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On the classification of microarray gene-expression data

K. E. Basford, G. J. McLachlan, S. I. Rathnayake
2012 Briefings in Bioinformatics  
We consider the classification of microarray gene-expression data.  ...  (that is, the genes, which can number in the tens of thousands).  ...  We also consider unsupervised classification or clustering of the microarray data.  ... 
doi:10.1093/bib/bbs056 pmid:22988257 fatcat:hzlwy6nnqfaajbqtkm5laesp6u

Unsupervised reduction of the dimensionality followed by supervised learning with a perceptron improves the classification of conditions in DNA microarray gene expression data

L. Conde, A. Mateos, J. Herrero, J. Dopazo
Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing  
This way of reducing the dimensionality of the data set seems to perform better than other ones previously proposed such as PCA.  ...  Firstly the dimensionality of the dataset of gene expression profiles is reduced to a number of non-redundant clusters of co-expressing genes using an unsupervised clustering algorithm, the Self Organizing  ...  INTRODUCTION DNA microarray technology opens up the possibility of measuring the expression level of thousands of genes in a single experiment [3] .  ... 
doi:10.1109/nnsp.2002.1030019 dblp:conf/nnsp/CondeMHD02 fatcat:bdfcs4k4lrearholyhv5qvysye

Combined Gene Selection Methods for Microarray Data Analysis [chapter]

Hong Hu, Jiuyong Li, Hua Wang, Grant Daggard
2006 Lecture Notes in Computer Science  
The study suggests that the combination of filter and wrapper methods in general improve the accuracy performance of gene expression Microarray data classification.  ...  In recent years, the rapid development of DNA Microarray technology has made it possible for scientists to monitor the expression level of thousands of genes in a single experiment.  ...  The primary purpose of gene expression Microarray classification is to build a classifier from the categorized historical gene expression Microarray data, and then use the classifier to categorize future  ... 
doi:10.1007/11892960_117 fatcat:66lolr4kvrbhtcsaot3emhk2iu

On the Effectiveness of Gene Selection for Microarray Classification Methods [chapter]

Zhongwei Zhang, Jiuyong Li, Hong Hu, Hong Zhou
2010 Lecture Notes in Computer Science  
An approach to improving the accuracy and efficiency of Microarray data classification is to make a small selection from the large volume of high dimensional gene expression dataset.  ...  We have conducted some experiments on the effectiveness of gene selection for Microarray classification methods such as two benchmark algorithms: SVMs and C4.5.  ...  In the preceding section, we identify problems in gene expression Microarray data classification and highlight the importance of gene selection for gene expression Microarray data.  ... 
doi:10.1007/978-3-642-12101-2_31 fatcat:xspprs52zraebprw6qvzdobfru

Cross-platform analysis of cancer microarray data improves gene expression based classification of phenotypes

Patrick Warnat, Roland Eils, Benedikt Brors
2005 BMC Bioinformatics  
Cross-platform classification of multiple cancer microarray data sets yields discriminative gene expression signatures that are found and validated on a large number of microarray samples, generated by  ...  To exemplify the potential of this cross-platform classification analysis, we use two leukemia microarray data sets to show that important genes with regard to the biology of leukemia are selected in an  ...  Acknowledgements The authors acknowledge financial support by the BMBF (BioFuture; 0311880A), and the National Genome Research Network (01 GR 0450).  ... 
doi:10.1186/1471-2105-6-265 pmid:16271137 pmcid:PMC1312314 fatcat:tjabi22gufb6xer53vh35cyz6e

On Combining Multiple Microarray Studies for Improved Rinctional Classification by Whole-Dataset Feature Selection

See-Kiong Ng, Soon-Heng Tan, V.S. Sundararajan
2003 Genome Informatics Series  
We show that the functional classification of genes from microarray data can be improved further by combining gene expression data from multiple microarray studies, even if the experimental focus or conditions  ...  As microarray technologies become routinely applied in genome laboratories for studying gene expression, it is not uncommon that experiments on identical or similar sets of genes are conducted by multiple  ...  Microarray data from six different gene expression studies on Saccharomyces cerevisiae were selected for our evaluation of gene functional classification.  ... 
doi:10.11234/gi1990.14.44 fatcat:vguftwvlynewdcogxi6pzme2uy

The Performance of Bio-Inspired Evolutionary Gene Selection Methods for Cancer Classification Using Microarray Dataset

Hala M. Alshamlan, Ghada H. Badr, Yousef A. Alohali
2014 International Journal of Bioscience Biochemistry and Bioinformatics  
However, most of them did not concern on identifying minimum number of informative genes with high classification accuracy.  ...  Therefore, in our research study we discuss the performance of Bio-Inspired evolutionary gene selection method in cancer classification using microarray dataset.  ...  The Manuscript accuracy of microarray dataset classification depends on both the quality of the provided microarray data and the utilized classification method.  ... 
doi:10.7763/ijbbb.2014.v4.332 fatcat:msd7sqjcsrc4rozf5mdsng6tyu

A Comparative Analysis of Hybrid Approach for Gene Cancer Classification using Genetic Algorithm and FFBNN with Classifiers ANFIS and Fuzzy NN

Vaishali P Khobragade
2012 IOSR Journal of Engineering  
With the advent of microarray technology Many algorithms and techniques were developed for the microarray gene classification process.  ...  The methods that are used for microarray gene classification process are GA with FFBNN, GA with Fuzzy NN, GA with ANFIS.The microarray gene expression dataset dimension is reduced by GA, and then the selected  ...  One significant application of gene expression microarray data is the classification of biological samples or prophecy of clinical and other outcomes [3] .  ... 
doi:10.9790/3021-021134452 fatcat:ggd4kcckgrf5jidqt6kr5ectta

Review On Feature Selection Techniques And The Impact Of Svm For Cancer Classification Using Gene Expression Profile

Victo Sudha George, Cyril Raj
2011 International Journal of Computer Science & Engineering Survey  
The DNA microarray technology has modernized the approach of biology research in such a way that scientists can now measure the expression levels of thousands of genes simultaneously in a single experiment  ...  But compared to the number of genes involved, available training data sets generally have a fairly small sample size for classification.  ...  Cancer classification using gene expression data is a nontrivial task due to the very nature of the gene expression data.  ... 
doi:10.5121/ijcses.2011.2302 fatcat:q2m646wiq5hxjdanq3aa6qpvr4

A Comparative Study of Statistical and Artificial Intelligence based Classification Algorithms on Central Nervous System Cancer Microarray Gene Expression Data

Mustafa Turan Arslan, Adem Kalinli
2016 International Journal of Intelligent Systems and Applications in Engineering  
A variety of methods are used in order to classify cancer gene expression profiles based on microarray data.  ...  To reduce dimension of DNA microarray gene expression has been used Correlation-based Feature Selection (CFS) technique.  ...  Acknowledgements This work was supported by the Research Fund of Erciyes University of Turkey, grant number: FYL-2015-6095.  ... 
doi:10.18201/ijisae.267094 fatcat:t4nktpab3fdh3elxyvxb22agh4

A Survey on Probabilistic Computational Model for Microarray Data Classification

Barnali Sahu, Ishara Priyadarsani
2017 International Journal of Advanced Research in Computer Science and Software Engineering  
Probabilistic classifiers have received relatively little attention in the literature of less number of sample sizes and a large number of gene sizes in microarray data and microarray data exhibit a high  ...  SUMMARY This survey presented the different approaches of the probabilistic classifier to solve the problem of microarray data classification.  ...  Based on the study of gene expression microarray data appropriate probabilistic classification is much more complicated than deterministic classification [24] .  ... 
doi:10.23956/ijarcsse/sv7i5/0221 fatcat:77ctmx7xg5hqhl5ca55ztleadm

Microarray Data Analysis and Mining Tools

Saravanakumar Selvaraj, Jeyakumar Natarajan
2011 Bioinformation  
Using microarrays one can analyze the expression of many genes in a single reaction quickly and in an efficient manner.  ...  First, we report the common data mining applications such as selecting differentially expressed genes, clustering, and classification.  ...  Knowledge Discovery with Microarray Data: Classification, clustering and identification of differential genes can be considered as basic microarray data analysis tasks with gene expression profiles alone  ... 
doi:10.6026/97320630006095 pmid:21584183 pmcid:PMC3089881 fatcat:ux4yryjurvgnno2gsv4z5jcqju

Machine Learning Based Approaches For Cancer Classification Using Gene Expression Data

Amit Bhola, Arvind Kumar Tiwari
2018 Zenodo  
By using this abundance of gene expression data researchers are exploring the possibilities of cancer classification.  ...  The recent development of DNA microarray technology has made monitoring of thousands of gene expression simultaneously.  ...  The recent development of microarray technology has motivated the simultaneous monitoring of genes and cancer classification using gene expression data [2, 3, 4, 5, 6] .  ... 
doi:10.5281/zenodo.1207823 fatcat:oxyrmtuqyfa67ezh7p3fdouziu

A Study on Computational Process in Gene Expression Data

Monica Sushil Cynthia E., VairaprakashGurusamy, kannan S.
2017 International Journal of Engineering and Technology  
The major goal of this survey is focused on various techniques of data mining for developing a prediction model for disease susceptibility using Gene Expression Data.The microarray data is pre-processed  ...  of gene expression, analysis of the expression, Pattern Recognition, and Identification.  ...  Introduction DNA microarrays propose the capability to appear at the expression of thousands of genes in a particularresearch one of the significantrelevance of microarray knowledge is disease identification  ... 
doi:10.21817/ijet/2017/v9i4/170904018 fatcat:wei2xzdapfaatlbldrypqiyd6a

Technical differences between sequencing and microarray platforms impact transcriptomic subtyping of colorectal cancer

Ina A. Eilertsen, Seyed H. Moosavi, Jonas M. Strømme, Arild Nesbakken, Bjarne Johannessen, Ragnhild A. Lothe, Anita Sveen
2019 Cancer Letters  
expressed genes (<1 FPKM), as well as over-saturation of highly expressed genes on microarrays.  ...  Subtypes defined largely by genes expressed at low levels, including the CRIS-D subtype and the estimated level of tumor-infiltrating cytotoxic lymphocytes, had a weaker correspondence in classification  ...  Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.canlet.2019.10.040.  ... 
doi:10.1016/j.canlet.2019.10.040 pmid:31678167 fatcat:62i2pqudsbdadisjw2nmu4auc4
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