Classification Of Gene Expression Data Research Articles

Classification of gene expression data: A hubness-aware semi-supervised approach

Background and objectiveClassification of gene expression data is the common denominator of various biomedical recognition tasks. However, obtaining class labels for large training samples may be difficult or even impossible in many cases. Therefore, semi-supervised classification techniques are required as semi-supervised classifiers take advantage of unlabeled data. MethodsGene expression data is high-dimensional which gives rise to the phenomena known under the umbrella of the curse of dimensionality, one of its recently explored aspects being the presence of hubs or hubness for short. Therefore, hubness-aware classifiers have been developed recently, such as Naive Hubness-Bayesian k-Nearest Neighbor (NHBNN). In this paper, we propose a semi-supervised extension of NHBNN which follows the self-training schema. As one of the core components of self-training is the certainty score, we propose a new hubness-aware certainty score. ResultsWe performed experiments on publicly available gene expression data. These experiments show that the proposed classifier outperforms its competitors. We investigated the impact of each of the components (classification algorithm, semi-supervised technique, hubness-aware certainty score) separately and showed that each of these components are relevant to the performance of the proposed approach. ConclusionsOur results imply that our approach may increase classification accuracy and reduce computational costs (i.e., runtime). Based on the promising results presented in the paper, we envision that hubness-aware techniques will be used in various other biomedical machine learning tasks. In order to accelerate this process, we made an implementation of hubness-aware machine learning techniques publicly available in the PyHubs software package (http://www.biointelligence.hu/pyhubs) implemented in Python, one of the most popular programming languages of data science.

Hybrid Dimension Reduction Techniques with Genetic Algorithm and Neural Network for Classifying Leukemia Gene Expression Data

Background/Objectives: This paper presents a hybrid framework for classification of leukemia gene expression data. The framework used in this work consists of three subsystems, namely, class based dimension reduction subsystem, feature selection subsystem and classification subsystem. Methods/Statistical Analysis: This work uses class based dimension reduction techniques by employing PCA and Canonical Correlation Analysis (CCA) to the leukemia gene expression dataset. Acute Lymphoblastic Leukemia (ALL) class is subjected to Principal Component Analysis (PCA) and Acute Myeloid Leukemia (AML) class to CCA thus obtaining dimension reduced data. The feature selection subsystem uses Genetic Algorithm (GA) to select an optimal subset of informative genes. The classification subsystem utilizes these informative genes to train the NN and the classifier is obtained. Findings: The performance of the hybrid framework, GA-PCA and CCA, is analyzed and compared with that of single dimension reduction techniques, namely, GA-PCA and GA-CCA. The experimental results show that the proposed framework achieved accuracy of 88.23%. The sensitivity of the system is 85% and specificity of the system is 92.85%. This aids in determining the informative genes that are relevant to leukemia gene expression data. Applications/Improvements: The classification accuracy of GA-PCA and CCA has shown improvement when compared to that of single dimension reduction technique. Hence, combining more than one method yields higher classification accuracy and aids in identification of new classes.

A semi-supervised rough set and random forest approach for pattern classification of gene expression data

A semi-supervised rough set and random forest approach for pattern classification of gene expression data

A semi-supervised rough set and random forest approach for pattern classification of gene expression data

In this paper, we present a semi-supervised rough set-based random forest gene selection method for classification of data patterns. The proposed method tries to find the genes of interest known as significant genes and maximise the accuracy of the model with reduction percentage. The advantage of this approach is analysed by experimental results on three benchmark datasets such as leukaemia, colon cancer and SRBCT and results showed an improved accuracy over existing methods such as support vector machine, k-nearest neighbour and random forest. Finally, the performance of those selected significant genes has been measured using classifier validity and statistical measures. The experimental results and performance measures proves the efficiency of the proposed hybridised technique over traditional random forest method.

Locally linear embedding and neighborhood rough set-based gene selection for gene expression data classification.

Cancer subtype recognition and feature selection are important problems in the diagnosis and treatment of tumors. Here, we propose a novel gene selection approach applied to gene expression data classification. First, two classical feature reduction methods including locally linear embedding (LLE) and rough set (RS) are summarized. The advantages and disadvantages of these algorithms were analyzed and an optimized model for tumor gene selection was developed based on LLE and neighborhood RS (NRS). Bhattacharyya distance was introduced to delete irrelevant genes, pair-wise redundant analysis was performed to remove strongly correlated genes, and the wavelet soft threshold was determined to eliminate noise in the gene datasets. Next, prior optimized search processing was carried out. A new approach combining dimension reduction of LLE and feature reduction of NRS (LLE-NRS) was developed for selecting gene subsets, and then an open source software Weka was applied to distinguish different tumor types and verify the cross-validation classification accuracy of our proposed method. The experimental results demonstrated that the classification performance of the proposed LLE-NRS for selecting gene subset outperforms those of other related models in terms of accuracy, and our proposed approach is feasible and effective in the field of high-dimensional tumor classification.

A new data-driven method for microarray data classification

Knowledge gained through classification of microarray gene expression data is increasingly important as they are useful for phenotype classification of diseases. In this paper we propose a rule-based approach called 'Large Coverage Rule' for microarray data classification. The proposed approach is a parameter-free data-driven approach that constructs decision rule based on the expression values of a gene. A simple Rank-Based Scoring algorithm is proposed for selecting informative genes. The performance of the proposed approach is evaluated using ten publicly available gene expression data sets. From the simulation result, it is found that the proposed approach generates compact rules and produces comparatively good classification accuracy than the others. Gene ontology based biological semantics is also carried out to analyse the informative genes. Statistical analysis of test result shows that the generated rules are simple to interpret, highly comprehensible and classifies microarray data accurately.

A hybrid heuristic dimensionality reduction technique for microarray gene expression data classification: a blending of GA, PSO and ACO

The micro-array gene expression data are mostly utilised for biological applications for classification of tumours in genes. Majority of recent work are concentrated to reduce the dimension of micro-array gene expression data. The modern dimension reduction approaches have followed statistical techniques and transformation techniques like principal component analysis (PCA). This paper deals with a hybrid heuristic dimensionality reduction technique for microarray gene expression data classification using biologically inspired heuristic algorithms. The proposed hybrid heuristic technique has combined genetic algorithm (GA), particle swarm optimisation (PSO) and ant colony optimisation (ACO). The dimensionality reduced data using the proposed technique correlates well with the classifier with less classification error after training. The test case investigation is performed with acute lymphoblastic leukaemia (ALL) and acute myeloid leukaemia (AML) dataset and the classification accuracy of the proposed technique are compared with conventional technique.

Applying Cost-Sensitive Extreme Learning Machine and Dissimilarity Integration to Gene Expression Data Classification.

Embedding cost-sensitive factors into the classifiers increases the classification stability and reduces the classification costs for classifying high-scale, redundant, and imbalanced datasets, such as the gene expression data. In this study, we extend our previous work, that is, Dissimilar ELM (D-ELM), by introducing misclassification costs into the classifier. We name the proposed algorithm as the cost-sensitive D-ELM (CS-D-ELM). Furthermore, we embed rejection cost into the CS-D-ELM to increase the classification stability of the proposed algorithm. Experimental results show that the rejection cost embedded CS-D-ELM algorithm effectively reduces the average and overall cost of the classification process, while the classification accuracy still remains competitive. The proposed method can be extended to classification problems of other redundant and imbalanced data.

Novel gene sets improve set-level classification of prokaryotic gene expression data

BackgroundSet-level classification of gene expression data has received significant attention recently. In this setting, high-dimensional vectors of features corresponding to genes are converted into lower-dimensional vectors of features corresponding to biologically interpretable gene sets. The dimensionality reduction brings the promise of a decreased risk of overfitting, potentially resulting in improved accuracy of the learned classifiers. However, recent empirical research has not confirmed this expectation. Here we hypothesize that the reported unfavorable classification results in the set-level framework were due to the adoption of unsuitable gene sets defined typically on the basis of the Gene ontology and the KEGG database of metabolic networks. We explore an alternative approach to defining gene sets, based on regulatory interactions, which we expect to collect genes with more correlated expression. We hypothesize that such more correlated gene sets will enable to learn more accurate classifiers.MethodsWe define two families of gene sets using information on regulatory interactions, and evaluate them on phenotype-classification tasks using public prokaryotic gene expression data sets. From each of the two gene-set families, we first select the best-performing subtype. The two selected subtypes are then evaluated on independent (testing) data sets against state-of-the-art gene sets and against the conventional gene-level approach.ResultsThe novel gene sets are indeed more correlated than the conventional ones, and lead to significantly more accurate classifiers. The novel gene sets are indeed more correlated than the conventional ones, and lead to significantly more accurate classifiers.ConclusionNovel gene sets defined on the basis of regulatory interactions improve set-level classification of gene expression data. The experimental scripts and other material needed to reproduce the experiments are available at http://ida.felk.cvut.cz/novelgenesets.tar.gz.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0786-7) contains supplementary material, which is available to authorized users.

Gene Expression Data Classification Using Support Vector Machine and Mutual Information-based Gene Selection

DNA microarray technology can monitor the expression levels of thousands of genes simultaneously during important biological processes and across collections of related samples. Knowledge gained through microarray data analysis is increasingly important as they are useful for phenotype classification of diseases. This paper presents an effective method for gene classification using Support Vector Machine (SVM). SVM is a supervised learning algorithm capable of solving complex classification problems. Mutual information (MI) between the genes and the class label is used for identifying the informative genes. The selected genes are utilized for training the SVM classifier and the testing ability is evaluated using Leave-one-Out Cross Validation (LOOCV) method. The performance of the proposed approach is evaluated using two cancer microarray datasets. From the simulation study it is observed that the proposed approach reduces the dimension of the input features by identifying the most informative gene subset and improve classification accuracy when compared to other approaches.

Regularised extreme learning machine with misclassification cost and rejection cost for gene expression data classification.

The main purpose of traditional classification algorithms on bioinformatics application is to acquire better classification accuracy. However, these algorithms cannot meet the requirement that minimises the average misclassification cost. In this paper, a new algorithm of cost-sensitive regularised extreme learning machine (CS-RELM) was proposed by using probability estimation and misclassification cost to reconstruct the classification results. By improving the classification accuracy of a group of small sample which higher misclassification cost, the new CS-RELM can minimise the classification cost. The 'rejection cost' was integrated into CS-RELM algorithm to further reduce the average misclassification cost. By using Colon Tumour dataset and SRBCT (Small Round Blue Cells Tumour) dataset, CS-RELM was compared with other cost-sensitive algorithms such as extreme learning machine (ELM), cost-sensitive extreme learning machine, regularised extreme learning machine, cost-sensitive support vector machine (SVM). The results of experiments show that CS-RELM with embedded rejection cost could reduce the average cost of misclassification and made more credible classification decision than others.

Mapping microarray gene expression data into dissimilarity spaces for tumor classification

Microarray gene expression data sets usually contain a large number of genes, but a small number of samples. In this article, we present a two-stage classification model by combining feature selection with the dissimilarity-based representation paradigm. In the preprocessing stage, the ReliefF algorithm is used to generate a subset with a number of top-ranked genes; in the learning/classification stage, the samples represented by the previously selected genes are mapped into a dissimilarity space, which is then used to construct a classifier capable of separating the classes more easily than a feature-based model. The ultimate aim of this paper is not to find the best subset of genes, but to analyze the performance of the dissimilarity-based models by means of a comprehensive collection of experiments for the classification of microarray gene expression data. To this end, we compare the classification results of an artificial neural network, a support vector machine and the Fisher’s linear discriminant classifier built on the feature (gene) space with those on the dissimilarity space when varying the number of genes selected by ReliefF, using eight different microarray databases. The results show that the dissimilarity-based classifiers systematically outperform the feature-based models. In addition, classification through the proposed representation appears to be more robust (i.e. less sensitive to the number of genes) than that with the conventional feature-based representation.

Disagreement Measure Based Ensemble of Extreme Learning Machine for Gene Expression Data Classification

Selective ensemble learning has become a powerful tool for biological information analysis of gene expression data.In order to mining gene expression data better,we use ensemble of Extreme Learning Machine(ELM) to overcome the shortage that a single ELM is unstable in data classification.In this paper,we propose an algorithm,which is the dissimilarity ensemble based on disagreement measure of Extreme Learning Machines(D-D-ELM).First,we judge the dissimilarity of Extreme Learning Machines with disagreement measure.Then we remove the corresponding ELMs based on the average classification accuracy.At last,the rest ELMs are grouped into an ensemble classifier by the strategy of majority voting.This algorithm is applied on the data of gene expression Breast,Leukemia,Colon,Heart.The theoretical analysis and experiment are given and the statistical analysis on the experimental results demonstrates that D-D-ELM can achieve better classification accuracy with less number of ELMs.

Classification of Micro Array Gene Expression Proposed using Statistical Approaches

Classification analysis of microarray gene expression data has been performed widely to find out the biological features and to differentiate intimately related cell types that usually appear in the diagnosis of cancer. Many algorithms and techniques have been developed for the microarray gene classification process. These developed techniques accomplish microarray gene classification process with the aid of three basic phases namely, dimensionality reduction, feature selection and gene classification. In our previous work, microarray gene classification by statistical analysis approach with Fuzzy Inference System (FIS) was proposed for precise classification of genes to their corresponding gene types. Among various dimensionality reduction techniques, the paper proposed prescribed statistical procedures to efficiently perform the classification process. To further substantiate and to analyze the performance, we conduct a comparative study in this work. The comparative study considers two popular dimensionality reduction techniques called Principle Component Analysis (PCA) and Multi-linear Principle Component Analysis (MPCA). The dimensionality reduction techniques replace the proposed statistical approach and perform microarray gene expression data classification. Based on the obtained results, we conduct the performance study over the combination of statistical approach with FIS, LPP with FIS and MPCA with FIS. The study results that the statistical approach with FIS outperforms the classification performance when compared to the other methods.

A new approach to enhance the performance of decision tree for classifying gene expression data

BackgroundGene expression data classification is a challenging task due to the large dimensionality and very small number of samples. Decision tree is one of the popular machine learning approaches to address such classification problems. However, the existing decision tree algorithms use a single gene feature at each node to split the data into its child nodes and hence might suffer from poor performance specially when classifying gene expression dataset.ResultsBy using a new decision tree algorithm where, each node of the tree consists of more than one gene, we enhance the classification performance of traditional decision tree classifiers. Our method selects suitable genes that are combined using a linear function to form a derived composite feature. To determine the structure of the tree we use the area under the Receiver Operating Characteristics curve (AUC). Experimental analysis demonstrates higher classification accuracy using the new decision tree compared to the other existing decision trees in literature.ConclusionWe experimentally compare the effect of our scheme against other well known decision tree techniques. Experiments show that our algorithm can substantially boost the classification performance of the decision tree.

Dissimilarity based ensemble of extreme learning machine for gene expression data classification

Extreme learning machine (ELM) has salient features such as fast learning speed and excellent generalization performance. However, a single extreme learning machine is unstable in data classification. To overcome this drawback, more and more researchers consider using ensemble of ELMs. This paper proposes a method integrating voting-based extreme learning machines (V-ELMs) with dissimilarity (D-ELM). First, based on different dissimilarity measures, we remove a number of ELMs from the ensemble pool. Then, the remaining ELMs are grouped as an ensemble classifier by majority voting. Finally we use disagreement measure and double-fault measure to validate the D-ELM. The theoretical analysis and experimental results on gene expression data demonstrate that (1) the D-ELM can achieve better classification accuracy with less number of ELMs; (2) the double-fault measure based D-ELM (DF-D-ELM) performs better than disagreement measure based D-ELM (D-D-ELM).

Microarray gene-expression data classification using less gene expressions by combining feature selection methods and classifiers

Microarray Data, often characterised by high-dimensions and small samples, is used for cancer classification problems that classify the given (tissue) samples as deceased or healthy on the basis of analysis of gene expression profile. The goal of feature selection is to search the most relevant features from thousands of related features of a particular problem domain. The focus of this study is a method that relaxes the maximum accuracy criterion for feature selection and selects the combination of feature selection method and classifier that using small subset of features obtains accuracy not statistically indicatively different than the maximum accuracy. By selecting the classifier employing small number of features along with a good accuracy, the risk of over fitting (bias) is reduced. This has been corroborated empirically using some common attribute selection methods (ReliefF, SVM-RFE, FCBF, and Gain Ratio) and classifiers (3 Nearest Neighbour, Naive Bayes and SVM) applied to 6 different microarray cancer data sets. We use hypothesis testing to compare several configurations and select particular configurations that perform well with small genes on these data sets.

Michigan vs. Pittsburgh Style GA Optimisation of Fuzzy Rule Bases for Gene Expression Analysis

Microarray studies and gene expression analysis have received a lot of attention and provide many promising avenues towards the understanding of fundamental questions in biology and medicine. In this paper, the authors perform gene expression analysis and apply two hybrid GA-fuzzy approaches to classify gene expression data. Both are based on fuzzy if-then rule bases but they differ in the way these rule bases are optimised. The authors employ both a Michigan style approach, where single rules are handled as individuals in the population of the genetic algorithm, and a Pittsburgh type algorithm, which treats whole rule sets as individuals. Experimental results show that both approaches achieve good classification accuracy but that the Michigan style algorithm clearly outperforms the Pittsburgh classifier.

Classification of gene expression data using Spiking Wavelet Radial Basis Neural Network

The paper discusses how Spiking Wavelet Radial Basis Neural Network can be effectively used for the classification of gene expression data. A new spiking function has been proposed in the non-linear integrate and fire model and its inter spike interval is derived and used in the Wavelet Radial Basis Neural Network for the classification of gene expression data. The proposed model is termed as Spiking Wavelet Radial Basis Neural Network (SWRNN). The classification accuracy has been evaluated on various benchmark gene expression datasets. A comparative performance evaluation of the proposed model has been made with the Wavelet Radial Basis Neural Network (WRNN) and the standard available results in terms of classification accuracy. The comparison of the proposed SWRNN and WRNN has also been done in terms of execution time. It has been observed that the increase in classification accuracy for the proposed SWRNN is highly statistically significant for most of the gene expression datasets when compared with WRNN and the standard results. Thus incorporating a spiking function in an artificial neural network can make it more powerful for classification.

A simulation to analyze feature selection methods utilizing gene ontology for gene expression classification

Gene expression profile classification is a pivotal research domain assisting in the transformation from traditional to personalized medicine. A major challenge associated with gene expression data classification is the small number of samples relative to the large number of genes. To address this problem, researchers have devised various feature selection algorithms to reduce the number of genes. Recent studies have been experimenting with the use of semantic similarity between genes in Gene Ontology (GO) as a method to improve feature selection. While there are few studies that discuss how to use GO for feature selection, there is no simulation study that addresses when to use GO-based feature selection. To investigate this, we developed a novel simulation, which generates binary class datasets, where the differentially expressed genes between two classes have some underlying relationship in GO. This allows us to investigate the effects of various factors such as the relative connectedness of the underlying genes in GO, the mean magnitude of separation between differentially expressed genes denoted by δ, and the number of training samples. Our simulation results suggest that the connectedness in GO of the differentially expressed genes for a biological condition is the primary factor for determining the efficacy of GO-based feature selection. In particular, as the connectedness of differentially expressed genes increases, the classification accuracy improvement increases. To quantify this notion of connectedness, we defined a measure called Biological Condition Annotation Level BCAL(G), where G is a graph of differentially expressed genes. Our main conclusions with respect to GO-based feature selection are the following: (1) it increases classification accuracy when BCAL(G)⩾0.696; (2) it decreases classification accuracy when BCAL(G)⩽0.389; (3) it provides marginal accuracy improvement when 0.389<BCAL(G)<0.696 and δ<1; (4) as the number of genes in a biological condition increases beyond 50 and δ⩾0.7, the improvement from GO-based feature selection decreases; and (5) we recommend not using GO-based feature selection when a biological condition has less than ten genes. Our results are derived from datasets preprocessed using RMA (Robust Multi-array Average), cases where δ is between 0.3 and 2.5, and training sample sizes between 20 and 200, therefore our conclusions are limited to these specifications. Overall, this simulation is innovative and addresses the question of when SoFoCles-style feature selection should be used for classification instead of statistical-based ranking measures.