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4th Annual Ohio Collaborative Conference on Bioinformatics

作者： Boczko, Erik M. Xie, Minhui Wu, Di Young, Todd Ohio Univ Dept Math Athens OH 45701 USA Vanderbilt Univ Med Ctr Dept Biomed Informat Nashville TN 37235 USA Vanderbilt Univ Dept EE & CS Nashville TN USA

We compare methods based on the Signed Distance Function (SDF) a new tool for binary classification with standard Support Vector Machine (SVM) methods. We demonstrate on several sets of micro-array data that the perfo... 详细信息

ISBN: (纸本)9780769536859

关键词： Machine Learning microarray data

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Binary Classification Based on Potential Functions

Binary Classification Based on Potential Functions

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4th Annual Ohio Collaborative Conference on Bioinformatics

作者： Boczko, Erik M. Di Lullo, Andrew Young, Todd R. Vanderbilt Univ Med Ctr Nashville TN 37235 USA Ohio Univ Dept Phys Athens OH 45701 USA Ohio Univ Dept Math Athens OH 45701 USA

We introduce a simple and computationally trivial method for binary classification based on the evaluation of potential functions. We demonstrate that despite the conceptual and computational simplicity of the method ... 详细信息

ISBN: (纸本)9780769536859

关键词： Machine Learning microarray data

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Improving Accuracy of Discovered Knowledge through Direct Interaction and Cohesion-based Framework: A Study in Cell Cycle data of Yeast

Improving Accuracy of Discovered Knowledge through Direct In...

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7th International Conference on Advances in Pattern Recognition

作者： Bhattacharyya, Ramkishore Microsoft India R&D Pvt Ltd Hyderabad 500032 Andhra Pradesh India

ISBN: (纸本)9780769535203

Association mining tasks, when put to microarray data, normal trend is to highlight amount of discovered knowledge while quality analysis goes to backseat. Ideally, two more information is equally important: a) accuracy of knowledge extracted in a rule with respect to known biological functions, and b) predictability of biological interactions from discovered rules. Most of the support and/or confidence-based techniques address only predictability or neither of them. It requires tedious post-processing to unearth the actually interesting ones from the bulky output set. In the present work, we exploit the notion of direct interaction (DI) and cohesion to develop a sound methodology for binding genes under common affinity groups and mine intra-group associations. To evaluate soundness, we apply the method in cell cycle data of yeast and analyze result with the help of known biological interactions in BM. We found impressive values for both accuracy and predictability.

关键词： accuracy association mining cohesion direct interaction microarray data predictability

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An Improved Binary Particle Swarm Optimisation for Gene Selection in Classifying Cancer Classes

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10th International Work-Conference on Artificial Neural Networks (IWANN 2009)

作者： Mohamad, Mohd Saberi Omatu, Sigeru Deris, Safaai Yoshioka, Michifumi Zainal, Anazida Osaka Prefecture Univ Dept Comp Sci & Intelligent Syst Grad Sch Engn Osaka 5998531 Japan Univ Teknol Malaysia Fac Comp Sci & Informat Syst Dept Software Engn Skudai 81310 Malaysia

ISBN: (纸本)9783642024801

The application of microarray data for cancer classification has recently gained in popularity. The main problem that needs to be addressed is the selection of a smaller subset of genes from the thousands of genes in the data that contributes to a disease. This selection process is difficult because of the availability of the small number of samples compared to the huge number of genes, many irrelevant genes, and noisy genes. Therefore, this paper proposes an improved binary particle swarm optimisation to select a near-optimal (smaller) subset of informative genes that is relevant for cancer classification. Experimental results show that the performance of the proposed method is superior to a standard version of particle swarm optimisation and other related previous works in terms of classification accuracy and the number of selected genes.

关键词： Gene selection hybrid approach microarray data particle swarm optimisation

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Searching for Glycomics Role in Stem Cell Development

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5th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics

作者： Sorathiya, Anil Jucikas, Tadas Piecewicz, Stephanie Sengupta, Shiladitya Lio, Pietro Univ Cambridge Comp Lab 15 JJ Thomson Ave Cambridge CB3 0FD England Univ Cambridge DAMTP Cambridge CB30WA England MIT Cambridge MA 02139 USA

ISBN: (纸本)9783642025037

We present a methodology to analyze zebrafish knock-out experiment replicated tune series microarray data. The knock-out experiment aimed to elucidate the transcriptomal regulators underlying the glycocalyx-regulation of vasculogenesis by performing global gene expression analysis of NDST mutants and wild-type siblings at three distinct tune points during development. Cluster analysis and the construction of a genetic interaction network allows to identify groups of genes acting ill the process of early stage vasculogenesis. We report;the following findings: we found a large number of gene clusters, particularly glycans, during the three developmental steps of the zebrafish organism. In each step;genes connectivity changes according to two different powerlaws. The clusters are highlighted ill such a way;that it is possible to see the dynamics of the interactions through the time points recorded ill tire microarray experiment. Vegf-related genes seem riot to be involved at transcriptomics level, suggesting alternative regulative pathways do exist, to modulate transcriptomal signatures ill developing zebrafish. Our results show that, there are several glycan-related genies which may be involved in early processes such as vasculogenesis.

关键词： Vasculogenesis zebrafish complex network microarray data

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A CYCLIC HYBRID METHOD TO SELECT A SMALLER SUBSET OF INFORMATIVE GENES FOR CANCER CLASSIFICATION

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INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL 2009年第8期5卷 2189-2202页

作者： Mohamad, Mohd Saberi Omatu, Sigeru Yoshioka, Michifumi Deris, Safaai Osaka Prefecture Univ Grad Sch Engn Dept Comp Sci & Intelligent Syst Osaka 5998531 Japan Univ Teknol Malaysia Fac Comp Sci & Informat Syst Dept Software Engn Skudai 81310 Johore Malaysia

microarray data are expected to be useful for cancer classification. The main problem that needs to be addressed is the selection of a smaller subset of genes from the thousands of genes in the data that contributes to a cancer disease. This selection process is difficult due to many irrelevant genes, noisy data, and the availability of the small number of samples compared to the huge number of genes (higher-dimensional data). Hence, this paper aims to select a smaller subset of informative genes that is the most relevant for the cancer classification. To achieve the aim, a cyclic hybrid method has been proposed. Five real microarray data sets are used to test the effectiveness of the method. Experimental results show that the performance of the proposed method is superior to other experimental methods and related previous works in terms of classification accuracy and the number of selected genes. In addition, a scatter gene graph and a list of informative genes in the best gene subsets are also presented for biological usage.

关键词： Cancer classification Gene selection Genetic algorithm Cyclic hybrid method microarray data

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EDGE³: A web-based solution for management and analysis of Agilent two color microarray experiments

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BMC BIOINFORMATICS 2009年第1期10卷 280-280页

作者： Vollrath, Aaron L. Smith, Adam A. Craven, Mark Bradfield, Christopher A. Univ Wisconsin McArdle Lab Canc Res Sch Med & Publ Hlth Madison WI 53706 USA Univ Wisconsin Dept Biostat & Med Informat Madison WI 53706 USA Univ Wisconsin Dept Comp Sci Madison WI 53706 USA

Background: The ability to generate transcriptional data on the scale of entire genomes has been a boon both in the improvement of biological understanding and in the amount of data generated. The latter, the amount of data generated, has implications when it comes to effective storage, analysis and sharing of these data. A number of software tools have been developed to store, analyze, and share microarray data. However, a majority of these tools do not offer all of these features nor do they specifically target the commonly used two color Agilent DNA microarray platform. Thus, the motivating factor for the development of EDGE(3) was to incorporate the storage, analysis and sharing of microarray data in a manner that would provide a means for research groups to collaborate on Agilent-based microarray experiments without a large investment in software-related expenditures or extensive training of end-users. Results: EDGE(3) has been developed with two major functions in mind. The first function is to provide a workflow process for the generation of microarray data by a research laboratory or a microarray facility. The second is to store, analyze, and share microarray data in a manner that doesn't require complicated software. To satisfy the first function, EDGE(3) has been developed as a means to establish a well defined experimental workflow and information system for microarray generation. To satisfy the second function, the software application utilized as the user interface of EDGE(3) is a web browser. Within the web browser, a user is able to access the entire functionality, including, but not limited to, the ability to perform a number of bioinformatics based analyses, collaborate between research groups through a user-based security model, and access to the raw data files and quality control files generated by the software used to extract the signals from an array image. Conclusion: Here, we present EDGE(3), an open-source, web-based application that allows f

关键词： microarray data Query Object Agilent Feature Extraction Software data Analysis Section Array Object

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An Efficient Statistical Model Based Classification Algorithm for Classifying Cancer Gene Expression data with Minimal Gene Subsets

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International Journal of Cyber Society and Education 2009年第2期2卷 51-66页

作者： Mallika Rangasamy Saravanan Venketraman Sri Ramakrishna College of Arts and Science for Women Karunya School of Science and Humanities Karunya University

data mining algorithms are extensively used to classify gene expression data, in which prediction of disease plays a vital role. This paper aims to develop a new classification algorithm for cancer gene expression data using minimal number of gene combinations i. e. minimum gene subsets. The model uses classical statistical technique for gene ranking and two different classifiers for gene selection and prediction. The proposed method proves the capability of producing very high accuracy with very minimum number of genes. The methodology was tried with three publicly available cancer databases and the results were compared with the earlier approaches and proven better and promising prediction strength with less computational burden. This paper also focuses on the importance of applying an efficient gene selection method prior to classification can lead to good performance and the results are proven to be the best.

关键词： microarray data Classification SVM-OAA LDA Prediction ANOVA P-values

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Inference of gene expression networks using memetic gene expression programming 09

Inference of gene expression networks using memetic gene exp...

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Proceedings of the Thirty-Second Australasian Conference on Computer Science - Volume 91

作者： Armita Zarnegar Peter Vamplew Andrew Stranieri University of Ballarat Ballarat Victoria Australia

ISBN: (纸本)9781920682729

In this paper we aim to infer a model of genetic networks from time series data of gene expression profiles by using a new gene expression programming algorithm. Gene expression networks are modelled by differential equations which represent temporal gene expression relations. Gene Expression Programming is a new extension of genetic programming. Here we combine a local search method with gene expression programming to form a memetic algorithm in order to find not only the system of differential equations but also fine tune its constant parameters. The effectiveness of the proposed method is justified by comparing its performance with that of conventional genetic programming applied to this problem in previous studies.

关键词： differential equations microarray data gene networks gene expression programming gene expression profile evolutionary algorithm

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HYBRIDISING GENETIC ALGORITHM-NEURAL NETWORK (GANN) IN MARKER GENES DETECTION

HYBRIDISING GENETIC ALGORITHM-NEURAL NETWORK (GANN) IN MARKE...

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2009 International Conference on Machine Learning and Cybernetics(2009机器学习与控制论国际会议)

作者： DONG-LING TONG School of Design Engineering and Computing Bournemouth University Poole Dorset UK

The identification of marker genes trigger the growth of mutated cells has received a significant attention from both medical and computing communities. Through the identified genes, the pathology of mutated cells can be revealed and precautions can be taken to prevent further proliferation of abnormal cells. In this paper, we propose an innovative gene identification framework based on genetic algorithms and neural networks to identify marker genes for leukaemia cancer. Our approach able to provide a sharper focus on a group of highly expressed genes in leukaemia dataset and the identified genes have been proven significant to the study of leukaemia cancer development.

关键词： Genetic algorithms neural networks microarray data gene selection fitness optimisation

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