Probabilistic Graphical Models for Genetics, Genomics and Postgenomics (Hardcover)

Editor in Chief Christine Sinoquet , Editor Raphael Mourad

  • 出版商: Oxford University
  • 出版日期: 2014-11-25
  • 售價: $1,860
  • 貴賓價: 9.8$1,823
  • 語言: 英文
  • 頁數: 480
  • 裝訂: Hardcover
  • ISBN: 0198709021
  • ISBN-13: 9780198709022
  • 下單後立即進貨 (約5~7天)

商品描述

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 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 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 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.

商品描述(中文翻譯)

如今,生物資訊學家和遺傳學家面臨著大量的高通量數據,這些數據通常具有不確定性、高維度和複雜性等特徵。這些數據只有在以靈活且可擴展的模型表示後,才能對這些所謂的「組學」數據提供洞見,然後才能進行進一步的分析。在統計學和機器學習的交界處,概率圖模型(PGMs)代表了一種強大的形式,用於發現複雜的關係網絡。

這些模型也適合納入先驗的生物信息。從基因表達數據重建網絡或許是PGMs成功應用的最具代表性的研究領域。然而,這些模型也重新引起了對遺傳學的廣泛興趣,特別是在關聯遺傳學、因果發現、結果預測、拷貝數變異檢測和表觀遺傳學方面。本書提供了PGMs在遺傳學、基因組學和後基因組學應用的概述,以滿足這種日益增長的興趣。

生物資訊學的一個顯著特徵是跨學科性,但當需要領域專家之間的複雜合作時,這種特徵便達到了極限。目前,專門設計使用概率圖模型的後基因組學或遺傳學的高級方法的人數不多。本書解讀了這些模型,使其被認為困難的特性不再妨礙其使用,並專注於十五個示例,展示模型背後的機制。

《遺傳學、基因組學和後基因組學的概率圖模型》涵蓋六個主要主題:
(1) 基因網絡推斷
(2) 因果發現
(3) 關聯遺傳學
(4) 表觀遺傳學
(5) 拷貝數變異檢測
(6) 從高維基因組數據預測結果。

本書由國際領先的專家撰寫,是概率圖模型與遺傳學、基因組學和後基因組學交匯處最前沿工作的集成。自成一體的章節提供了應用這些強大技術的利弊的深入說明。