Gene-based multiclass cancer diagnosis with class-selective rejections.
Supervised learning of microarray data is receiving much attention in recent years. Multiclass cancer diagnosis, based on selected gene profiles, are used as adjunct of clinical diagnosis. However, supervised diagnosis may hinder patient care, add expense or confound a result. To avoid this misleadi...
| Publicado en: | Journal of Biomedicine & Biotechnology pp. 10p - 11 |
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| Autores principales: | , , |
| Formato: | algorithm equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
2009 Regular Issue
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=105438740&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 105438740 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 11107243 137K jtl: Journal of Biomedicine & Biotechnology issn: 11107243 maglogo: N pubinfo: dt: 2009 Regular Issue pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 105438740 2010398056 10.1155/2009/608701 NLM19584932 105438740 ppf: 10p ppct: 1 formats: fmt: @attributes: type: P tig: atl: Gene-based multiclass cancer diagnosis with class-selective rejections. aug: au: Jrad N Grall-Maës E Beauseroy P affil: Institut Charles Delaunay (ICD, FRE CNRS 2848), Université de Technologie de Troyes, LM2S 12 rue Marie Curie, BP 2060, 10010 Troyes cedex, France. nisrine.jrad@utt.fr sug: subj: Genes Genetic Techniques Neoplasms Diagnosis Algorithms Analysis of Variance Data Analysis Gene Expression Health Care Costs Kruskal-Wallis Test Mathematics Patient Care Reliability Statistics Time Factors Human ab: Supervised learning of microarray data is receiving much attention in recent years. Multiclass cancer diagnosis, based on selected gene profiles, are used as adjunct of clinical diagnosis. However, supervised diagnosis may hinder patient care, add expense or confound a result. To avoid this misleading, a multiclass cancer diagnosis with class-selective rejection is proposed. It rejects some patients from one, some, or all classes in order to ensure a higher reliability while reducing time and expense costs. Moreover, this classifier takes into account asymmetric penalties dependent on each class and on each wrong or partially correct decision. It is based on nu-1-SVM coupled with its regularization path and minimizes a general loss function defined in the class-selective rejection scheme. The state of art multiclass algorithms can be considered as a particular case of the proposed algorithm where the number of decisions is given by the classes and the loss function is defined by the Bayesian risk. Two experiments are carried out in the Bayesian and the class selective rejection frameworks. Five genes selected datasets are used to assess the performance of the proposed method. Results are discussed and accuracies are compared with those computed by the Naive Bayes, Nearest Neighbor, Linear Perceptron, Multilayer Perceptron, and Support Vector Machines classifiers. pubtype: Academic Journal doctype: algorithm equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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