Record Nr.

UNINA9910818732403321

Autore

Beaumarchais Pierre-Augustin Caron de

Titolo

Almaviva et Rosine : Pantomime en trois actes, melee de danses / / Pierre-Augustin Caron de Beaumarchais

Pubbl/distr/stampa


[Place of publication not identified] : , : Ligaran, , 2015

ISBN

2-335-05574-7

Descrizione fisica

1 online resource (47 p.)

Disciplina

842.009

Soggetti

French drama - History and criticism

French drama

French literature

Lingua di pubblicazione

Francese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Note generali

Description based upon print version of record.

Nota di contenuto

Couverture; Page de Copyright; Page de titre; Personnages; Acte premier; Scène première; Scène II; Scène III; Scène IV; Scène V; Scène VI; Scène VII; Scène VIII; Scène IX; Acte II; Scène première; Scène II; Scène III; Scène IV; Scène V; Scène VI; Scène VII; Scène VIII; Scène IX; Scène X; Scène XI; Scène XII; Scène XIII; Scène XIV; Acte III; Scène première; Scène II; Scène III; Scène IV; Scène V; Scène VI; Scène VII; Scène VIII

Sommario/riassunto

Extrait : ""SCÈNE PREMIÈRE : Les Paysans sont occupés à orner la maison de Bartholo, et à élever un trône de fleurs pour la fête de Rosine, que le docteur veut célébrer. Sept heures sonnent : les paysans pressent leur ouvrage. SCÈNE II : Figaro entre, il gronde les paysans. Tout devrait être prêt ; Rosine est peut-être éveillée. Figaro et les paysans se disputent.""À PROPOS DES ÉDITIONS LIGARANLes éditions LIGARAN proposent des versions numériques de qualité de grands livres de la littérature classique mais également des livres rares en partenariat avec la BNF. Beaucoup de soins sont apportés

Record Nr.

UNINA9910350247903321

Autore

Shinmura Shuichi

Titolo

High-dimensional Microarray Data Analysis : Cancer Gene Diagnosis and Malignancy Indexes by Microarray / / by Shuichi Shinmura

Pubbl/distr/stampa


Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2019

ISBN

981-13-5998-9

Edizione

[1st ed. 2019.]

Descrizione fisica

1 online resource (XXV, 419 p. 261 illus., 130 illus. in color.)

Disciplina

519.5

Soggetti

Biometry

Statistics

Social sciences - Statistical methods

Biostatistics

Statistical Theory and Methods

Statistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Nota di contenuto

1 New Theory of Discriminant Analysis and Cancer Gene Analysis -- 2 Overview of Cancer Gene Diagnosis by RIP and Revised LP-OLDF -- 3 Cancer Gene Diagnosis of Alon Microarray -- 4 Further Examinations of SMs---Defect of Revised LP-OLDF and Correlations of Genes -- 5　Cancer Gene Diagnosis of Golub et al. Microarray -- 6 Cancer Gene Diagnosis of Shipp et al. Microarray -- 7 Cancer Gene Diagnosis of Singh et al. Microarray -- 8 Cancer Gene Diagnosis of Tian et al. Microarray -- 9 Cancer Gene Diagnosis of Chiaretti et al. Microarray -- 10 LINGO Programs of Cancer Gene Analysis -- Index.

Sommario/riassunto

This book shows how to decompose high-dimensional microarrays into small subspaces (Small Matryoshkas, SMs), statistically analyze them, and perform cancer gene diagnosis. The information is useful for genetic experts, anyone who analyzes genetic data, and students to use as practical textbooks. Discriminant analysis is the best approach for microarray consisting of normal and cancer classes. Microarrays are linearly separable data (LSD, Fact 3). However, because most linear discriminant function (LDF) cannot discriminate LSD theoretically and

error rates are high, no one had discovered Fact 3 until now. Hard-margin SVM (H-SVM) and Revised IP-OLDF (RIP) can find Fact3 easily. LSD has the Matryoshka structure and is easily decomposed into many SMs (Fact 4). Because all SMs are small samples and LSD, statistical methods analyze SMs easily. However, useful results cannot be obtained. On the other hand, H-SVM and RIP can discriminate two classes in SM entirely. RatioSV is the ratio of SV distance and discriminant range. The maximum RatioSVs of six microarrays is over 11.67%. This fact shows that SV separates two classes by window width (11.67%). Such easy discrimination has been unresolved since 1970. The reason is revealed by facts presented here, so this book can be read and enjoyed like a mystery novel. Many studies point out that it is difficult to separate signal and noise in a high-dimensional gene space. However, the definition of the signal is not clear. Convincing evidence is presented that LSD is a signal. Statistical analysis of the genes contained in the SM cannot provide useful information, but it shows that the discriminant score (DS) discriminated by RIP or H-SVM is easily LSD. For example, the Alon microarray has 2,000 genes which can be divided into 66 SMs. If 66 DSs are used as variables, the result is a 66-dimensional data. These signal data can be analyzed to find malignancy indicators by principal component analysis and cluster analysis.