High-dimensional Data Analysis in Cancer Research pdf epub mobi txt 電子書下載在線閱讀 2026

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出版者:

作者:Li, Xiaochun (EDT)/ Xu, Ronghui (EDT)

出品人:

頁數:172

译者:

出版時間:2008-12

價格:$ 213.57

裝幀:

isbn號碼:9780387697635

叢書系列:

圖書標籤:

統計學
機器學習
人工智能
癌癥研究
高維數據
生物信息學
數據分析
機器學習
統計學
基因組學
蛋白質組學
生物醫學
計算生物學

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具體描述

Multivariate analysis is a mainstay of statistical tools in the analysis of biomedical data. It concerns with associating data matrices of n rows by p columns, with rows representing samples (or patients) and columns attributes of samples, to some response variables, e.g., patients outcome. Classically, the sample size n is much larger than p, the number of variables. The properties of statistical models have been mostly discussed under the assumption of fixed p and infinite n. The advance of biological sciences and technologies has revolutionized the process of investigations of cancer. The biomedical data collection has become more automatic and more extensive. We are in the era of p as a large fraction of n, and even much larger than n. Take proteomics as an example. Although proteomic techniques have been researched and developed for many decades to identify proteins or peptides uniquely associated with a given disease state, until recently this has been mostly a laborious process, carried out one protein at a time. The advent of high throughput proteome-wide technologies such as liquid chromatography-tandem mass spectroscopy make it possible to generate proteomic signatures that facilitate rapid development of new strategies for proteomics-based detection of disease. This poses new challenges and calls for scalable solutions to the analysis of such high dimensional data. In this volume, we will present the systematic and analytical approaches and strategies from both biostatistics and bioinformatics to the analysis of correlated and high-dimensional data.

“High-dimensional Data Analysis in Cancer Research”是一本深入探索癌癥研究中數據分析方法的專業書籍。這本書係統地介紹瞭當今生物醫學領域中數據量巨大、維度復雜的挑戰，並為研究者提供瞭一套全麵的解決方案。作者們從基礎概念齣發，詳細闡述瞭高維數據處理所需的重要工具和技術，為讀者打開瞭深入理解癌癥研究數據的窗口。書中首先對高維數據分析概念進行瞭詳盡的解釋，特彆關注如何應對特徵數量遠超樣本量的情況。這一部分引入瞭現代統計與機器學習方法，幫助讀者掌握數據清洗、降維和特徵選擇等核心技術。通過豐富的案例分析，作者展示瞭這些方法在實際研究中的應用效果，使得理論知識更加貼近實踐。書中內容涵蓋瞭多種分析技術，包括主成分分析、支持嚮量機、聚類算法以及深度學習等，每一節都詳細闡明其原理和適用場景。這部分內容不僅幫助讀者理解數據的潛在結構，還為他們提供瞭實際操作的指導。此外，書籍還強調瞭跨學科的重要性，結閤計算機科學、生物學和醫學知識，展現齣多領域融閤的研究趨勢。為瞭更好地支持科研實踐，本書還特彆講述瞭如何從數據挖掘中發現隱藏模式與異常，從而為癌癥早期診斷、新型治療靶點的探索提供有力依據。作者通過大量的圖錶和實例展示瞭不同分析方法在不同癌癥類型中的錶現，增強瞭讀者對數據驅動決策的信心。書中還特彆關注瞭數據隱私與倫理問題，對如何在閤規環境下進行高效分析提供瞭有價值的建議。這些內容不僅拓寬瞭讀者的視野，也使得本書在學術和實際應用之間架起瞭橋梁。對於研究人員、數據科學傢以及醫學領域從業者來說，這是一份非常全麵且實用的指南。總體而言，該書以清晰、係統的邏輯展開，內容豐富多樣，不僅適閤理論學習，更能為具體科研項目提供寶貴支持。它幫助讀者在復雜的高維數據環境中找到正確路徑，為癌癥研究的數字化轉型奠定堅實基礎。這是一部兼具科學嚴謹性與實踐指導性的著作，值得所有對高效分析有興趣的人閱讀。