小分子RNA介导的基因表达调控（导读版） pdf epub mobi txt 电子书 下载 2025

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Rajesh 著

图书标签:

• 小RNA
• 基因表达
• 调控
• RNAi
• miRNA
• 生物化学
• 分子生物学
• 遗传学
• 导读
• 生命科学

出版社： 科学出版社

ISBN：9787030329097

版次：1

商品编码：10937130

包装：精装

开本：16开

出版时间：2012-01-01

页数：432

正文语种：英文

内容简介

内源小分子RNA广泛存在于各种生物中，包括人类、小鼠、果蝇、蠕虫、真菌和细菌等。microRNA作为一种细胞调控关键因子能够修饰基因的表达。在高等真核生物中，microRNA甚至能调控约50%基因的表达。
本书汇集了众多科技工作者的前沿性工作，内容包括从细菌到人类等生物组织中microRNA调控途径的多样性。除了阐述调控小分子RNA的生物合成机制及其加工过程，作者还探讨了这些途径的功能在寄主体内的重要性。
本书围绕小分子RNA这一新发现的调控分子，针对其参与调控的广度与创新性进行了阐述。小分子RNA已经成为研究基因功能的强有力工具，并带来了一系列的重大发现，必将对增进基因功能与疾病治疗的理解带来革命性的改变。

目录

前言
致谢
编者简介
撰稿人
第1章 MicroMining:通过计算方式发现未知的microRNA Adam Grundhoff
第2章 动物microRNA基因预测 Ola Snφve,Pal S*trom
第3章 研究microRNA存在与功能的一系列资源 Praveen Sethupathy,Molly Megraw, Artemis G. Hatzigeorgiou
第4章 大肠杆菌Hfq结合小RNA对mRNA稳定性及翻译的调控 Hiroji Aiba
第5章 动物细胞巾microRNA调控基因表达的机制 Yang Yu,Timothy W. Nilsen
第6章 秀丽隐杆线虫microRNA Mona J. Nolde,Frank J. Slack
第7章 秀丽隐杆线虫小RNA的分离及鉴定 Chisato Ushida, Yusuke Hokii
第8章 MicroRNA与果蝇发育 Utpal Bhadra,Sunit KumarSingh,Singh,S. N. C. V. L. Pushpavalli,Praveensingh B. Hajeri,Manika Pal-Bhadra
第9章 斑马鱼RNA干扰与microRNA Alex S. Flynt,Elizabeth J. Thatcher,James G. Patton
第10章 植物microRNA的产生和功能 Zoltan Havelda
第11章 拟南芥内源小RNA途径 Manu Agarwal,Julien Curaba,Xuemei Chen
第12章 如何评价microRNA表达——技术指导 Mirco Castoldi,Vladimir Benes,Martina U. Muckenthaler
第13章 MicroRNA基因表达定量的方法 Lori A. Neely
第14章 MicroRNA介导的可变剪切调控 Rajesh K. Gaur
第15章 RNA聚合酶Ⅱ介导的内含子microRNA表达系统研究进展 Shi-Lung Lin,Shao-Yao Ying
第16章 基于microRNA的RNA聚合酶Ⅱ表达载体在动物细胞RNA干扰中的应用 Anne B. Vojtek,Kwan-Ho Chung,Paresh D. Patel,David L. Turner
第17章 转基因RNA干扰技术——一种用于哺乳动物反向遗传学研究的快速低成本方法 Linghua Qiu,Zuoshang Xu
第18章 AIDS交响曲——基于microRNA的治疗方法 Yoichi R. Fujii
第19章 MicroRNA与癌症——连点成线 Sumedha D. Jayasena
第20章 哺乳动物巾小RNA介导的转录水平基因沉默 Daniel H. Kim, John J. Rossi
第21章 由RNA介导的转录水平基因沉默控制的基因表达调控 Kevin V. Morris
索引

精彩书摘

1 MicroMining
Computational Approaches
to microRNA Discovery
Adam Grundhoff
Overview....................................................................................
............................1
1.1 Introduction.......................................................................................................2
1.2 When Is a Small RNA an miRNA?...................................................................2
1.3 Advantages and Disadvantages of Experimental versus Computational
miRNA Identification........................................................................................3
1.4 Computational Prediction of miRNAs..............................................................5
1.4.1 Getting Started: Upstream Filtering......................................................7
1.4.2 Following Through: Structure Prediction and Scoring....................... 12
1.4.3 Wrapping It Up: Experimental Validation........................................... 14
1.5 Viral miRNAs................................................................................................. 15
1.6 Conclusions...................................................................................................... 16
References................................................................................................................. 16
Overview
The recent past has seen the rapid identification of thousands of microRNAs
(miRNAs) encoded by various metazoan organisms as well as some viruses, and it
is very likely that many more still await discovery. Most of the hitherto-known miRNAs
have been identified via the cloning and sequencing of small RNAs. While very
powerful, this approach is not without its limitations: especially those miRNAs that
are of low abundance, or which are only expressed in certain cell types or only during
brief periods of organismal development, or are easily missed in cloning-based
screens. Thus, alternative means of miRNA discovery are needed.
Given that the signal that marks the miRNA precursor for the cellular processing
machinery appears to be a relatively simple one (i.e., a hairpin structure), and
considering the rapidly increasing availability of large-scale genomic sequencing
data for many organisms, computational methods appear ideally suited for the comprehensive
identification of hitherto-unknown miRNAs. This chapter discusses the
general principles of computational miRNA identification methods, examines their
advantages and disadvantages as compared to the cloning method, and takes a look
at the various miRNA prediction algorithms that have been developed recently.
1.1 I ntroduction
miRNAs are small (～22 nt) RNA molecules that are able to regulate the expression of
fully or partially complementary mRNA transcripts. As described in greater detail
elsewhere in this book, they are initially transcribed as part of hairpin structures
within much larger precursor transcripts (the so-called primary RNAs or pri-miRNAs).
Following excision of the stem-loops by the RNase III?like enzyme Drosha,
the isolated hairpins (called precursor miRNAs or pre-miRNAs) are exported to
the cytoplasm and further processed by the Dicer complex to produce the mature,
single-stranded miRNA molecule. Recent evidence suggests that plants and animals
encode a multitude of miRNAs, many of which are evolutionarily conserved. As of
this writing, it is still true that the majority of known miRNAs have been identified
experimentally, that is, by cloning of small RNAs. However, this method has certain
limitations, and alternative means for the prediction of novel miRNAs are therefore
increasingly sought.
The observation that pre-miRNAs form characteristic stem-loops has spurred the
development of a number of computational approaches designed to identify novel
miRNA candidates based on the prediction and analysis of secondary structures.
Given the already complete or near-complete sequencing of whole genomes from
many species, such approaches hold great promise for identifying the full complement
of miRNAs encoded by a given organism. However, because the precise set of
structural features that differentiate a pre-miRNA stem-loop from the large number
of hairpins in the genome is not known, additional filters have to be employed to
reduce the number of false-positive predictions, and experimental confirmation of
the remaining candidates is required. In this chapter, I will compare the benefits
and disadvantages of computational miRNA prediction methods in comparison to
the cloning method, review principles of the existing miRNA prediction algorithms,
discuss the general challenges and pitfalls of in silico miRNA identification, and
provide an outlook of what might be expected from these approaches in the future.
Finally, I will consider a special application of the miRNA prediction problem: the
identification of miRNAs in viral genomes.
1.2 W hen is a small RNA an miRNA ?
In order to devise approaches designed to identify miRNAs, be they experimental
or computational, it is important to clearly define what an miRNA is. In a biological
sense, such a definition is quite straightforward: an miRNA is simply a small,
single-stranded regulatory RNA molecule that is generated from its precursor molecules
via successive processing by Drosha and Dicer. It is much more difficult,
however, to define practicable criteria that are readily testable on an experimental
or computational basis and that can unequivocally identify a candidate sequence as
a genuine miRNA. Following the realization that miRNAs represent abundant molecules
expressed in a wide variety of organisms, a consortium of researchers agreed
on a set of criteria that have to be fulfilled before a candidate can be called a bona
fide miRNA.1 According to these guidelines, it is necessary to provide evidence that
(1) the candidate sequence is expressed as an appropriately sized RNA molecule in
living cells and, furthermore, does not stem from random degradation (Expression
criteria), and (2) that the maturation of the candidate involves processing by Drosha
and Dicer (Biogenesis criteria). The expression criteria are preferentially satisfied by
detection of a distinct band of approximately 22 nt on a Northern blot. Alternatively,
the ability to detect the molecule in a library of cloned, size-selected RNAs is considered
sufficient evidence, especially if the library contains high copy numbers of
the particular candidate sequence.
To satisfy the biogenesis criteria, the guidelines by Ambros et al.1 call for experimental
proof of Dicer processing by demonstrating that increased levels of the precursor
accumulate in cells with decreased Dicer expression. In contrast, experimental
proof of Drosha processing is generally not required; instead, it is sufficient to show
that the putative precursor transcript has the capacity to adopt a secondary structure
that is likely to be amenable to Drosha processing. Of course, given the incomplete
knowledge of the rules governing recognition of target mRNAs by Drosha, it is not
known what exactly makes a given RNA structure amenable to Drosha processing,
and (as will be discussed later) this complicates the computational prediction
of miRNA candidates considerably. Based on the characteristics of known miRNA
precursor structures, however, it is generally agreed that the minimal requirements
are (1) the adopted structure is a hairpin that does not contain many or large internal
bulges, and (2) the mature miRNA is to be found within the stem (not the loop) part
of the hairpin.
Evolutionary conservation serves as a third biogenesis criterion: As miRNAs
are often conserved in closely related (and sometimes even in distant) species,
phylogenetic conservation of the miRNA sequence itself as well as its fold-back
structure is considered strong evidence that the candidate sequence represents a
genuine miRNA. An ideal miRNA candidate would meet all of the preceding criteria;
however, it is generally considered sufficient to provide convincing evidence
for at least one criterion out of the two categories. Indeed, because Dicer knockout
cells are not readily available for most organisms, and effective knockdown of
Dicer is technically challenging, positive experimental proof of Dicer processing
is rarely shown.
1.3 A dvantages and Disadvantages of Experimental
versus Computational miRNA Identification
The “traditional” approach to identifying miRNAs consists of cloning of small RNA
moieties. Although several protocols for the efficient cloning of such molecules have
been devised, they all rely on the common principle of ligating linkers to size-fractionated
RNAs, followed by cDNA synthesis and typically PCR amplification. The
obtained products are then either cloned (often after concatamerization to increase
the information obtained in a single-sequence read) and sequenced, or subjected
directly to massive parallel sequencing approaches (“deep sequencing”). According
to the guidelines described earlier, these candidates are then further evaluated to
ensure that the putative pre-miRNA sequence adopts an appropriate hairpin structure
around the candidate. If this is the case, the candidate can generally be considered a
bona fide miRNA, since the recovery of the clone from a small RNA library already
satisfies the expression criterion (nevertheless, Northern blots are often performed to
allow for proper quantification of the miRNA).
The cloning approach has been extremely successful, and although increasing
numbers of miRNAs are being identified via computational means, the majority
of confirmed miRNAs currently listed in the miRNA database (miRBase, http://
microrna.sanger.ac.uk) still have been identified via this method. One of the great
advantages of the cloning protocol is that it provides the precise sequence of the
mature miRNA molecule. Therefore, in contrast to hybridization-based methods,
even closely related miRNAs that differ in only one nucleotide position can be distinguished.
Also, the currently available computational prediction tools generally only
allow identification of miRNA precursors but do not reliably predict the location of
Drosha and Dicer cleavage sites. In contrast, cloning identifies the precise 5′ and 3′
termini of the mature miRNA molecule.
As it appears that nucleotides 2 to 8 of the miRNA (the so-called seed region)
are especially important for target recognition, knowledge of the precise ends (and
particularly the 5′ terminus) is a distinct advantage if a computational prediction
of target transcripts is to be performed. As might be expected, the frequency with
which a given miRNA is cloned often is approximately equivalent to its abundance
(although this frequency may also be affected by other factors; see the following text)
and therefore provides a rough estimate of its expression levels. Thus, abundantly
expressed miRNAs are usually readily identified. However, it can be challenging
to achieve a saturated screen that also captures rare miRNAs. Furthermore, even if
such miRNAs are contained within the library, one can never be entirely certain that
enough clones have been sequenced to identify all of them.
In addition to these constraints, the scope of a cloning screen is also limited by
its source material; naturally, only miRNAs that are expressed in the cells from
which the RNA material was derived can be identified. Many miRNAs, however,
are expressed in a tissue-dependent manner, or are only expressed at certain developmental
stages. This limitation can be partially overcome for relatively simple organisms,
where the RNA can be prepared from whole animals (e.g., mixed larvae stages
and adults from worms or insects).
In organisms with higher complexity such as vertebrates, however, the situation is
more difficult: RNA from different embryonic or adult tissues can be mixed, but the
sensitivity of the screen will dramatically decrease with the complexity of the source
material, and it is very unlikely that nonabundant miRNAs could be identified in
such screens. While these problems could be theoretically solved by massive screening
efforts, that is, performing separate screens with material prepared from every
individual tissue at each developmental stage, the cloning approach also appears
limited in a more fundamental way. Several observations suggest that some miRNAs
are more readily cloned than others owing to intrinsic properties such as sequence
composition, the presence of certain nucleotides at their termini, or posttranscriptional
modifications such as methylation or RNA editing.2?6
Computational approaches to miRNA discovery are not subject to many of the
limitations that apply to the cloning method. Certainly, one of the biggest advantages
of computational miRNA identification is the universal scope of the analysis; as the
prediction does not require experimental material, it can potentially discover all of
the miRNAs encoded by a given organism, even those that are expressed only at
very low levels, in rare cells, or during brief periods of development. However, this
advantage is partially annulled by the insufficient precision of the presently available
algorithms: as the programs (to varying degrees) produce large numbers of falsepositive
predictions, experimental verification is still a necessity. Northern blotting is
frequently performed to investigate the expression of the computationally predicted
candidates, or the predicted sequences are amplified from small RNA libraries.
These procedures are not particularly compatible with high-throughput screening,
and since many computational methods produce large numbers of candidates, only a
small contingent of the predictions is usually subjected to experimental verification,
whereas the majority remains untested. More importantly, the experimental validation
methods are subject to many of the same limitations that hamper the cloning
approach. Thus, even if an experimental verification is attempted and fails, it is often
impossible to decide whether the failure was due to a false-positive prediction, insufficient
sensitivity of the experimental detection method, or lack of expression in the
tested tissue or cell line.
It is thus perhaps not surprising that the expression criterion has not been satisfied
for most computationally predicted miRNA candidates. While some groups have
attempted to reconcile these difficulties by developing expression analysis tools that
are, for example, more sensitive or allow high-throughput screening, there is also
tremendous effort to increase the reliability of computational prediction methods
such that experimental confirmation is becoming less important.
1.4 Computational Prediction of miRNA s
A plethora of computational approaches aimed at the prediction of miRNAs have
been devised, and although nearly all of them use the evaluation of features that are
thought to be characteristic for miRNAs in order to identify novel candidates, they
vary significantly in scope, complexity, and level of sophistication of the underlying
algorithms. Some approaches strive to identify the totality of miRNAs encoded by a
given organism, whereas others aim to identify only miRNAs that represent closely
related ortho- or paralogs of those that are already known. Some programs investigate
some of the largest genomes, those of mammals, whereas others consider only
some of the smallest, those of viruses.
Despite these differences, most of the approaches function according to a common
scheme that might be abstracted as follows. First, a pool of input sequences
(usually representing the complete genome of a given organism) is filtered in order to
limit the number of candidates that have to be evaluated by downstream algorithms.
I will refer to this process as upstream filtering in the following. The filtered pool is
then subjected to a structure prediction. The obtained structures are then compared
to those of known pre-miRNAs, and a score calculation is performed, depending on
the degree of similarity. Finally, experimental validation is attempted, usually for a
selection of the highest-scoring candidates.
There are considerable differences in the degree to which structural features are
investigated during the scoring step; sometimes the filter might simply ensure that the
candidate forms a hairpin structure, whereas in other cases it might investigate the
candidate’s structure down to the minutest detail. The level of sophistication, in large
part, will depend on the design of the upstream filter and the efficiency with which
this filter preselects a set of candidates enriched for genuine miRNAs. For example,
phylogenetic conservation is the most widely used upstream filter (and at least presently,
it is also appears to be the most efficient). Indeed, if the sequence of a known
mature miRNA is perfectly conserved in a closely (or even distantly) related species,
a relatively simple structural analysis that shows that the ability of the surrounding
sequences to adopt a fold-back structure is conserved as well might suffice.
In contrast, an ab initio prediction method in which the upstream filter is minimal
will require a much more detailed structural analysis during the downstream scoring
step. Thus, a highly efficient upstream filter requires a less elaborate downstream
structure evaluation, and vice versa. The cloning method might be considered a special
case of this scheme in which the upstream filtering is based on an experimental
procedure; since this method produces only little background, the subsequent structural
investigation can be minimal.
All of the available computational approaches are subject to the production
of false-positive (i.e., candidates that pass the filters but do not represent genuine
miRNAs) and false-negative predictions (i.e., bona fide miRNAs that are rejected
during the upstream filtering or the downstream scoring step). The ratio with which
true-positive versus false-positive predictions are made will determine the algorithm’s
accuracy, while the ratio of true-positive versus false-negative predictions
will determine its sensitivity. Such rates are frequently estimated in order to judge an
algorithm’s performance.
Estimating the rate of false-negative predictions is a relatively straightforward
process. Often, only a limited number of the contingent of known miRNAs is used to
establish the parameters of the filtering and scoring algorithms. The remaining miRNAs
are then subjected to the prediction procedure, and the number of rejected versus
retained miRNAs is determined. Alternatively, the full complement of miRNAs
is repeatedly passed through the filters, and the method parameters are adjusted
until an acceptable ratio between rejected and accepted miRNAs is achieved (what
exactly an acceptable ratio is will greatly vary with the overall design and scope of
the method).
The estimation of false-positive prediction rates is a more complicated matter:
in order to measure such numbers with high reliability, one ideally would
have a set of sequences that assuredly does not contain any miRNAs at all, or a
set in which all of the genuine miRNAs are known beforehand. In theory, such
a set can be created artificially from randomly generated sequences, or by shuffling
naturally occurring ones, but since biological sequences are nonrandom, such
a reference set would be hardly representative of the experimental sequence set.
Alternatively, one might select genetic elements that have known functions and
are thus unlikely to additionally represent miRNAs, but this would reduce the
complexity of the reference set so drastically that the gained information would be
close to meaningless.
In reality, the rate of false-positive predictions is often estimated on an experimental
basis. For this purpose, a representative subset of the predictions (or all of

前言/序言

好的，这是一份关于不同主题的图书简介，完全不涉及“小分子RNA介导的基因表达调控（导读版）”的内容，旨在提供详实且具有专业深度的阅读指引。 --- 图书简介：复杂系统动力学与全球气候建模 第一部分：复杂系统动力学的理论基石与数学框架 书名：非线性涌现：从统计物理到适应性复杂系统的数学基础 内容导读： 本书深入探讨了复杂系统理论的核心支柱，聚焦于从微观相互作用中如何涌现出宏观有序和不规则现象。全书以严谨的数学语言为基础，构建了一套理解和量化复杂性（Complexity）的分析工具集。 第一章：统计物理学的遗产与限制 详细回顾了玻尔兹曼统计、吉布斯系综理论及其在平衡态系统中的强大预测能力。重点讨论了如何利用这些基础概念来理解相变现象，特别是临界指数的普适性问题。随后，我们批判性地分析了标准统计力学在处理远离平衡态（Out-of-Equilibrium）系统时的局限性，为引入非平衡态动力学奠定基础。 第二章：动力系统理论的拓扑学视角 本章聚焦于连续和离散动力系统的定性分析。从洛伦兹吸引子（Lorenz Attractor）的经典案例出发，深入讲解了李雅普诺夫稳定性分析、分岔理论（Bifurcation Theory）以及混沌的数学定义——如拓扑熵和费根鲍姆常数。我们详细阐述了庞加莱截面法在降维分析高维流形上的应用，并引入了随机微分方程（SDEs）来描述具有噪声驱动的系统演化路径。 第三章：信息论与复杂性度量 复杂系统本质上是信息处理系统。本章引入了香农信息论，并将其扩展至算法信息论（Kolmogorov Complexity）和有效复杂性（Effective Complexity）的概念。重点探讨了互信息（Mutual Information）和转移熵（Transfer Entropy）在量化系统各组成部分间信息流和因果关系中的应用。通过分析网络的结构熵，读者将掌握评估系统组织程度和信息处理效率的定量指标。 第四章：图论与网络科学的几何结构 图论是描述复杂系统的语言。本书系统梳理了网络科学的经典模型，如ER随机图、无标度网络（Scale-Free Networks，Barabási-Albert模型）和小型世界网络（Small-World Networks，Watts-Strogatz模型）。我们不仅关注拓扑属性（如度分布、聚类系数、平均路径长度），更深入到网络的嵌入几何——即网络如何在内在度量空间中被感知，以及如何利用谱图理论（Spectral Graph Theory）揭示网络的模块化结构和关键节点的识别方法。 --- 第二部分：全球气候建模：耦合过程与长期预测 书名：地球系统：能量平衡、水文循环与气候反馈的数值模拟 内容导读： 本书旨在为读者提供一个关于现代全球气候模型（GCMs）构建原理、核心物理过程参数化以及不确定性分析的全面概述。它是一本连接基础地球物理学与前沿计算科学的桥梁之作。 第一章：大气动力学与流体静力学平衡 本章从纳维-斯托克斯方程（Navier-Stokes Equations）出发，推导了地球流体运动的控制方程，包括科里奥利力项和地转风平衡。详细讨论了大气环流模式（如哈德里、费雷尔和极地环流）的形成机制，并重点分析了边界层过程对能量和动量传输的贡献。 第二章：辐射传输与能量收支 气候变化的核心在于能量失衡。本章深入剖析了大气、海洋和地表间的短波和长波辐射过程。我们详细阐述了辐射传输方程的求解，重点关注温室气体（如水汽、二氧化碳）的吸收谱线、云对辐射的强迫作用（Cloud Radiative Forcing）及其在模型中的参数化方法，这是当前气候建模面临的最大挑战之一。 第三章：海洋环流与热力学耦合 海洋是地球气候系统的主要热量“惯性器”。本章侧重于大尺度海洋环流，包括热盐环流（Thermohaline Circulation）的驱动机制，如深水形成区的关键性作用。探讨了海气界面的动量、热量和水汽通量计算，以及如何将海洋混合层和深海环流整合到高分辨率耦合气候模型（AOGCMs）中。 第四章：水文循环与冰雪圈的反馈机制 降水、蒸发和径流过程的准确模拟对区域气候预测至关重要。本章详述了地表过程模型（Land Surface Models, LSMs），包括土壤湿度、植被覆盖对能量平衡的反作用。此外，还全面覆盖了冰雪圈（Cryosphere）对气候系统的反馈：海冰反照率效应、冰盖消融对海平面上升的贡献，以及永久冻土融化释放温室气体的潜在正反馈循环。 第五章：模型评估、敏感性测试与预测不确定性 现代气候模型并非完美，本章引导读者理解模型的验证方法，包括与古气候记录和再分析资料的对比。重点讲解了集合预测系统（Ensemble Prediction Systems）的构建，以及如何通过皮沙诺概率（P-value）和概率密度函数来量化气候敏感性（Climate Sensitivity）的不确定性区间，从而为政策制定提供基于风险的科学依据。 --- 第三部分：前沿计算方法：高维数据降维与机器学习在模拟中的应用 书名：从拉格朗日点到高维嵌入：时空数据的特征提取与预测 内容导读： 本书聚焦于处理和解析大规模、高维、非平稳时间序列数据（如气候模拟输出或金融市场数据）所必需的先进计算技术。它为数据科学家和物理学家提供了从海量数据中提取物理意义的实用工具箱。 第一章：特征提取的基础：经验正交函数（EOF）与主成分分析（PCA） 详尽阐述了EOF分解在气候学中用于识别主导模态（Dominant Modes）的方法。讨论了其与PCA的数学关系，并深入分析了EOF分析中存在的旋转问题（Rotational Ambiguity）及其解决策略，如最大协方差分析（MCA）在耦合系统分析中的优势。 第二章：非线性降维：流形学习与动力系统重构 面对混沌和非线性系统的内在复杂性，本章引入了流形学习技术。重点介绍局部线性嵌入（LLE）和t-SNE，展示它们如何将高维观测数据映射到低维“本征流形”上，以揭示系统的潜在动力学结构。此外，详细讨论了滞后嵌入（Time-Delay Embedding）技术，用于从单变量时间序列中重构系统的吸引子。 第三章：数据驱动的模式识别：深度学习基础 本章为深度学习在复杂系统分析中的应用奠定了基础。涵盖了卷积神经网络（CNN）在识别空间模式（如大气阻塞事件）中的应用，以及循环神经网络（RNN）及其变体（如LSTM和GRU）在处理长期依赖时间序列预测中的优势。 第四章：因果发现与物理约束下的机器学习 机器学习模型必须尊重物理定律。本章探讨了物理信息神经网络（PINNs）的设计哲学，即如何将偏微分方程（PDEs）作为损失函数的一部分嵌入到训练过程中，以确保模型预测不仅拟合数据，同时也保持物理一致性。此外，引入了格兰杰因果关系（Granger Causality）的现代检验方法，用于在多变量数据中明确区分相关性与真正的驱动力。

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《小分子RNA介导的基因表达调控（导读版）》这本书，当我第一次拿到它的时候，就有一种“相见恨晚”的感觉。虽然我不是生物学专业的，但一直对生命科学的精妙之处充满兴趣。这本书恰好满足了我这种渴望，用一种非常人性化、非常接地气的方式，将小分子RNA这个在基因调控中扮演着核心角色的领域呈现在我面前。书的开篇并没有直接抛出晦涩难懂的概念，而是从生物体如何通过调控基因表达来适应环境、维持生命活动这样一个更宏大的视角切入，让我迅速对基因调控的重要性有了深刻的认识。接着，作者巧妙地将话题引向了小分子RNA，并用生动形象的语言，将miRNA、siRNA等不同的分子“拟人化”，仿佛它们是细胞内部的“特工”或者“信使”，在精密的网络中传递着信息，调控着基因的“开关”。我尤其欣赏书中对小分子RNA作用机制的阐述，比如它们如何识别靶向的mRNA，如何影响蛋白质的合成，这些复杂的生化过程，在作者的笔下变得清晰而有趣，就像在观看一部精彩的科幻电影。

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说实话，看到《小分子RNA介导的基因表达调控（导读版）》这个书名的时候，我其实有点犹豫。我不是生物学专业的，担心这本书会过于专业，读起来会很吃力。但抱着试一试的心态翻开了它，却惊喜地发现，这完全是一本为我这样的读者量身打造的书。作者的叙述方式非常清晰流畅，逻辑性也很强。它并没有一开始就铺天盖地地介绍各种专业名词，而是从一个更广泛的背景入手，比如生命体是如何通过控制基因的开关来维持自身正常运作的。然后，才慢慢引出小分子RNA这个在基因调控中扮演着重要角色的“新星”。我尤其喜欢它对小分子RNA的“家族史”的介绍，以及它们如何从最初被发现到如今成为生物学研究的热点。书中对miRNA和siRNA等不同小分子RNA功能的阐述，也十分到位，用非常形象的比喻，比如把它们比作“基因的沉默者”或者“精密的微调器”，让我这个非专业人士也能轻松理解它们在细胞中的关键作用。整本书读下来，感觉就像是在听一位经验丰富的科学家，用最易懂的语言，为我揭示细胞内部一场精彩绝伦的“基因调控大战”。

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拿到《小分子RNA介导的基因表达调控（导读版）》这本书，我最大的感受就是“原来科学可以这么有趣！”。我一直对生命科学的奥秘充满好奇，但很多时候，科学著作的专业性和枯燥性会让人望而却步。然而，这本书却颠覆了我的这种刻板印象。作者在开篇就用非常引人入胜的语言，将基因表达调控的重要性娓娓道来，让我立刻被吸引住了。它并没有直接深入到复杂的分子机制，而是先为读者构建了一个宏观的认知框架。我尤其喜欢它对小分子RNA这个“主角”的描绘，仿佛它们是细胞内部无声的“指挥官”，精准地调控着每一个基因的“工作状态”。书中对miRNA和siRNA如何影响基因表达的解释，图文并茂，通俗易懂，让我这个生物学领域的“小白”也能看得津津有味。它不仅仅是知识的堆砌，更是一种思维方式的引导，让我开始用更宏观、更系统的角度去看待生命现象。这本书就像一扇窗，透过它，我看到了一个微观而又宏大的生命世界，充满了精妙的设计和无穷的奥秘。

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当我打开这本书，脑海中闪过的第一个念头就是：“这下可算有得看了！”我一直对基因调控这个话题很感兴趣，但市面上很多相关的书籍要么过于学术，要么太浅显，难以满足我深入了解的需求。这本《小分子RNA介导的基因表达调控（导读版）》恰好填补了这个空白。虽然它是“导读版”，但内容之充实，让我着实吃了一惊。它从宏观上介绍了基因表达调控的整体框架，然后聚焦到小分子RNA这个“主角”上，详细阐述了它们的种类、生成机制以及在不同生物过程中的作用。我特别欣赏书中对不同类型小分子RNA（比如miRNA和siRNA）作用机制的深入剖析，图文并茂地展示了它们如何与靶基因相互作用，进而影响蛋白质的合成。其中关于miRNA调控基因表达的“种子区域”假说，以及siRNA的RNA干扰（RNAi）通路，在我看来简直是精彩绝伦的章节，让我对这些微小的分子如何发挥如此巨大的调控作用有了更深刻的认识。这本书的讲解方式，就像在带领我进行一次细胞内的“探险”，每一次翻页，都可能发现一个关于生命运作的新奇之处。它没有回避复杂的技术细节，但又不会让读者感到 overwhelming，反而能激发进一步探索的兴趣。

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初拿到这本《小分子RNA介导的基因表达调控（导读版）》，我原本以为这是一本硬核的学术专著，光是书名就透着一股子专业和严谨。但翻开目录，才发现它远比我想象的要亲民。开篇的章节，虽然涉及到了分子生物学的基本概念，但作者用一种非常生动形象的方式来解释，比如把基因比作“生命蓝图”，把蛋白质比作“建筑工人”，一下子就把复杂的概念变得容易理解。我尤其喜欢它对小分子RNA“身份”的描述，不仅仅是简单的RNA序列，更是细胞内部精密的“信使”和“调控员”，它们是如何悄无声息地影响着基因的“开关”，从而 orchestrate 整个细胞的活动，这部分简直让我惊叹于生命的神奇。虽然我并非此领域的专家，但阅读过程中，我感觉自己就像一个初入科学殿堂的学生，在一位循循善诱的老师的带领下，一步步揭开生命的奥秘。它没有一开始就抛出晦涩难懂的专业术语，而是循序渐进，让我在不知不觉中吸收了大量知识。即使是像miRNA、siRNA这样的专业词汇，在书中也被赋予了易于理解的“角色”，仿佛它们是舞台上的演员，在基因调控的戏剧中扮演着至关重要的角色。这种“导读版”的诚意，体现在每一个章节的编排和语言的选择上，它成功地架起了普通读者与前沿科学之间的桥梁，让我这个对生物学仅有浅薄了解的人，也能感受到这项研究的魅力与重要性。

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有点过时了，只综述了2006年以前的，目录是中文，内容是英文的，图解比较清晰。本书汇集了众多科技工作者的前沿性工作，内容包括从细菌到人类等生物组织中microRNA调控途径的多样性。除了阐述调控小分子RNA的生物合成机制及其加工过程，作者还探讨了这些途径的功能在寄主体内的重要性。

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本书围绕小分子RNA这一新发现的调控分子，针对其参与调控的广度与创新性进行了阐述。小分子RNA已经成为研究基因功能的强有力工具，并带来了一系列的重大发现，必将对增进基因功能与疾病治疗的理解带来革命性的改变。

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本书介绍了sRNA在基因表达调控中的作用的几个方面，内容很多，具有一定参考价值；缺点，缺少系统性。

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本书围绕小分子RNA这一新发现的调控分子，针对其参与调控的广度与创新性进行了阐述。小分子RNA已经成为研究基因功能的强有力工具，并带来了一系列的重大发现，必将对增进基因功能与疾病治疗的理解带来革命性的改变。

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是全英文版，原书出版于2009年，内容较新。纸张不错，印刷字迹有些淡，装帧很好，值得专业人士购买、阅读和收藏。

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内容很丰富，对实验很有指导作用

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准备好好阅读，等有点心得再追加评论吧。

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此书的内容排版非常好 书内容也很全面 赞！

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内容很丰富，对实验很有指导作用