A semi-supervised rough set and random forest approach for pattern classification of 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.

Most of the traditional gene selection approaches are borrowed from other fields such as statistics and computer science, However, they do not prioritize biologically relevant genes since the ultimate goal is to determine features that optimize model performance metrics not to build a biologically meaningful model. Therefore, there is an imminent need for new computational tools that integrate the biological knowledge about the data in the process of gene selection and machine learning. Integrative gene selection enables incorporation of biological domain knowledge from external biological resources. In this study, we propose a new computational approach named CogNet that is an integrative gene selection tool that exploits biological knowledge for grouping the genes for the computational modeling tasks of ranking and classification. In CogNet, the pathfindR serves as the biological grouping tool to allow the main algorithm to rank active-subnetwork-oriented KEGG pathway enrichment analysis results to build a biologically relevant model. CogNet provides a list of significant KEGG pathways that can classify the data with a very high accuracy. The list also provides the genes belonging to these pathways that are differentially expressed that are used as features in the classification problem. The list facilitates deep analysis and better interpretability of the role of KEGG pathways in classification of the data thus better establishing the biological relevance of these differentially expressed genes. Even though the main aim of our study is not to improve the accuracy of any existing tool, the performance of the CogNet outperforms a similar approach called maTE while obtaining similar performance compared to other similar tools including SVM-RCE. CogNet was tested on 13 gene expression datasets concerning a variety of diseases.

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.

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.

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.

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

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.

Analysis of large-scale gene expression data is a research hotspot in the field of bioinformatics, which can be used to study abnormal phenomenon in plant growth process. This paper proposes a biological knowledge integration method based on parallel clustering to select gene subsets effectively. Gene ontology is utilised to obtain the biological functional similarity, and combined with gene expression data. Parallelised affinity propagation algorithm is used to cluster data since it can not only obtain more biologically meaningful subsets, but also avoid the loss of some potential value in genes from simple gene primary selection. The algorithm is verified with four typical plant datasets and compared with other well-known integration methods. Experimental results on plant stress response datasets demonstrate that the proposed method can select genes with stronger classification ability.

Rapid technological advancements in microarray analysis continue to generate enormous amounts of genomic data. However, neither hardware nor software computational capabilities have kept pace with this drastic increase. This paper presents a novel framework designed to achieve fast, robust, and accurate (biologically-significant) multi-class classification of gene expression data using distributed knowledge discovery and computational integration routines, specifically for cancer applications. The proposed paradigm consists of the following key computational steps: (a) preprocessing normalization and discretization of gene expression data, (b) partition data using two methods: overlapped windows and adaptive selection, (c) perform association rule discovery on partitioned data-spaces using FP-growth method, (d) integrate derived association rules on distributed processor nodes using a novel knowledge integration algorithm, (e) further prune rules to reduce dimensionality using parametric significance estimation, and (f) cluster remaining rules using a novel clustering algorithm for enhanced visualization and interpretation of discovered gene rule sets.

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.

Prediction of gene function from expression profiles is an intriguing problem that has been attempted with both unsupervised clustering and supervised learning methods. By the incorporation of prior knowledge concerning gene function, supervised methods avoid some of the problems with clustering. However, even supervised methods ignore the fact that the functional classes associated with genes are typically organized in an ontology. Hence, we introduce a new supervised method for learning in such an ontology. It is tested on both an artificial data set and a data set containing measurements from human fibroblast cells. We also give an approach for measuring the classification performance in an ontology.KeywordsSupport Vector MachineGene Expression DataDirected Acyclic GraphSupervise MethodIncorrect PredictionThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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.

We introduce methods for the exploratory analysis of microarray data, especially focusing on cluster algorithms. Benefits and problems are discussed. We describe application and suitability of unsupervised learning methods for the classification of gene expression data. Cluster algorithms are treated in more detail, including assessment of cluster quality. When dealing with microarray data, most cluster algorithms must be applied with caution. As long as the structure of the true generating models of such data is not fully understood, the use of simple algorithms seems to be more appropriate than the application of complex black-box algorithms. New methods explicitly targeted to the analysis of microarray data are increasingly being developed in order to increase the amount of useful information extracted from the experiments. Unsupervised methods can be a helpful tool for the analysis of microarray data, but a critical choice of the algorithm and a careful interpretation of the results are required in order to avoid false conclusions.

To emphasize gene interactions in the classification algorithms, a new representation is proposed, comprising gene-pairs and not single genes. Each pair is represented by L1 difference in the corresponding expression values. The novel representation is evaluated on benchmark datasets and is shown to often increase classification accuracy for genetic datasets. Exploiting the gene-pair representation and the Gene Ontology (GO), the semantic similarity of gene pairs can be incorporated to pre-select pairs with a high similarity value. The GO-based feature selection approach is compared to the plain data driven selection and is shown to often increase classification accuracy.

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