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GENETIC ALGORITHMS AND SILHOUETTE MEASURES APPLIED TO MICROARRAY DATA CLASSIFICATION

TSUN-CHEN LIN, RU-SHENG LIU, SHU-YUAN CHEN, CHEN-CHUNG LIU, CHIEH-YU CHEN
2005 Proceedings of the 3rd Asia-Pacific Bioinformatics Conference  
In this paper, we apply the genetic algorithm and the silhouette statistic in conjunction with several distance functions to the problem of multi-class prediction.  ...  Microarray technology allows large-scale parallel measurements of the expression of many thousands genes and thus aiding in the development of efficient cancer diagnosis and classification platforms.  ...  Genetic Algorithms To classify samples using microarrays, it is necessary to decide which genes should be included to form the sample vector (predictor set).  ... 
doi:10.1142/9781860947322_0023 fatcat:ij4kdakonjbjfepwqhpulhlsz4

Comparative Analysis of Genomic Signal Processing for Microarray Data Clustering

R. S. H. Istepanian, A. Sungoor, J-C Nebel
2011 IEEE Transactions on Nanobioscience  
and Linear Genetic Programs (LGP) to classify microarray data.  ...  Genomic signal processing (GSP) is a new area of research that applies and develops advanced digital signal processing methodologies for genetic data processing and visualization [3] .  ... 
doi:10.1109/tnb.2011.2178262 pmid:22157075 fatcat:vbc76jetwbax3ntd4zazuk6hee

Microarray Breast Cancer Data Clustering Using Map Reduce Based K-Means Algorithm

Hymavathi Thottathyl, Kanadam Karteeka Pavan, Rajeev Priyatam Panchadula
2020 Revue d'intelligence artificielle : Revue des Sciences et Technologies de l'Information  
In this article, we examined microarray data for breast cancer with the k-means clustering algorithm, but it was hard to scale and process a large number of micro-array data alone.  ...  To this end, we use a chart to minimize the paradigm for evaluating microarray data on breast cancer.  ...  The genetic algorithm is applied for 3-foldover-validation, offering a clustering precision of 97.7 percent that is precise relative to current algorithms.  ... 
doi:10.18280/ria.340610 fatcat:jvcblk4iprhv3h2yqgb6q4g2tq

Automatic Clustering Using a Synergy of Genetic Algorithm and Multi-objective Differential Evolution [chapter]

Debarati Kundu, Kaushik Suresh, Sayan Ghosh, Swagatam Das, Ajith Abraham, Youakim Badr
2009 Lecture Notes in Computer Science  
This paper applies the Differential Evolution (DE) and Genetic Algorithm (GA) to the task of automatic fuzzy clustering in a Multi-objective Optimization (MO) framework.  ...  It compares the performance a hybrid of the GA and DE (GADE) algorithms over the fuzzy clustering problem, where two conflicting fuzzy validity indices are simultaneously optimized.  ...  Significance and Validation of Microarray Data Clustering Results In this section the best clustering solution provided by different algorithms on the sporulation data of yeast has been visualized using  ... 
doi:10.1007/978-3-642-02319-4_21 fatcat:6xkrm4w37jgy3mizapzwmrcq4e

Genetic Algorithms Applied to Multi-Class Clustering for Gene Expression Data

Haiyan Pan, Jun Zhu, Danfu Han
2003 Genomics, Proteomics & Bioinformatics  
The performance of HGACLUS and other methods was compared by using simulated data and open microarray gene-expression datasets.  ...  A hybrid GA (genetic algorithm)-based clustering (HGACLUS) schema, combining merits of the Simulated Annealing, was described for finding an optimal or near-optimal set of medoids.  ...  Microarray experiments are increasingly being carried out in biological and medical researches to address a wide range of problems, including the classification of tumors (1) (2) (3) (4) (5) (6) (7) .  ... 
doi:10.1016/s1672-0229(03)01033-7 pmid:15629056 pmcid:PMC5172428 fatcat:umvi4dxgqzexbevwxmx4b3xqym

Algorithms for large-scale genotyping microarrays

W.-m. Liu, X. Di, G. Yang, H. Matsuzaki, J. Huang, R. Mei, T. B. Ryder, T. A. Webster, S. Dong, G. Liu, K. W. Jones, G. C. Kennedy (+1 others)
2003 Bioinformatics  
We apply our algorithms to several different genotyping microarrays. We use reference types, informative Mendelian relationship in families, and leave-one-out cross validation to verify our results.  ...  We use the average silhouette width, separation and other quantities as quality measures for genotyping classification.  ...  Jane Zhang for helpful discussion or providing data.  ... 
doi:10.1093/bioinformatics/btg332 pmid:14668223 fatcat:zi6oaf7hjreq3mxb2ziac2f3ue

An Evolutionary and Visual Framework for Clustering of DNA Microarray Data

José A. Castellanos-Garzón, Fernando Díaz
2013 Journal of Integrative Bioinformatics  
SummaryThis paper presents a case study to show the competence of our evolutionary and visual framework for cluster analysis of DNA microarray data.  ...  The results of the genetic algorithm for clustering have shown that it can find better solutions than the other methods for the selected data set.  ...  Acknowledgements This work has partially been funded by the Spanish Ministry of Science and Innovation, the Plan E from the Spanish Government, the European Union from the ERDF (TIN2009-14057-C03-02).  ... 
doi:10.1515/jib-2013-232 fatcat:ulfiiobelvbn5pwmfolk3nj7fa

An evolutionary and visual framework for clustering of DNA microarray data

José A Castellanos-Garzón, Fernando Díaz
2013 Journal of Integrative Bioinformatics  
This paper presents a case study to show the competence of our evolutionary and visual framework for cluster analysis of DNA microarray data.  ...  The results of the genetic algorithm for clustering have shown that it can find better solutions than the other methods for the selected data set.  ...  Acknowledgements This work has partially been funded by the Spanish Ministry of Science and Innovation, the Plan E from the Spanish Government, the European Union from the ERDF (TIN2009-14057-C03-02).  ... 
doi:10.2390/biecoll-jib-2013-232 pmid:24231146 fatcat:cucj4znk6jbh7d36ubouomqu5m

Combining Pareto-optimal clusters using supervised learning for identifying co-expressed genes

Ujjwal Maulik, Anirban Mukhopadhyay, Sanghamitra Bandyopadhyay
2009 BMC Bioinformatics  
Using microarray data sets, clustering algorithms have been actively utilized in order to identify groups of co-expressed genes.  ...  This indicates that the proposed clustering method can be used efficiently to identify co-expressed genes in microarray gene expression data.  ...  In this regard, a fuzzy majority voting technique followed by SVM classification is applied on the resultant 3 set of non-dominated solutions in order to obtain the final solution.  ... 
doi:10.1186/1471-2105-10-27 pmid:19154590 pmcid:PMC2657792 fatcat:jtsay2sn4vfpdk6y62wgqfrivm

A multi-array multi-SNP genotyping algorithm for Affymetrix SNP microarrays

Yuanyuan Xiao, Mark R. Segal, Y.H. Yang, Ru-Fang Yeh
2007 Computer applications in the biosciences : CABIOS  
The sensitive and high-throughput nature of hybridization-based DNA microarray technology provides an ideal platform for such an application by interrogating up to hundreds of thousands of single nucleotide  ...  The sensitive and high-throughput nature of hybridization-based DNA microarray technology provides an ideal platform for such an application by interrogating up to hundreds of thousands of single nucleotide  ...  ACKNOWLEDGEMENTS We thank CBMB, UCSF for funding support and Dr Joseph Wiemels for providing Nsp I arrays. Conflict of Interest: none declared.  ... 
doi:10.1093/bioinformatics/btm131 pmid:17459966 fatcat:6garqzmpobhhxou6ss4nnqb3wm

Effective Clustering Algorithms for Gene Expression Data [article]

T. Chandrasekhar, K. Thangavel, E. Elayaraja
2012 arXiv   pre-print
Experimental analysis shows that the proposed method performs well on gene Expression Data when compare with the traditional K- Means clustering and Silhouette Coefficients cluster measure.  ...  Identification of co-expressed genes and coherent patterns is the central goal in microarray or gene expression data analysis and is an important task in Bioinformatics research.  ...  Both the algorithms were tested with gene expression data and analysis the performance of cluster values using silhouette measurement.  ... 
arXiv:1201.4914v1 fatcat:nsgrkay2nrckrd2uf5euc5v5km

Algorithm for automatic genotype calling of single nucleotide polymorphisms using the full course of TaqMan real-time data

A. Callegaro
2006 Nucleic Acids Research  
Here, we describe the algorithm and test its validity, compared to the standard end-point method and to DNA sequencing.  ...  Best cycle genotyping algorithm (BCGA), written in the open source language R, is based on the assumptions that classification depends on the time (cycle) of amplification and that it is possible to identify  ...  Funding to pay the Open Access publication charges for this article was provided by FIRB RBNE01TZZ8.  ... 
doi:10.1093/nar/gkl185 pmid:16617143 pmcid:PMC1440877 fatcat:3igao76mqnc4zian52zukayrty

Identification of sepsis subtypes in critically ill adults using gene expression profiling

David M Maslove, Benjamin M Tang, Anthony S McLean
2012 Critical Care  
We used microarray-based gene expression data from adult patients with sepsis in order to identify molecularly distinct sepsis subtypes.  ...  There were differences between subtypes in the expression of pharmacogenes related to hydrocortisone, vasopressin, norepinephrine, and drotrecogin alpha.  ...  Lastly, we note that clustering algorithms applied to microarray data must be used with caution, as these will invariably identify clusters [36] .  ... 
doi:10.1186/cc11667 pmid:23036193 pmcid:PMC3682285 fatcat:lfzpqh3vvfd5dpkcw32i5hucly

Partitioning of functional gene expression data using principal points

Jaehee Kim, Haseong Kim
2017 BMC Bioinformatics  
DNA microarrays offer motivation and hope for the simultaneous study of variations in multiple genes.  ...  As real data applications, we are able to find partitioned genes through the gene expressions found in budding yeast data and Escherichia coli data.  ...  Availability of data and materials The datasets generated and analysed during the current study are available from the corresponding author on reasonable request.  ... 
doi:10.1186/s12859-017-1860-0 pmid:29025390 pmcid:PMC5639779 fatcat:xhaa2kljvrbytbqt53vh4jilmq

Prediction of core cancer genes using a hybrid of feature selection and machine learning methods

Y.X. Liu, N.N. Zhang, Y. He, L.J. Lun
2015 Genetics and Molecular Research  
Machine learning techniques are of great importance in the analysis of microarray expression data, and provide a systematic and promising way to predict core cancer genes.  ...  First feature filtering algorithms were applied to select a set of top-ranked genes, and then hierarchical clustering and collapsing dense clusters were used to select core cancer genes.  ...  Data preprocess and representation Data preprocessing is very important for microarray data analysis.  ... 
doi:10.4238/2015.august.3.10 pmid:26345818 fatcat:og6w5sx32zhhndglq7ezemwm4q
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