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Fuzzy based Feature Selection Scheme through Transductive SVM Technique for Cancer Pattern Classification and Prediction

J. Suganthi, V. Malathi
2016 Indian Journal of Science and Technology  
Introduction The Cancer Classification of various tumors or cancer patterns is one of the most important Data Mining Classification Techniques for cancer pattern diagnosis and also to discover the drug  ...  Vector Machines S 3 VM and Consistency Based Feature Selection approach through Transductive Support Vector Machine (CBFS+TSVM) for Semi Supervised Classification.  ... 
doi:10.17485/ijst/2016/v9i16/87951 fatcat:riqkm2qwwveuhmwgueagbifj2a

Novel methods to identify biologically relevant genes for leukemia and prostate cancer from gene expression profiles

Austin H Chen, Yin-Wu Tsau, Ching-Heng Lin
2010 BMC Genomics  
High-throughput microarray experiments now permit researchers to screen thousands of genes simultaneously and determine the different expression levels of genes in normal or cancerous tissues.  ...  Results: In this paper, we propose two novel techniques, entitled random forest gene selection (RFGS) and support vector sampling technique (SVST).  ...  Acknowledgements The authors thank the National Science Council for their financial support regarding project NSC 98-2221-E-320-005. Author Details  ... 
doi:10.1186/1471-2164-11-274 pmid:20433712 pmcid:PMC2873479 fatcat:atlbieduvjhhpocpjzqlklit5y

Identifying Cancer Biomarkers From Microarray Data Using Feature Selection and Semisupervised Learning

Debasis Chakraborty, Ujjwal Maulik
2014 IEEE Journal of Translational Engineering in Health and Medicine  
In this paper, a combination of kernelized fuzzy rough set (KFRS) and semisupervised support vector machine (S 3 VM) is proposed for predicting cancer biomarkers from one miRNA and three gene expression  ...  Recent studies have revealed that patterns of altered microarray expression profiles in cancer can serve as molecular biomarkers for tumor diagnosis, prognosis of diseasespecific outcomes, and prediction  ...  Hence the development of suitable machine learning techniques for finding onco-miRNAs that target onco-genes is an important task that could provide alternate ways of diagnosis and therapy of the diseases  ... 
doi:10.1109/jtehm.2014.2375820 pmid:27170887 pmcid:PMC4848046 fatcat:exqtnapsifcb7pla7gclygwfgm

Examining the Classification Accuracy of TSVMs with ?Feature Selection in Comparison with the GLAD Algorithm [article]

Hala Helmi, Jon M. Garibaldi, Uwe Aickelin
2013 arXiv   pre-print
classification of microarray data.  ...  Therefore, in this paper, we suggest Transductive Support Vector Machines (TSVMs) as semi-supervised learning algorithms, learning with both labelled samples data and unlabelled samples to perform the  ...  Support Vector Machines Support Vector Machine (SVMs), as a supervised machine learning technique, perform well in several areas of biological research, including evaluating microarray expression data  ... 
arXiv:1307.1387v1 fatcat:4thjbqlbjjgm7ox337qdj5wxmu

Gene Expression Signature and the Prediction of Ulcerative Colitis–Associated Colorectal Cancer by DNA Microarray

Toshiaki Watanabe, Takashi Kobunai, Etsuko Toda, Takamitsu Kanazawa, Yoshihiro Kazama, Junichiro Tanaka, Toshiaki Tanaka, Yoko Yamamoto, Keisuke Hata, Tetsu Kojima, Tadashi Yokoyama, Tsuyoshi Konishi (+7 others)
2007 Clinical Cancer Research  
Using the k-nearest neighbor method and the support vector machine, we could predict the development of UC-associated neoplasms with an accuracy of 86.8% and 98.1%, respectively.  ...  To identify genes that could predict the development of cancer in UC, we conducted a DNA microarray analysis using nonneoplastic rectal mucosa of UC patients.  ...  On the other hand, recent advances in DNA microarray technology have shown the potential use of expression profiles for molecular classification of cancer, as well as for prediction of disease outcome  ... 
doi:10.1158/1078-0432.ccr-06-0753 pmid:17255260 fatcat:6seulmyp2napvbdofkflil5lp4

Proceedings of the Fourth Annual Conference of the MidSouth Computational Biology and Bioinformatics Society

Dawn Wilkins, Yuriy Gusev, Raja Loganantharaj, Susan Bridges, Stephen Winters-Hilt, Jonathan D Wren
2007 BMC Bioinformatics  
Acknowledgements We thank the Conference Committee and Program Committee for their help in organizing MCBIOS 2007, and we also thank our MCBIOS members and external peer-reviewers for their dedication  ...  and efforts to review submitted manuscripts.  ...  The first [33] describes a new Support Vector Machine (SVM) based method for clustering (unsupervised learning) -a marked departure from the standard supervised-learning approach to SVMs.  ... 
doi:10.1186/1471-2105-8-s7-s1 pmid:18047708 pmcid:PMC2099477 fatcat:2inyye3afzc6fbvshm2m7ck3tm

A multi-gene approach to differentiate papillary thyroid carcinoma from benign lesions: gene selection using support vector machines with bootstrapping

Krzysztof Fujarewicz, Michał Jarząb, Markus Eszlinger, Knut Krohn, Ralf Paschke, Małgorzata Oczko-Wojciechowska, Małgorzata Wiench, Aleksandra Kukulska, Barbara Jarząb, Andrzej Swierniak
2007 Endocrine-Related Cancer  
Gene selection was carried out by the support vector machines method with bootstrapping, which allowed us 1) ranking the genes that were most important for classification quality and appeared most frequently  ...  We also assessed the accuracy of benign/malignant classification, based on gene expression profiling, for PTC.  ...  This work was partially supported by the Deutsche Krebshilfe grant 106542 (R P and K K) and the Interdisciplinary Center for Clinical Research at the Faculty of Medicine of the University of Leipzig (projects  ... 
doi:10.1677/erc-06-0048 pmid:17914110 pmcid:PMC2216417 fatcat:7sw4m6y2afd4zpi4xmaez5wzyi

Integrative analysis of multi-omics data for identifying multi-markers for diagnosing pancreatic cancer

Min-Seok Kwon, Yongkang Kim, Seungyeoun Lee, Junghyun Namkung, Taegyun Yun, Sung Yi, Sangjo Han, Meejoo Kang, Sun Kim, Jin-Young Jang, Taesung Park
2015 BMC Genomics  
Using support vector machine (SVM) modelling and leave-one-out cross validation (LOOCV), we evaluated the diagnostic performance of single-or multi-markers based on miRNA and mRNA expression profiles from  ...  Methods: In this study, we evaluated the benefits of multi-omics data analysis by integrating miRNA and mRNA expression data in pancreatic cancer.  ...  In this step, support vector machine (SVM) was applied for qualitative classification evaluated with leave-one-out cross validation (LOOCV).  ... 
doi:10.1186/1471-2164-16-s9-s4 pmid:26328610 pmcid:PMC4547403 fatcat:j4ibronbbjbtxpeb4rq2di4cce

Computational Data Mining in Cancer Bioinformatics and Cancer Epidemiology

Zhenqiu Liu, Dechang Chen, Xuewen Chen, Haomiao Jia
2009 Journal of Biomedicine and Biotechnology  
In one article, Huang and Wu propose a novel method for cancer diagnosis using gene expression data by casting the classification problem as finding sparse representations of test samples with respect  ...  This method is more robust than the traditional support vector machines (SVMs).  ... 
doi:10.1155/2009/582697 pmid:19841677 pmcid:PMC2762242 fatcat:7ut2hmu77rfphkbl2ktp7oljce

Examining the Classification Accuracy of TSVMs with Feature Selection in Comparison with the GLAD Algorithm

Hala Helmi, Jonathan M. Garibaldi, Uwe Aickelin
2011 Social Science Research Network  
Support Vector Machines Support Vector Machine (SVMs), as a supervised machine learning technique, perform well in several areas of biological research, including evaluating microarray expression data  ...  for Transductive Support Vector Machines (TSVMs).  ... 
doi:10.2139/ssrn.2829242 fatcat:mwf2cwa2vnfgpjqkma45crifz4

Identification of potential tissue-specific cancer biomarkers and development of cancer versus normal genomic classifiers

Akram Mohammed, Greyson Biegert, Jiri Adamec, Tomáš Helikar
2017 OncoTarget  
Machine learning techniques for cancer prediction and biomarker discovery can hasten cancer detection and significantly improve prognosis.  ...  To demonstrate this potential, 2,175 gene expression samples from nine tissue types were obtained to identify gene sets whose expression is characteristic of each cancer class.  ...  Discovery; KEGG -Kyoto Encyclopedia of Genes and Genomes; ML -Machine Learning; SVM -Support Vector Machine; RF -Random Forests; GEO -Gene Expression Omnibus; RMA -Robust Multi Average; GO -Gene Ontology  ... 
doi:10.18632/oncotarget.21127 pmid:29156751 pmcid:PMC5689641 fatcat:vg2sjfrzibaj7pjn5nrn4xrlsu

Profiling alternatively spliced mRNA isoforms for prostate cancer classification

Chaolin Zhang, Hai-Ri Li, Jian-Bing Fan, Jessica Wang-Rodriguez, Tracy Downs, Xiang-Dong Fu, Michael Q Zhang
2006 BMC Bioinformatics  
Here, we investigate the advantage of using splice isoforms, which couple transcriptional and splicing regulation, for cancer classification.  ...  A support vector machine (SVM) classifier trained on 128 signature isoforms can correctly predict 92% of the cases, which outperforms the classifier using overall mRNA abundance by about 5%.  ...  Acknowledgements We would like to thank Joanne Yeakley and Marina Bibikova for help generating the array data used in this study. We thank Dr.  ... 
doi:10.1186/1471-2105-7-202 pmid:16608523 pmcid:PMC1458362 fatcat:cgve6kwsxfbxnpooaxpeiwjbae

SynBlast: Assisting the analysis of conserved synteny information

Jörg Lehmann, Peter F Stadler, Sonja J Prohaska
2008 BMC Bioinformatics  
Here, we investigate the advantage of using splice isoforms, which couple transcriptional and splicing regulation, for cancer classification.  ...  A support vector machine (SVM) classifier trained on 128 signature isoforms can correctly predict 92% of the cases, which outperforms the classifier using overall mRNA abundance by about 5%.  ...  Acknowledgements We would like to thank Joanne Yeakley and Marina Bibikova for help generating the array data used in this study. We thank Dr.  ... 
doi:10.1186/1471-2105-9-351 pmid:18721485 pmcid:PMC2543028 fatcat:qa6mof2dwvelziwkbe75e5r5si

On the statistical assessment of classifiers using DNA microarray data

N Ancona, R Maglietta, A Piepoli, A D'Addabbo, R Cotugno, M Savino, S Liuni, M Carella, G Pesole, F Perri
2006 BMC Bioinformatics  
In this paper we present a method for the statistical assessment of cancer predictors which make use of gene expression profiles.  ...  The methodology is applied to a new data set of microarray gene expression data collected in Casa Sollievo della Sofferenza Hospital, Foggia -Italy.  ...  Acknowledgements We would like to thank Sebastiano Stramaglia for some valuable and illuminating discussions on numerous theoretic and experimental aspects of the paper.  ... 
doi:10.1186/1471-2105-7-387 pmid:16919171 pmcid:PMC1564153 fatcat:ewqwocbgirbjdapzpzuyvq4mqe

Gene expression profiling of papillary thyroid carcinomas in Korean patients by oligonucleotide microarrays

Ki-Wook Chung, Seok Won Kim, Sun Wook Kim
2012 Journal of the Korean Surgical Society  
ACKNOWLEDGEMENTS This work was supported by a grant from the National Cancer Center (Number 0710230-1) .  ...  Classifications were made using a support vector machine (SVM) algorithm in ArrayAssist (Stratagene, La Jolla, CA, USA); 16 samples (10 PTCs and 6 controls) were used by training, and 10 samples (9 PTCs  ...  a group of genes for the diagnosis of PTCs.  ... 
doi:10.4174/jkss.2012.82.5.271 pmid:22563533 pmcid:PMC3341475 fatcat:jb5k745enfdzxgwvanzhxdbflm
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