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EXTRACTION OF CO-BEHAVING GENES BY SIMILARITY ENSEMBLES

JOURNAL OF BIOLOGICAL SYSTEMS 2017年第3期25卷 479-494页

作者： Mohammadi-Jenghara, Moslem Ebrahimpour-Komleh, Hossein Univ Kashan Dept Comp & Elect Engn Kashan Iran

microarray technology is used as a source of data for a wide range of biology studies. Useful biological information can be extracted from the analysis of microarray data, namely, the impact of a particular gene expression on the expression of other genes or the determination of expressed genes under different conditions. The purpose of this paper is to find co-behavioral genes in different data sets for different times and conditions. In other words, genes with similar behavior, same increase or decrease, under different medical, stress, and time conditions in terms of expression are determined. Multi-valued discretization of expression data was used for extracting genes with identical behavior. The algorithm proposed in this study is based on data and methods ensemble. The data ensemble technique was used to extract candidate genes with identical behavior. Other methods were also applied on all the data sets;as a result, many co-behavioral candidate genes with different similarity and correlation values were identified. Finally, the ultimate output was created from the ensemble of different methods. By applying the algorithm on yeast gene expression data, meaningful relations among genes were extracted.

关键词： Co-Behavior Genes Gene Expression microarray data analysis microarray Discretization Gene Similarity

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An improved Binary Particle Swarm Optimization (iBPSO) for Gene Selection and Cancer Classification using DNA microarrays

An improved Binary Particle Swarm Optimization (iBPSO) for G...

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Conference on Information and Communication Technology

作者： Indu Jain Vinod Kumar Jain Renu Jain School of Mathematics and Allied Sciences (SOMAAS) Jiwaji University Computer Science and Engineering Discipline PDPM-IIITDM

ISBN: (纸本)9781538682166

DNA microarrays enable the detection of genetic changes attributable to cancer by simultaneously analyzing the expression of thousands of genes. However, the identification of most relevant genes from thousands of gene expressions available in each biological sample, for cancer classification pose a great challenge. Although researchers have applied BPSO based wrapper approaches to get most relevant genes prior to cancer classification, these approaches didn't achieve good classification accuracy due to the premature convergence caused by local stagnation problem. This paper proposes an improved Binary Particle Swarm Optimization (iBPSO) to tackle these issues. The proposed iBPSO based wrapper is examined using Naive-Bayes (NB), k-Nearest Neighbor (kNN), and Support Vector Machines (SVM) classifiers with stratified 5-fold cross-validation. The proposed iBPSO exhibited its efficacy in terms of classification accuracy and the number of selected genes in comparison to standard BPSO on six benchmark cancer microarray datasets. Our proposed iBPSO also effectively escapes from local minima stagnation.

关键词： microarray data analysis Cancer classification Improved binary particle swarm optimization (iBPSO) Gene selection

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Gene expression and protein-protein interaction data for identification of colon cancer related genes using f-information measures

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NATURAL COMPUTING 2016年第3期15卷 449-463页

作者： Paul, Sushmita Maji, Pradipta Univ Erlangen Nurnberg Dept Dermatol Lab Syst Tumor Immunol Erlangen Germany Indian Stat Inst Biomed Imaging & Bioinformat Lab Machine Intelligence Unit Kolkata 700108 India

One of the most important and challenging problems in functional genomics is how to select the disease genes. In this regard, the paper presents a new computational method to identify disease genes. It judiciously integrates the information of gene expression profiles and shortest path analysis of protein-protein interaction networks. While the -information based maximum relevance-maximum significance framework is used to select differentially expressed genes as disease genes using gene expression profiles, the functional protein association network is used to study the mechanism of diseases. An important finding is that some -information measures are shown to be effective for selecting relevant and significant genes from microarray data. Extensive experimental study on colorectal cancer establishes the fact that the genes identified by the integrated method have more colorectal cancer genes than the genes identified from the gene expression profiles alone, irrespective of any gene selection algorithm. Also, these genes have greater functional similarity with the reported colorectal cancer genes than the genes identified from the gene expression profiles alone. The enrichment analysis of the obtained genes reveals to be associated with some of the important KEGG pathways. All these results indicate that the integrated method is quite promising and may become a useful tool for identifying disease genes.

关键词： microarray data analysis Gene selection f-Information Classification Protein-protein interaction network

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Drug repositioning for non-small cell lung cancer by using machine learning algorithms and topological graph theory

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BMC BIOINFORMATICS 2016年第1期17卷 13-26页

作者： Huang, Chien-Hung Chang, Peter Mu-Hsin Hsu, Chia-Wei Huang, Chi-Ying F. Ng, Ka-Lok Natl Formosa Univ Dept Comp Sci & Informat Engn Huwei 63205 Taiwan Taipei Vet Gen Hosp Dept Med Div Hematol & Oncol Taipei 112 Taiwan Natl Yang Ming Univ Fac Med Taipei 112 Taiwan Natl Yang Ming Univ Inst Biopharmaceut Sci Taipei 112 Taiwan Asia Univ Dept Bioinformat & Med Engn Taichung 41354 Taiwan China Med Univ China Med Univ Hosp Dept Med Res Taichung 40402 Taiwan

Background: Non-small cell lung cancer (NSCLC) is one of the leading causes of death globally, and research into NSCLC has been accumulating steadily over several years. Drug repositioning is the current trend in the pharmaceutical industry for identifying potential new uses for existing drugs and accelerating the development process of drugs, as well as reducing side effects. Results: This work integrates two approaches - machine learning algorithms and topological parameter-based classification - to develop a novel pipeline of drug repositioning to analyze four lung cancer microarray datasets, enriched biological processes, potential therapeutic drugs and targeted genes for NSCLC treatments. A total of 7 (8) and 11 (12) promising drugs (targeted genes) were discovered for treating early-and late-stage NSCLC, respectively. The effectiveness of these drugs is supported by the literature, experimentally determined in-vitro IC50 and clinical trials. This work provides better drug prediction accuracy than competitive research according to IC50 measurements. Conclusions: With the novel pipeline of drug repositioning, the discovery of enriched pathways and potential drugs related to NSCLC can provide insight into the key regulators of tumorigenesis and the treatment of NSCLC. Based on the verified effectiveness of the targeted drugs predicted by this pipeline, we suggest that our drug-finding pipeline is effective for repositioning drugs.

关键词： Non-small cell lung cancer Drug repositioning microarray data analysis Machine learning algorithm Topological parameters Protein-protein interactions Enrichment analysis Connectivity Map

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Statistical discrimination of breast cancer microarray data

Statistical discrimination of breast cancer microarray data

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International Conference on Bioinformatics and Systems Biology (BSB)

作者： Kumar, Gautam Lahiri, Tapobarata Kumar, Rajnish Indian Inst Informat Technol Dept Appl Sci Allahabad 211012 Uttar Pradesh India

ISBN: (纸本)9781509022618

Recently published literatures indicate that, a consistent and precise classification system of microarray data is very essential for the successful identification of genes responsible for a cancer subtype and early treatment of cancer. However, in common clinical practice, diagnostic assertion of malignancies mostly relies on the morphological examination of tissues and clinical tests. In spite of the recent progress of treatment that uses semiempirical approaches involving computation methods, there are still uncertainties in cancer identification and diagnosis. High density oligonucleotide chips and genomic microarray data are being used progressively more in cancer research by generating huge expression data for thousands of genes simultaneously that poses problem of mining specific genes responsible for characterization of a specific cancer subtype. In this backdrop, we briefly address the impact of various statistical methods and their relative performances for the identification of potential probes and Discriminant genes for the breast cancer. We used Fisher's Discriminant Ratio (FDR), two tailed T-Test and vector norm on raw expression data and for each of the probe generated from the difference data accounting for up and down regulation of expression for each probes for various samples. The result indicates the potential of these methods to identify genes responsible for manifestation of breast cancer which is also well supported by the published result of experiments. The success of this approach not only gives the benefit of identification of cancer specific genes but also may help building of efficient classifier on the basis of these genes for automatic diagnostics of cancer.

关键词： Breast Cancer microarray data analysis Statistical classification Gene identification

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MapReduce based parallel gene selection method

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APPLIED INTELLIGENCE 2015年第2期42卷 147-156页

作者： Islam, A. K. M. Tauhidul Jeong, Byeong-Soo Bari, A. T. M. Golam Lim, Chae-Gyun Jeon, Seok-Hee Kyung Hee Univ Dept Comp Engn Seoul South Korea

microarray data analysis has been widely used for extracting relevant biological information from thousands of genes simultaneously expressed in a specific cell. Although many genes are expressed in a sample tissue, most of these are irrelevant or insignificant for clinical diagnosis or disease classification because of missing values and noises. Thus, finding a small, closely related gene set to accurately classify disease cells is an important research problem. At the same time, scalable gene selection methods are required for microarray data analysis due to rapidly increasing volume of microarray data. In this paper, we propose a scalable parallel gene selection method using the M a p R e u d c e programming model. The proposed method utilizes the kNN classifier algorithm for evaluating classification accuracy and uses four real and three synthetic datasets for experiments. Experimental results show that the proposed method can offer good scalability on large data with increasing number of nodes and it can also provide higher classification accuracy rather than using whole gene set for classification.

关键词： microarray data analysis Gene selection MapReduce

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An Evolutionary Approach for Balancing Effectiveness and Representation Level in Gene Selection

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JOURNAL OF INFORMATION TECHNOLOGY RESEARCH 2015年第2期8卷 16-33页

作者： Dessi, Nicoletta Pes, Barbara Cannas, Laura Maria Univ Cagliari Dept Math & Comp Sci Cagliari Italy

As data mining develops and expands to new application areas, feature selection also reveals various aspects to be considered. This paper underlines two aspects that seem to categorize the large body of available feature selection algorithms: the effectiveness and the representation level. The effectiveness deals with selecting the minimum set of variables that maximize the accuracy of a classifier and the representation level concerns discovering how relevant the variables are for the domain of interest. For balancing the above aspects, the paper proposes an evolutionary framework for feature selection that expresses a hybrid method, organized in layers, each of them exploits a specific model of search strategy. Extensive experiments on gene selection from DNA-microarray datasets are presented and discussed. Results indicate that the framework compares well with different hybrid methods proposed in literature as it has the capability of finding well suited subsets of informative features while improving classification accuracy.

关键词： Bioinformatics Feature Selection Genetic Algorithms High-Dimensional data microarray data analysis

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Mining potential information for Multiclass microarray data using Centroid-based Dimension Reduction

Mining potential information for Multiclass Microarray Data ...

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IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C)

作者： Guo, Shun Guo, Donghui Xiamen Univ Dept Elect Engn Fujian 361005 Peoples R China Xiamen Univ IC Design & IT Res Ctr Fujian Prov Fujian 361005 Peoples R China

ISBN: (纸本)9781467395984

In this paper, we propose a novel dimension reduction algorithm that implements an information fusion of Centroid-based feature selection and partial least squares (PLS) based feature extraction. This paper focuses on mining the potential information hidden in multiclass microarray data and interpreting the results provided by the potential information. Firstly, a centroid concept has been introduced to define the objective function of feature selection. In order to obtain the sparse solution, logistic regression with L1 regularization has been incorporated into the objective function. The Centroid-based feature selection is then proposed to solve the optimization problem. By using the One-Versus-All (OVA) techniques, the Centroid-based feature selection is extended to solve multiclass problems. Secondly, we perform feature important analysis on microarray data by Centroid-based feature selection to determine the information feature subset (biomarkers). Finally, PLS-based feature extraction is conducted on the selected feature subset to extract the features that best reflect the nature of classification. The proposed algorithm is compared with three state-of-the-art algorithms using eight multiclass microarray datasets. The experimental results demonstrate that the proposed algorithm performs effectively and is competitive. Furthermore, mining the potential information of the microarray dataset improves the interpretability of the results.

关键词： Dimension reduction Centroid L1 regularization partial least squares (PLS) microarray data analysis

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Predicting microRNA Expression from Sequence 6

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6th European Conference of the International-Federation-for-Medical-and-Biological-Engineering (MBEC)

作者： Ogul, Hasan Tuncer, M. Emre Baskent Univ Dept Comp Engn TR-06490 Ankara Turkey

ISBN: (纸本)9783319111278

Given the promoter sequence of a microRNA, we attempt to predict its expression using a regression model learnt from the expression levels of other microRNAs obtained through a microarray experiment. To our knowledge, this is the first study that evaluates the predictability of microRNA expression from sequence. The promising results encourage the use of the system as a supporting means for microarray missing data imputation or completing old experiments with new explorations.

关键词： Regression analysis Relevance Vector Machines microarray data analysis microRNA regulation. missing data imputation promoter elements

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A novel biclustering algorithm of binary microarray data: BiBinCons and BiBinAlter

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BIOdata MINING 2015年第1期8卷 38-38页

作者： Ben Saber, Haifa Elloumi, Mourad Tunis Time Univ ENSIT Latice Lab Tunis Tunisia Tunis Univ Tunis El Manar Ensit Latice Lab Tunis Tunisia

The biclustering of microarray data has been the subject of a large research. No one of the existing biclustering algorithms is perfect. The construction of biologically significant groups of biclusters for large microarray data is still a problem that requires a continuous work. Biological validation of biclusters of microarray data is one of the most important open issues. So far, there are no general guidelines in the literature on how to validate biologically extracted biclusters. In this paper, we develop two biclustering algorithms of binary microarray data, adopting the Iterative Row and Column Clustering Combination (IRCCC) approach, called BiBinCons and BiBinAlter. However, the BiBinAlter algorithm is an improvement of BiBinCons. On the other hand, BiBinAlter differs from BiBinCons by the use of the EvalStab and IndHomog evaluation functions in addition to the CroBin one (Bioinformatics 20: 1993-2003, 2004). BiBinAlter can extracts biclusters of good quality with better p-values.

关键词： Biclustering Algorithm Evaluation function microarray data analysis

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