Asso approach for classifying gene expression data based on optimal features

Chapter 9 - Effective dimensionality reduction model with machine learning classification for microarray gene expression data

The purpose of this paper is to introduce transductive inference with support vector machines (TSVM) as a powerful methodology for classification of gene expression data, using training and prediction data sets. The following classification problems will be considered: determination of cancer diagnosis categories and classification of genes from the budding yeast Saccharomyces cerevisiae in functional groups. In the case of training samples, experts have already classified the samples in their respective classes. So, given each prediction sample, the purpose is to determine its corresponding class. The main aspect of TSVM is that the classification task will be implemented in just one step, improving the generalization capability of the classifier. The TSVM will be compared with the traditional inductive method (SVM) in a series of experiments concerning the two classification problems, with promising results.

Though DNA microarray technology simultaneously measures the expression levels of thousands of genes, only a few underlying gene features may account for significant data variation in gene classification problems. Selection of features from huge data set is difficult and so dimension reduction of gene expression data set is essential in order to determining important features, which play key role in predicting an outcome. Rough set theory (RST) has been used recently for dimension reduction of data, however, the existing methods are inadequate to finding minimal reduct. The paper proposes a RST based technique, applied on gene expression data for dimension reduction by obtaining single reduct in one pass. The gene expression data are discretized using linguistic terms with proper semantics and represented by fuzzy sets. The discretized values are calculated using Gaussian membership function with varied mean and standard deviation in order to eliminate the ambiguity between different linguistic terms. The genes are classified using linguistic decision attribute values based on the frequency of gene expression data. Discritization and classification of gene expression data are performed simultaneously, which significantly reduces time complexity of the procedure. Thus, the proposed framework selects the most significant samples for gene classification, resulting dimension reduction. The Proposed method produces output, which exhibits no variation with experimental microarray gene information unlike other existing methods.

In order to understand even the simplest cellular processes, we need to integrate proteomic, gene expression and other biomolecular data. To date, most computational approaches aimed at integrating proteomics and gene expression data use direct gene/protein correlation measures. However, due to post-transcriptional and translational regulations, the correspondence between the expression of a gene and its protein is complicated. We apply a multivariate statistical method, co-inertia analysis (CIA), to visualise gene and proteomic expression data stemming from the same biological samples. Principal components analysis or correspondence analysis can be used for data exploration on single datasets. CIA is then used to explore the relationships between two or more datasets. We further explore the data by projecting gene ontology (GO) information onto these plots to describe the cellular processes in action. We apply these techniques to gene expression and protein abundance data from studies of the human malarial parasite life cycle and the NCI-60 cancer cell lines. In each case, we visualise gene expression, protein abundance and GO classes in the same low dimensional projections and identify GO classes that are likely to be of biological importance.

(1) Background: Gene-expression data usually contain missing values (MVs). Numerous methods focused on how to estimate MVs have been proposed in the past few years. Recent studies show that those imputation algorithms made little difference in classification. Thus, some scholars believe that how to select the informative genes for downstream classification is more important than how to impute MVs. However, most feature-selection (FS) algorithms need beforehand imputation, and the impact of beforehand MV imputation on downstream FS performance is seldom considered. (2) Method: A modified chi-square test-based FS is introduced for gene-expression data. To deal with the challenge of a small sample size of gene-expression data, a heuristic method called recursive element aggregation is proposed in this study. Our approach can directly handle incomplete data without any imputation methods or missing-data assumptions. The most informative genes can be selected through a threshold. After that, the best-first search strategy is utilized to find optimal feature subsets for classification. (3) Results: We compare our method with several FS algorithms. Evaluation is performed on twelve original incomplete cancer gene-expression datasets. We demonstrate that MV imputation on an incomplete dataset impacts subsequent FS in terms of classification tasks. Through directly conducting FS on incomplete data, our method can avoid potential disturbances on subsequent FS procedures caused by MV imputation. An experiment on small, round blue cell tumor (SRBCT) dataset showed that our method found additional genes besides many common genes with the two compared existing methods.

Microarray technology has transformed the biotechnological research to next level in the recent years. It provides the expression levels of various genes involved in a particular disease. Prostate cancer disease turned into life threatening cancer. The genes causing this disease are identified through the classification methods. These gene expression data have problems like high dimensional with low sample size which imposes active challenges in the existing classification algorithms. Feature selection techniques are applied in order to address the dimensionality issues. . This paper aims in analyzing the feature selection methods for classification of gene expression data of Prostate and identify the significant genes that have a major influence on the disease. The three different feature selection methods such as Filters, wrappers and embedded selectors are applied before the classification process for selecting the top ranked genes. Then, the extracted top ranked genes are applied on the classification algorithms such as SVM, k-NN, Random Forest and Artificial Neural Network. After the inclusion of feature selection technique, the classification accuracy is significantly boosted even with less number of genes. Random Forest classification algorithm outperforms other classification methods. The significant genes that has the major influence in prostate cancer disease are identified such as KLK3, GFI1, CXCR2 and TNFRSF10C.

Gene expression (GE) profiles expansively revised to disclose intuition into the multifariousness of cancer furthermore to discover concealed information which provides biological knowledge for the classification of cancer. Precise cancer classification straightly through original GE profiles stays challenging on account of the intrinsic high-dimension feature along with the small magnitude of the data samples. Therefore, choosing high discriminative genes as of the GE data have turn into progressively fascinating in the bioinformatics field. This given paper gives a technique for the GE data classification utilising entropy-based graph classifier. Initially, the proposed technique evaluate the GE data's signal to noise ratio (SNR) values, additionally, selects the relevant features using krill herd (KH) optimization process. The truth is that not all features are helpful for classification, and some redundant together with the irrelevant features might even serve as outlier. To dispose the outliers, feature reduction is done with the assist of Euclidean distance. Classification is made utilising entropy-based graph classifier. The proposed process' effectiveness contrasted with the existing method concerning classifications is established from the experimental outcome.

Microarray experiments generate data sets containing valuable information on the gene expression levels of millions of genes in the form of a set of biological samples. Inference of gene regulatory networks from microarray data has become an important research area in bioinformatics. Several computational methods have been proposed to infer important relationships between transcription factors with target genes from gene expression and transcription factor data sets. The inferences from these methods are consistent with the biological literature and can help researchers design reasonable and efficient drugs to resist different diseases. This paper is a survey of different methods used for preprocessing and inferring the gene regulatory networks from a data set of gene expression.

One of the most significant research topics in bioinformatics is the classification of gene expression. Gene expression data commonly have a large number of features and a small number of samples. The gene expression data are very different from one to another, this differentiation among data and the feature's large number make the classification for gene expression data challenging. In this study, for classification we assessed the accuracy for most powerful deep learning's algorithms such as Deep Neural Network, Recurrent Neural Network, Convolutional Neural Network and improved Deep Neural Network with the preprocessing technique. The DNN was improved by adding Dropout to it by which the overfitting problem was overcame. Our results showed that the proposed improved-DNN outperforms the other algorithms among all used datasets.

BackgroundInterpretation of high-throughput gene expression data continues to require mathematical tools in data analysis that recognizes the shape of the data in high dimensions. Topological data analysis (TDA) has recently been successful in extracting robust features in several applications dealing with high dimensional constructs. In this work, we utilize some recent developments in TDA to curate gene expression data. Our work differs from the predecessors in two aspects: (1) Traditional TDA pipelines use topological signatures called barcodes to enhance feature vectors which are used for classification. In contrast, this work involves curating relevant features to obtain somewhat better representatives with the help of TDA. This representatives of the entire data facilitates better comprehension of the phenotype labels. (2) Most of the earlier works employ barcodes obtained using topological summaries as fingerprints for the data. Even though they are stable signatures, there exists no direct mapping between the data and said barcodes.ResultsThe topology relevant curated data that we obtain provides an improvement in shallow learning as well as deep learning based supervised classifications. We further show that the representative cycles we compute have an unsupervised inclination towards phenotype labels. This work thus shows that topological signatures are able to comprehend gene expression levels and classify cohorts accordingly.ConclusionsIn this work, we engender representative persistent cycles to discern the gene expression data. These cycles allow us to directly procure genes entailed in similar processes.

Today, DNA microarray technology is the most widely employed functional genomics approach in the bioinformatics domain. Disclosing the patterns masked in gene expression data provides an opportunity for an enhanced comprehension of functional genomics. But, the large number of genes and the intricate biological networks has greatly increased the challenges of comprehending and interpreting the resulting mass of data, which often contains millions of measurements. So, clustering techniques are used to tackle this challenge. Several clustering techniques have been developed and used in the analysis of microarray data. But, such techniques are not much efficient in analyzing the gene expression data. In this paper, we propose a novel efficient hybrid clustering approach based on quad tree data structure. In the hierarchical clustering process, the creation of the closest pair data structure in each level is an important time factor that provides the processing time of clustering. Here, a quad tree based data structure is used for identifying the closest pair elements. Thus, the proposed approach reduces the processing time successfully and offers a better analysis of gene expression data.

Chapter 14 - Review on hybrid feature selection and classification of microarray gene expression data

Based on benchmarking various methods and analyzing multiple real scRNA-seq datasets, a computational platform/workflow and a set of tips for best practices are developed for analyzing causal interactions or relationships in single cells.

Classification 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 the unlabeled data. Furthermore, gene 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 were developed recently, such as Naive Hubness Bayesian k-Nearest Neighbor (NHBNN). In this paper, we propose a semi-supervised extension of NHBNN and show in experiments on publicly available gene expression data that the proposed classifier outperforms all its examined competitors.

In recent years, many studies have shown that microarray gene expression data is useful for disease identification and cancer classification. However, since gene expression data may contain thousands of genes simultaneously, successful microarray classification can be rather difficult. Feature (gene) selection is a frequently used pre-processing technology for successful classification of microarray gene expression data. Selecting a useful gene subset as a classifier not only decreases the computational time and cost, but also increases the classification accuracy. It is therefore imperative to extract only a small number of genes, which are exclusively relevant for the classification of a particular cancer/disease type. In this paper, correlation-based binary particle swarm optimizations is proposed to select the relevant genes, and a K-nearest neighbor with the leave-one-out cross-validation method serves as a classifier to evaluate the classification performance on six published cancer classification data sets. The experimental results show that the proposed method selects fewer gene subsets, while still resulting in higher prediction accuracy than the other literature methods.