Learning and Inference in Computational Systems Biology (Hardcover)

Computational systems biology aims to develop algorithms that uncover the structure and parameterization of the underlying mechanistic model—in other words, to answer specific questions about the underlying mechanisms of a biological system—in a process that can be thought of as learning or inference. This volume offers state-of-the-art perspectives from computational biology, statistics, modeling, and machine learning on new methodologies for learning and inference in biological networks.

The chapters offer practical approaches to biological inference problems ranging from genome-wide inference of genetic regulation to pathway-specific studies. Both deterministic models (based on ordinary differential equations) and stochastic models (which anticipate the increasing availability of data from small populations of cells) are considered. Several chapters emphasize Bayesian inference, so the editors have included an introduction to the philosophy of the Bayesian approach and an overview of current work on Bayesian inference. Taken together, the methods discussed by the experts in Learning and Inference in Computational Systems Biology provide a foundation upon which the next decade of research in systems biology can be built.

Contributors: Florence d'Alch e-Buc, John Angus, Matthew J. Beal, Nicholas Brunel, Ben Calderhead, Pei Gao, Mark Girolami, Andrew Golightly, Dirk Husmeier, Johannes Jaeger, Neil D. Lawrence, Juan Li, Kuang Lin, Pedro Mendes, Nicholas A. M. Monk, Eric Mjolsness, Manfred Opper, Claudia Rangel, Magnus Rattray, Andreas Ruttor, Guido Sanguinetti, Michalis Titsias, Vladislav Vyshemirsky, David L. Wild, Darren Wilkinson, Guy Yosiphon

Computational Molecular Biology series

計算系統生物學旨在開發算法，以揭示潛在機械模型的結構和參數化，換句話說，回答有關生物系統潛在機制的具體問題，這個過程可以被視為「學習」或「推斷」。本書提供了來自計算生物學、統計學、建模和機器學習的最新觀點，關於生物網絡中學習和推斷的新方法。

這些章節提供了從基因組範圍的遺傳調控推斷到特定通路研究的生物推斷問題的實用方法。考慮了基於常微分方程的確定性模型和預測小細胞數據日益增加的隨機模型。幾個章節強調貝葉斯推斷，因此編者包括了對貝葉斯方法的哲學介紹和對當前貝葉斯推斷工作的概述。綜合來看，計算系統生物學中專家們討論的方法為未來十年的系統生物學研究奠定了基礎。

貢獻者：Florence d'Alch e-Buc、John Angus、Matthew J. Beal、Nicholas Brunel、Ben Calderhead、Pei Gao、Mark Girolami、Andrew Golightly、Dirk Husmeier、Johannes Jaeger、Neil D. Lawrence、Juan Li、Kuang Lin、Pedro Mendes、Nicholas A. M. Monk、Eric Mjolsness、Manfred Opper、Claudia Rangel、Magnus Rattray、Andreas Ruttor、Guido Sanguinetti、Michalis Titsias、Vladislav Vyshemirsky、David L. Wild、Darren Wilkinson、Guy Yosiphon

《計算分子生物學系列》