Chaokun Yan ◽  
Jingjing Ma ◽  
Bin Wu ◽  
Jianlin Wang ◽  
Ge Zhang ◽  
...  
Keyword(s):  
Feature Selection ◽  
Fruit Fly ◽  
High Dimensional ◽  
Feature Subset ◽  
Fruit Fly Optimization Algorithm ◽  
Hybrid Filter ◽  
Chi Square ◽  
Fruit Fly Optimization ◽  
Chi Square Test ◽  
Optimal Feature

Aims: Microarray data is widely used in disease analysis and diagnosis. However, these data could contain thousands of genes and a small number of samples and some existing models cannot capture the patterns on these datasets accurately without utilizing feature selection method. Background: Feature selection is an important stage in data preprocessing. Given the limitations of employing filter or wrapper approaches individually for feature selection, it is promising to combine filter and wrapper into a hybrid algorithm by utilizing their respective advantages to search optimal feature subsets. Objective: The primary objective of this study is to design a good feature selection strategy for high-dimensional biomedical datasets. Method: A novel hybrid filter-wrapper approach is proposed for high dimensional datasets. First, the Chi-square Test is utilized to filter out most of the irrelevant or redundant features. Next, an improved binary Fruit Fly Optimization algorithm is used to further search optimal feature subset without degrading the classification accuracy. The KNN classifier with the 10-fold-CV is utilized to evaluate the accuracy of classification. Result: Experimental results show that CS-IFOA can use a smaller number of features while achieving higher classification accuracy. Furthermore, the standard deviation of the calculation results is relatively small, indicating that the repeated 10-fold-CV is reliable and the proposed algorithm is relatively robust. Conclusion: Proposed strategy can be used as an ideal pre-processing tool to help optimize the feature selection process of high-dimensional biomedical data sets, which further indicate integrating filter method into wrapper model can enhance the performance of feature subset selection. Other: For future work, proposed strategy will be applied to many other biological datasets, and other classifiers can also be combined with this strategy to verify and extend this approach. The findings of our study could open a basis for further research for hybrid feature selects approaches.

scholarly journals A COMPARATIVE STUDY ON GENE SELECTION METHODS FOR TISSUES CLASSIFICATION ON LARGE SCALE GENE EXPRESSION DATA

2016 ◽  
Vol 78 (5-10) ◽  
Author(s):  
Farzana Kabir Ahmad
Keyword(s):  
Gene Expression ◽  
Feature Selection ◽  
Gene Expression Data ◽  
Large Scale ◽  
Gene Selection ◽  
Support Vector ◽  
Breast Cancer Dataset ◽  
Expression Data ◽  
Selection Methods ◽  
Normal Tissues

Deoxyribonucleic acid (DNA) microarray technology is the recent invention that provided colossal opportunities to measure a large scale of gene expressions simultaneously. However, interpreting large scale of gene expression data remain a challenging issue due to their innate nature of “high dimensional low sample size”. Microarray data mainly involved thousands of genes, n in a very small size sample, p which complicates the data analysis process. For such a reason, feature selection methods also known as gene selection methods have become apparently need to select significant genes that present the maximum discriminative power between cancerous and normal tissues. Feature selection methods can be structured into three basic factions; a) filter methods; b) wrapper methods and c) embedded methods. Among these methods, filter gene selection methods provide easy way to calculate the informative genes and can simplify reduce the large scale microarray datasets. Although filter based gene selection techniques have been commonly used in analyzing microarray dataset, these techniques have been tested separately in different studies. Therefore, this study aims to investigate and compare the effectiveness of these four popular filter gene selection methods namely Signal-to-Noise ratio (SNR), Fisher Criterion (FC), Information Gain (IG) and t-Test in selecting informative genes that can distinguish cancer and normal tissues. In this experiment, common classifiers, Support Vector Machine (SVM) is used to train the selected genes. These gene selection methods are tested on three large scales of gene expression datasets, namely breast cancer dataset, colon dataset, and lung dataset. This study has discovered that IG and SNR are more suitable to be used with SVM. Furthermore, this study has shown SVM performance remained moderately unaffected unless a very small size of genes was selected.

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