High-dimensional Microarray Data Analysis: Cancer Gene Diagnosis and Malignancy Indexes by Microarray - Couverture rigide
Synopsis
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 ratioof 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.Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
À propos de l?auteur
Shuichi Shinmura, Seikei University
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
Autres éditions populaires du même titre
Résultats de recherche pour High-dimensional Microarray Data Analysis: Cancer Gene...
Image d'archives
High-dimensional Microarray Data Analysis. Cancer Gene Diagnosis and Malignancy Indexes by Microarray.
Edité par
Singapore, Springer., 2019
ISBN 10 : 9811359970
ISBN 13 : 9789811359972
Ancien ou d'occasion
Couverture rigide
Vendeur : Universitätsbuchhandlung Herta Hold GmbH, Berlin, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
xxv, 419 p. Hardcover. Versand aus Deutschland / We dispatch from Germany via Air Mail. Einband bestoßen, daher Mängelexemplar gestempelt, sonst sehr guter Zustand. Imperfect copy due to slightly bumped cover, apart from this in very good condition. Stamped. Sprache: Englisch. N° de réf. du vendeur 6909IB
Acheter D'occasion
EUR 12
Frais de port :
EUR 30
De Allemagne vers Etats-Unis
Quantité disponible : 2 disponible(s)
Image d'archives
High-Dimensional Microarray Data Analysis
Vendeur : Books Puddle, New York, NY, Etats-Unis
Évaluation du vendeur 4 sur 5 étoiles
Etat : New. N° de réf. du vendeur 26376431112
Acheter neuf
EUR 99,15
Frais de port :
EUR 3,45
Vers Etats-Unis
Quantité disponible : 1 disponible(s)
Image d'archives
High-Dimensional Microarray Data Analysis
Vendeur : Majestic Books, Hounslow, Royaume-Uni
Évaluation du vendeur 4 sur 5 étoiles
Etat : New. N° de réf. du vendeur 370695639
Acheter neuf
EUR 104,10
Frais de port :
EUR 7,39
De Royaume-Uni vers Etats-Unis
Quantité disponible : 1 disponible(s)
Image d'archives
High-Dimensional Microarray Data Analysis
Vendeur : Biblios, Frankfurt am main, HESSE, Allemagne
Évaluation du vendeur 4 sur 5 étoiles
Etat : New. N° de réf. du vendeur 18376431106
Acheter neuf
EUR 103,91
Frais de port :
EUR 9,95
De Allemagne vers Etats-Unis
Quantité disponible : 1 disponible(s)
Image d'archives
High-dimensional Microarray Data Analysis: Cancer Gene Diagnosis and Malignancy Indexes by Microarray
Vendeur : Lucky's Textbooks, Dallas, TX, Etats-Unis
Évaluation du vendeur 5 sur 5 étoiles
Etat : New. N° de réf. du vendeur ABLIING23Apr0412070086117
Acheter neuf
EUR 131,71
Frais de port :
EUR 3,45
Vers Etats-Unis
Quantité disponible : Plus de 20 disponibles
Image d'archives
High-dimensional Microarray Data Analysis: Cancer Gene Diagnosis and Malignancy Indexes by Microarray
Vendeur : Ria Christie Collections, Uxbridge, Royaume-Uni
Évaluation du vendeur 5 sur 5 étoiles
Etat : New. In. N° de réf. du vendeur ria9789811359972_new
Acheter neuf
EUR 133,67
Frais de port :
EUR 13,62
De Royaume-Uni vers Etats-Unis
Quantité disponible : Plus de 20 disponibles
Image fournie par le vendeur
High-dimensional Microarray Data Analysis
Edité par
Springer Nature Singapore Mai 2019, 2019
ISBN 10 : 9811359970
ISBN 13 : 9789811359972
Neuf
Couverture rigide
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Buch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 448 pp. Englisch. N° de réf. du vendeur 9789811359972
Acheter neuf
EUR 139,09
Frais de port :
EUR 23
De Allemagne vers Etats-Unis
Quantité disponible : 2 disponible(s)
Image fournie par le vendeur
High-dimensional Microarrays Data Analysis
Edité par
Springer Singapore, 2019
ISBN 10 : 9811359970
ISBN 13 : 9789811359972
Neuf
Couverture rigide
Vendeur : moluna, Greven, Allemagne
Évaluation du vendeur 4 sur 5 étoiles
Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Shows how a new theory of discriminant analysis was used to solve unresolved cancer gene analysis for the first timeExplains how high-dimensional data such as microarrays can be decomposed for genetic cancer diagnosis . N° de réf. du vendeur 259166371
Acheter neuf
EUR 118,61
Frais de port :
EUR 48,99
De Allemagne vers Etats-Unis
Quantité disponible : Plus de 20 disponibles
Image fournie par le vendeur
High-dimensional Microarray Data Analysis | Cancer Gene Diagnosis and Malignancy Indexes by Microarray
Edité par
Springer Singapore, 2019
ISBN 10 : 9811359970
ISBN 13 : 9789811359972
Neuf
Couverture rigide
Vendeur : preigu, Osnabrück, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Buch. Etat : Neu. High-dimensional Microarray Data Analysis | Cancer Gene Diagnosis and Malignancy Indexes by Microarray | Shuichi Shinmura | Buch | xxv | Englisch | 2019 | Springer Singapore | EAN 9789811359972 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand. N° de réf. du vendeur 115083978
Acheter neuf
EUR 123,10
Frais de port :
EUR 70
De Allemagne vers Etats-Unis
Quantité disponible : 5 disponible(s)
Image fournie par le vendeur
High-dimensional Microarray Data Analysis
Edité par
Springer Nature Singapore, Springer Nature Singapore Mai 2019, 2019
ISBN 10 : 9811359970
ISBN 13 : 9789811359972
Neuf
Couverture rigide
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Buch. Etat : Neu. Neuware -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 ratioof 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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 448 pp. Englisch. N° de réf. du vendeur 9789811359972
Acheter neuf
EUR 139,09
Frais de port :
EUR 60
De Allemagne vers Etats-Unis
Quantité disponible : 2 disponible(s)