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Fourth International Conference on Emerging Applications of Information Technology

作者： Mandal, Sudip Saha, Goutam Pal, Rajat Kumar GIMT ECE Dept Krishna Nagar India NEHU IT Dept Shillong Meghalaya India Univ Calcutta CSE Dept Kolkata India

ISBN: (纸本)9781479942725

Suitable analysis of microarray dataset can unlock the mystery of the origin of many dreaded disease like cancer which can subsequently be investigated for its rectification, resulting into search for drug design. A critical challenge of the post-genomic era is to find out the cancer causing genes that induce changes in gene expression profiles in the microarray dataset. Various algorithms based on SVM, data Mining Techniques, Information theory based investigations, Clustering Techniques etc. were used by previous researchers. In this paper, Rough Set Theory and Bayesian Network based techniques have been applied for the same purpose. Rough Set has been used to isolate genes from microarray dataset responsible for cervical cancer. Bayesian approach has been used for extracting the Gene Regulating Network using the isolated genes. The same has been repeated for a normal healthy person. By superimposing these two networks, it is possible to find out the distinct cellular pathway for development of cancer from the departure of directed edges of the two networks. The results obtained in this work are quite satisfactory.

关键词： Cancer diagnosis microarray data Rough set Rule reduction Gene regulatory network Bayesian network

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Random KNN 14

Random KNN

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14th IEEE International Conference on data Mining (IEEE ICDM)

作者： Li, Shengqiao Harner, E. James Adjeroh, Donald A. West Virginia Univ Dept Stat Morgantown WV 26506 USA West Virginia Univ Lane Dept Comp Sci & Elect Engn Morgantown WV 26506 USA

ISBN: (纸本)9781479942749

We present Random KNN, a novel generalization of traditional nearest-neighbor modeling. Random KNN consists of an ensemble of base k-nearest neighbor classifiers, each constructed from a random subset of the input variables. We study the properties of the proposed Random KNN. Using various datasets, we perform an empirical analysis of Random KNN performance and compare it with recently proposed methods for high-dimensional datasets. It is shown that Random KNN provides significant advantages in both the computational requirement and classification performance.

关键词： Classification Feature Selection High Dimensional data KNN microarray data Random KNN

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Evolutionary Algorithm Based on New Crossover for the Biclustering of Gene Expression data

Evolutionary Algorithm Based on New Crossover for the Biclus...

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9th IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB)

作者： Maatouk, Ons Ayadi, Wassim Bouziri, Hend Duval, Beatrice Univ Tunis ISG Tunis LARODEC Lab Tunis Tunisia Univ Angers LERIA F-49045 Angers France Univ Tunis LaTICE Lab ESSTT Tunis Tunisia

ISBN: (纸本)9783319091921;9783319091914

microarray represents a recent multidisciplinary technology. It measures the expression levels of several genes under different biological conditions, which allows to generate multiple data. These data can be analyzed through biclustering method to determinate groups of genes presenting a similar behavior under specific groups of conditions. This paper proposes a new evolutionary algorithm based on a new crossover method, dedicated to the biclustering of gene expression data. This proposed crossover method ensures the creation of new biclusters with better quality. To evaluate its performance, an experimental study was done on real microarray datasets. These experimentations show that our algorithm extracts high quality biclusters with highly correlated genes that are particularly involved in specific ontology structure.

关键词： Biclustering Evolutionary algorithm Crossover method microarray data data mining

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Toward Genomic Based Personalized Mathematical Models for Breast Cancer Tumor Growth 14

Toward Genomic Based Personalized Mathematical Models for Br...

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14th IEEE International Conference on BioInformatics and BioEngineering (BIBE)

作者： Saribudak, Aydin Ganic, Emir Zou, Jianmin Gundry, Stephen Uyar, M. Umit CUNY City Coll Dept Elect Engn New York NY 10031 USA CUNY Grad Ctr Dept Comp Sci New York NY USA

ISBN: (纸本)9781479975020

Our Genomic Relevance Parameterization (GReP) model aims to explore a possible relationship between gene expression values from breast cancer patients and mathematical tumor growth modeling parameters calculated using data from clinical and preclinical measurements. We introduce two methods to relate genomic information and the tumor growth measurements. One method explores the impact of exponentiation of gene expression values, whereas the other utilizes the correlation between co-regulated genes and the growth parameters. As inputs to our GReP model, we used patient tumor volume measurements and genomic information for 74 breast cancer related genes from the I-SPY 1 TRIAL. We performed a preliminary validation of GReP model using experimental data from literature including MDA-MB-231 cell line, MDA-MB-231 cell line with CXCL12 gene over-expressed, and the MDA-MB-231 sub-cell lines 1834 and 4175. Tumor growth curves generated by GReP model, for the initial exponential phase of tumor growth closely match the pre-clinical data reported in the literature. These promising results show that it may be possible to build tools combining clinical information and genomic data to model cancerous tumor growth.

关键词： tumor growth models gene expressions microarray data breast cancer I-SPY 1 exponential linear model

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Predicting 5-Year Survival Status of Patients with Breast Cancer based on Supervised Wavelet Method

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Osong Public Health and Research Perspectives 2014年第6期5卷 324-332页

作者： Farhadian, Maryam Mahjub, Hossein Poorolajal, Jalal Moghimbeigi, Abbas Mansoorizadeh, Muharram Department of Epidemiology and Biostatistics School of Public Health Hamadan University of Medical Sciences Hamadan Iran Research Center for Health Sciences and Department of Epidemiology and Biostatistics School of Public Health Hamadan University of Medical Sciences Hamadan Iran Modeling of Noncommunicable Diseases Research Center Department of Epidemiology and Biostatistics School of Public Health Hamadan University of Medical Sciences Hamadan Iran Modeling of Noncommunicable Disease Research Center Department of Biostatistics and Epidemiology School of Public Health Hamadan University of Medical Sciences Hamadan Iran Department of Computer Engineering Faculty of Engineering Bu-Ali Sina University Hamadan Iran

Objectives: Classification of breast cancer patients into different risk classes is very important in clinical applications. It is estimated that the advent of high-dimensional gene expression data could improve patient classification. In this study, a new method for transforming the high-dimensional gene expression data in a low-dimensional space based on wavelet transform (WT) is presented. Methods: The proposed method was applied to three publicly available microarray data sets. After dimensionality reduction using supervised wavelet, a predictive support vector machine (SVM) model was built upon the reduced dimensional space. In addition, the proposed method was compared with the supervised principal component analysis (PCA). Results: The performance of supervised wavelet and supervised PCA based on selected genes were better than the signature genes identified in the other studies. Furthermore, the supervised wavelet method generally performed better than the supervised PCA for predicting the 5-year survival status of patients with breast cancer based on microarray data. In addition, the proposed method had a relatively acceptable performance compared with the other studies. Conclusion: The results suggest the possibility of developing a new tool using wavelets for the dimension reduction of microarray data sets in the classification framework. © 2014.

关键词： Breast cancer microarray data Supervised wavelet Support vector machine

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Find Significant Gene Information Based on Changing Points of microarray data

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IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 2009年第4期56卷 1108-1116页

作者： Liu, Yihui Bai, Li Shandong Inst Light Ind Inst Intelligent Informat Proc Sch Informat Sci & Technol Jinan 250353 Peoples R China Univ Nottingham Sch Comp Sci Nottingham NG8 1BB England

For transformations, a set of new basis is normally chosen for the data. The selection of the new basis determines the properties that will be held by the transformed data. For wavelet transform, a set of wavelet basis aims to detect the localized features contained in microarray data. In this research, we investigate the performance of wavelet features based on wavelet detail coefficients at third level in wavelet space, which characterize the changing points of microarray data based on high-order information. In order to find the significant gene information, we reconstruct wavelet details based on detail coefficients. A genetic algorithm is used to select the best features from reconstructed details in original data space, and corresponding gene information is detected based on selected features. Experiments are carried out on four datasets and experimental results show that good performance is achieved based on twofold cross-validation experiments.

关键词： Features extraction feature optimization microarray data wavelet analysis

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Wavelet feature extraction for high-dimensional microarray data

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NEUROCOMPUTING 2009年第4-6期72卷 985-990页

作者： Liu, Yihui Shandong Inst Light Ind Sch Comp Sci & Informat Technol Jinan 250353 Peoples R China

It is well known that the problem arising from high dimensionality of data should be considered in pattern recognition. Original microarray data are usually of high dimensionality, whereas, only limited training samples are available. Therefore, dimensionality reduction is an important strategy to greatly improve the classification performance of microarray data. A novel method of feature extraction and dimensionality reduction for high-dimensional microarray data is proposed in this study. A set of orthogonal wavelet detail coefficients based on wavelet decomposition at different levels is extracted to characterize the localized features of microarray data. Experiments are carried out on four datasets. A highly competitive accuracy is achieved in comparison with the performance of other models. (C) 2008 Elsevier B.V. All rights reserved.

关键词： microarray data Dimensionality reduction Feature extraction Wavelet analysis

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A sequential feature extraction approach for naive bayes classification of microarray data

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EXPERT SYSTEMS WITH APPLICATIONS 2009年第6期36卷 9919-9923页

作者： Fan, Liwei Poh, Kim-Leng Zhou, Peng Natl Univ Singapore Dept Ind & Syst Engn Singapore 119260 Singapore Nanjing Univ Aeronaut & Astronaut Coll Econ & Management Nanjing 210016 Peoples R China

Accurate classification of microarray data plays a vital role in cancer prediction and diagnosis. Previous Studies have demonstrated the usefulness of naive Bayes classifier in solving various classification problems. In microarray data analysis, however, the conditional independence assumption embedded in the classifier itself and the characteristics of microarray data, e.g. the extremely high dimensionality, may severely affect the classification performance of naive Bayes classifier. This paper presents a sequential feature extraction approach for naive Bayes classification of microarray data. The proposed approach consists of feature selection by stepwise regression and feature transformation by class-conditional independent component analysis. Experimental results on five microarray datasets demonstrate the effectiveness of the proposed approach in improving the performance of naive Bayes classifier in microarray data analysis. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： microarray data Naive Bayes Feature extraction Independent component analysis (ICA) Stepwise regression

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Ensemble component selection for improving ICA based microarray data prediction models

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PATTERN RECOGNITION 2009年第7期42卷 1274-1283页

作者： Liu, Kun-Hong Li, Bo Zhang, Jun Du, Ji-Xiang Xiamen Univ Sch Software Xiamen 361005 Fujian Peoples R China Wuhan Univ Sci & Techol Sch Comp Sci Technol Wuhan 430081 Hubei Peoples R China Anhui Univ Sch Elect Sci & Technol Hefei Anhui Peoples R China Huaqiao Univ Dept Comp Sci & Technol Quanzhou 362021 Fujian Peoples R China

Independent component analysis (ICA) has been widely used to tackle the microarray dataset classification problem, but there still exists an unsolved problem that the independent component (IC) sets may not be reproducible after different ICA transformations. Inspired by the idea of ensemble feature selection, we design an ICA based ensemble learning system to fully utilize the difference among different IC sets. In this system, some IC sets are generated by different ICA transformations firstly. A multi-objective genetic algorithm (MOGA) is designed to select different biologically significant IC subsets from these IC sets, which are then applied to build base classifiers. Three schemes are used to fuse these base classifiers. The first fusion scheme is to combine all individuals in the final generation of the MOCA. In addition, in the evolution, we design a global-recording technique to record the best IC subsets of each IC set in a global-recording list. Then the IC subsets in the list are deployed to build base classifier so as to implement the second fusion scheme. Furthermore, by pruning about half of less accurate base classifiers obtained by the second scheme, a compact and more accurate ensemble system is built, which is regarded as the third fusion scheme. Three microarray datasets are used to test the ensemble systems, and the corresponding results demonstrate that these ensemble schemes can further improve the performance of the ICA based classification model, and the third fusion scheme leads to the most accurate ensemble system with the smallest ensemble size. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： microarray data Independent component analysis (ICA) Multiple classifier system (MCS) Ensemble component selection Multi-objective genetic algorithm (MOGA) Global-recording technique

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Interactive visualization of clusters in microarray data: an efficient tool for improved metabolic analysis of E. coli

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MICROBIAL CELL FACTORIES 2009年第1期8卷 1-12页

作者： Scharl, Theresa Striedner, Gerald Poetschacher, Florentina Leisch, Friedrich Bayer, Karl Univ Nat Resources & Appl Life Sci Dept Biotechnol A-1190 Vienna Austria Vienna Univ Technol Dept Stat & Probabil Theory A-1040 Vienna Austria Univ Munich Dept Stat D-80539 Munich Germany

Background: Interpretation of comprehensive DNA microarray data sets is a challenging task for biologists and process engineers where scientific assistance of statistics and bioinformatics is essential. Interdisciplinary cooperation and concerted development of software-tools for simplified and accelerated data analysis and interpretation is the key to overcome the bottleneck in data-analysis workflows. This approach is exemplified by gcExplorer an interactive visualization toolbox based on cluster analysis. Clustering is an important tool in gene expression data analysis to find groups of co-expressed genes which can finally suggest functional pathways and interactions between genes. The visualization of gene clusters gives practitioners an understanding of the cluster structure of their data and makes it easier to interpret the cluster results. Results: In this study the interactive visualization toolbox gcExplorer is applied to the interpretation of E. coli microarray data. The data sets derive from two fedbatch experiments conducted in order to investigate the impact of different induction strategies on the host metabolism and product yield. The software enables direct graphical comparison of these two experiments. The identification of potentially interesting gene candidates or functional groups is substantially accelerated and eased. Conclusion: It was shown that gcExplorer is a very helpful tool to gain a general overview of microarray experiments. Interesting gene expression patterns can easily be found, compared among different experiments and combined with information about gene function from publicly available databases.

关键词： microarray data Cluster Solution Limited Induction Cluster Centroid Neighborhood Graph

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