Mourad Raphaël (23 results)
- Hardcover
- First Edition
Seller: Burwood Books, Wickham Market, United KingdomBurwood Books
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Hardcover. Condition: Fine. First Edition. Hardback. 8vo. Original publishers illustrated laminated boards. Issued without dust jacket. At the crossroads between statistics and machine learning, probabilistic graphical models (PGMs) provide a powerful formal framework to model complex data. An expanding volume of biological data… of various types, the so-called 'omics', is in need of accurate and efficient methods for modelling and PGMs are expected to have a prominent role to play. This book provides an overview of the applications of PGMs to genetics, genomics and postgenomics to meet this increased interest. ISBN: 0198709021 Pages: 480 Fine.
- Hardcover
Seller: Romtrade Corp., STERLING HEIGHTS, MI, U.S.A.Romtrade Corp.
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Condition: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.
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Probabilistic Graphical Models for Genetics, Genomics and Postgenomics
Mourad, Editor Raphael [Editor]; Sinoquet, Editor in Chief Christine [Editor]
- Hardcover
- First Edition
Seller: Prior Books Ltd, Cheltenham, United KingdomPrior Books Ltd
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£ 57.00
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Hardcover. Condition: New. First Edition. Publisher's laminated hardback, firm and square, sharp corners, strong joints, no bumps, no rubs. Contents crisp and tight. Size: 250mm x 180mm. Thus a fresh, clean copy in new condition offered for sale at reasonable price.
- Hardcover
Seller: Zoom Books East, Glendale Heights, IL, U.S.A.Zoom Books East
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£ 91.57
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Condition: very_good. Book is in very good condition and may include minimal underlining highlighting. The book can also include "From the library of" labels. May not contain miscellaneous items toys, dvds, etc. . We offer 100% money back guarantee and 24 7 customer service.
- Hardcover
Seller: Books Puddle, New York, NY, U.S.A.Books Puddle
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Condition: Used. pp. 464.
- Hardcover
Seller: Majestic Books, Hounslow, United KingdomMajestic Books
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Condition: Used. pp. 464.
- Hardcover
Seller: Biblios, frankfurt am main, HESSE, GermanyBiblios
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Condition: Used. pp. 464.
- Hardcover
Seller: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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- Hardcover
Seller: GreatBookPricesUK, Woodford Green, United KingdomGreatBookPricesUK
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- Hardcover
Seller: GreatBookPricesUK, Woodford Green, United KingdomGreatBookPricesUK
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- Hardcover
Seller: Mispah books, Redhill, SURRE, United KingdomMispah books
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- Hardcover
Seller: THE SAINT BOOKSTORE, Southport, United KingdomTHE SAINT BOOKSTORE
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- Hardcover
Seller: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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- Hardcover
Seller: Revaluation Books, Exeter, United KingdomRevaluation Books
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Hardcover. Condition: Brand New. 449 pages. 9.75x7.50x1.00 inches. In Stock.
- Hardcover
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Seller: Brook Bookstore On Demand, Napoli, NA, ItalyBrook Bookstore On Demand
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Condition: new. Questo è un articolo print on demand.
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Seller: PBShop.store UK, Fairford, GLOS, United KingdomPBShop.store UK
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HRD. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
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Seller: PBShop.store US, Wood Dale, IL, U.S.A.PBShop.store US
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- Hardcover
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Seller: Grand Eagle Retail, Bensenville, IL, U.S.A.Grand Eagle Retail
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Hardcover. Condition: new. Hardcover. Nowadays bioinformaticians and geneticists are faced with myriad high-throughput data usually presenting the characteristics of uncertainty, high dimensionality and large complexity.These data will only allow insights into this wealth of so-called 'omics' data if represented by flexible and…scalable models, prior to any further analysis. At the interface between statistics and machine learning, probabilistic graphical models (PGMs) represent apowerful formalism to discover complex networks of relations.These models are also amenable to incorporating a priori biological information. Network reconstruction from geneexpression data represents perhaps the most emblematic area of research where PGMs have been successfully applied. However these models have also created renewed interest in genetics in the broad sense, in particular regarding association genetics, causality discovery, prediction of outcomes, detection of copy number variations, and epigenetics. This book provides an overview of the applications of PGMs to genetics, genomics and postgenomics to meet this increased interest.A salient feature of bioinformatics, interdisciplinarity, reaches its limit when an intricate cooperation between domain specialists is requested. Currently, few people are specialists in the designof advanced methods using probabilistic graphical models for postgenomics or genetics. This book deciphers such models so that their perceived difficulty no longer hinders their use and focuses on fifteen illustrations showing the mechanisms behind the models.Probabilistic Graphical Models for Genetics, Genomics and Postgenomics covers six main themes:(1) Gene network inference(2) Causality discovery(3) Association genetics(4)Epigenetics(5) Detection of copy number variations(6) Prediction of outcomes from high-dimensional genomic data.Written by leading international experts, thisis a collection of the most advanced work at the crossroads of probabilistic graphical models and genetics, genomics, and postgenomics. The self-contained chapters provide an enlightened account of the pros and cons of applying these powerful techniques. At the crossroads between statistics and machine learning, probabilistic graphical models (PGMs) provide a powerful formal framework to model complex data. An expanding volume of biological data of various types, the so-called 'omics', is in need of accurate and efficient methods for modelling and PGMs are expected to have a prominent role to play. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
- Hardcover
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Seller: CitiRetail, Stevenage, United KingdomCitiRetail
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£ 131.49
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Hardcover. Condition: new. Hardcover. Nowadays bioinformaticians and geneticists are faced with myriad high-throughput data usually presenting the characteristics of uncertainty, high dimensionality and large complexity. These data will only allow insights into this wealth of so-called 'omics' data if represented by flexible and… scalable models, prior to any further analysis. At the interface between statistics and machine learning, probabilistic graphical models (PGMs) represent apowerful formalism to discover complex networks of relations. These models are also amenable to incorporating a priori biological information. Network reconstruction from geneexpression data represents perhaps the most emblematic area of research where PGMs have been successfully applied. However these models have also created renewed interest in genetics in the broad sense, in particular regarding association genetics, causality discovery, prediction of outcomes, detection of copy number variations, and epigenetics. This book provides an overview of the applications of PGMs to genetics, genomics and postgenomics to meet this increased interest.A salient feature of bioinformatics, interdisciplinarity, reaches its limit when an intricate cooperation between domain specialists is requested. Currently, few people are specialists in the designof advanced methods using probabilistic graphical models for postgenomics or genetics. This book deciphers such models so that their perceived difficulty no longer hinders their use and focuses on fifteen illustrations showing the mechanisms behind the models. Probabilistic Graphical Models for Genetics, Genomics and Postgenomics covers six main themes:(1) Gene network inference(2) Causality discovery(3) Association genetics(4)Epigenetics(5) Detection of copy number variations(6) Prediction of outcomes from high-dimensional genomic data.Written by leading international experts, thisis a collection of the most advanced work at the crossroads of probabilistic graphical models and genetics, genomics, and postgenomics. The self-contained chapters provide an enlightened account of the pros and cons of applying these powerful techniques. At the crossroads between statistics and machine learning, probabilistic graphical models (PGMs) provide a powerful formal framework to model complex data. An expanding volume of biological data of various types, the so-called 'omics', is in need of accurate and efficient methods for modelling and PGMs are expected to have a prominent role to play. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
- Hardcover
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Seller: THE SAINT BOOKSTORE, Southport, United KingdomTHE SAINT BOOKSTORE
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£ 146.25
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Hardback. Condition: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.
- Hardcover
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Seller: preigu, Osnabrück, Germanypreigu
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£ 171.52
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Buch. Condition: Neu. Probabilistic Graphical Models for Genetics, Genomics, and Postgenomics | Christine Sinoquet | Buch | Gebunden | Englisch | 2014 | ACADEMIC | EAN 9780198709022 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.
- Hardcover
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Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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£ 176.20
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Buch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Nowadays bioinformaticians and geneticists are faced with myriad high-throughput data usually presenting the characteristics of uncertainty, high dimensionality and large complexity.These data will only allow insights into this wealth of so-cal…led 'omics' data if represented by flexible and scalable models, prior to any further analysis. At the interface between statistics and machine learning, probabilistic graphical models (PGMs) represent a powerful formalism to discover complex networks of relations.These models are also amenable to incorporating a priori biological information. Network reconstruction from gene expression data represents perhaps the most emblematic area of research where PGMs have been successfully applied. However these models have also created renewed interest in genetics in the broad sense, in particular regarding association genetics, causality discovery, prediction of outcomes, detection of copy number variations, and epigenetics. This book provides an overview of theapplications of PGMs to genetics, genomics and postgenomics to meet this increased interest.A salient feature of bioinformatics, interdisciplinarity, reaches its limit when an intricate cooperation between domain specialists is requested. Currently, few people are specialists in the design of advanced methods using probabilistic graphical models for postgenomics or genetics. This book deciphers such models so that their perceived difficulty no longer hinders their use and focuses on fifteen illustrations showing the mechanisms behind the models.Probabilistic Graphical Models for Genetics, Genomics and Postgenomics covers six main themes:(1) Gene network inference(2) Causality discovery(3) Association genetics(4) Epigenetics(5) Detection of copy number variations(6) Prediction of outcomes from high-dimensional genomic data.Written by leading international experts, this is a collection of the most advanced work at the crossroads of probabilistic graphical models and genetics, genomics, and postgenomics. The self-contained chapters provide an enlightened account of the pros and cons of applying these powerful techniques.
