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关键词： C语言  

ISBN: (纸本)9787121401183

摘要： 本书包含了最新的研究生入学统考大纲的全部内容。本书系统地介绍了线性表、堆栈、队列、串、数组、广义表、树、二叉树、图等典型数据结构，以及递归、查找和排序的方法。本书理论叙述简洁准确、实践应用举例丰富完整，从而达到了理论和实践密切结合的目的。本书采用C语言描述算法并提供了大量上机调试通过的程序实例。

关键词： 数据结构   算法分析  

ISBN: (纸本)9787121403675

摘要： 数据结构是高等学校计算机及其相关专业的核心课程，是计算机程序设计的基础。本书按照“像外行一样思考，像专家一样实践”的解决问题的思维方法，基于学习者的认知规律，列举大量实际或工程案例，从具体问题中引出抽象概念，运用类比、图形化描述等方式，对经典数据结构内容做深入浅出的介绍。在介绍数据结构和算法的基本概念和算法分析方法的基础上，从软件开发的角度，通过应用背景或知识背景介绍、数据分析、函数设计、算法设计、测试调试等环节，分别对顺序表、链表、栈、队列、串、数组、树、图等基本类型的数据结构进行了分析和讨论；介绍数据的典型操作方法，如数据排序方法和查找方法；介绍常见的如递归、分治法、动态规划、贪心法等经典算法。

关键词： 数据结构   算法分析  

ISBN: (纸本)9787563563272

摘要： 本书分为3篇，第1篇是习题和讲解，按照《数据结构与算法》教材的1-7章，分别撰写了每一章节的课程MAP、扩展学习、课后习题讲解和练习题，并附综合试卷5套，方便读者对学习的程度进行自我考查。第2篇是实验，根据课程内容设计了6章对应的实验，并针对典型实验进行了讲解和实现。第3篇是课程设计，也是本书的特色，讲解了数据结构知识在实际问题上的应用方法和范例。

关键词： 数据结构  

ISBN: (纸本)9787302586623

摘要： 本书按照清华大学计算机系本科”数据结构”大纲的要求, 从面向对象的概念、对象类设计的风格和数据结构的层次开始。从线性结构到非线性结构, 从简单到复杂, 循序渐进, 逐步深入地讨论了各种数据结构的内在的逻辑关系及它们在计算机中的实现方式和使用。此外, 对常用的迭代、递归、回溯等算法设计技巧, 搜索和排序算法等都做了详尽的描述, 并引入了简单的算法分析。全书采用了面向对象的观点讨论数据结构技术, 并以兼有面向过程和面向对象双重特色的C++语言作为算法的描述工具, 强化基本知识和基本能力的双基训练。

摘要： High-throughput sequencing data sets are usually deposited in public repositories (e.g., the European Nucleotide Archive) to ensure reproducibility. As the amount of data has reached petabyte scale, repositories do not allow one to perform online sequence searches, yet, such a feature would be highly useful to investigators. Toward this goal, in the last few years several computational approaches have been introduced to index and query large collections of data sets. Here, we propose an accessible survey of these approaches, which are generally based on representing data sets as sets of k-mers. We review their properties, introduce a classification, and present their general intuition. We summarize their performance and highlight their current strengths and limitations.

摘要： Interest in drug development for rare diseases has expanded dramatically since the Orphan Drug Act was passed in 1983, with 40% of new drug approvals in 2019 targeting orphan indications. However, limited quantitative understanding of natural history and disease progression hinders progress and increases the risks associated with rare disease drug development. Use of international data standards can assist in data harmonization and enable data exchange, integration into larger datasets, and a quantitative understanding of disease natural history. The US Food and Drug Administration (FDA) requires the use of Clinical Data Interchange Consortium (CDISC) Standards in new drug submissions to help the agency efficiently and effectively receive, process, review, and archive submissions, as well as to help integrate data to answer research questions. Such databases have been at the core of biomarker qualification efforts and fit-for-purpose models endorsed by the regulators. We describe the development of CDISC therapeutic area user guides for Duchenne muscular dystrophy and Huntington's disease through Critical Path Institute consortia. These guides describe formalized data structures and controlled terminology to map and integrate data from different sources. This will result in increased standardization of data collection and allow integration and comparison of data from multiple studies. Integration of multiple data sets enables a quantitative understanding of disease progression, which can help overcome common challenges in clinical trial design in these and other rare diseases. Ultimately, clinical data standardization will lead to a faster path to regulatory approval of urgently needed new therapies for patients.

关键词： 数据结构   C语言  

ISBN: (纸本)9787302573975

摘要： 本书基于作者多年的教学经验, 从实用的角度出发, 对线性和非线性数据结构的顺序和链式存储及其操作进行了详细讲解, 在教给学生数据结构设计和算法设计的同时, 培养学生分析问题、解决问题和总结问题的能力。书中的每一章均配有实战练习及大量习题, 实现了理论与实践相结合, 让学生学以致用。本书免费提供电子课件、源代码及习题答案, 全部案例均已在Visual C++ 6.0环境中成功运行。

关键词： 数据结构   算法分析  

ISBN: (纸本)9787560659749

摘要： 本书以高级程序设计能力的培养为目标, 介绍数据结构和算法设计的相关知识, 帮助读者针对实际应用, 选择合适的数据结构并设计相应算法。全书分为两部分, 第一部分讨论了软件设计规范及程序设计的关键技术, 并从数据的逻辑结构、存储结构和运算实现角度介绍了常见的数据结构及典型应用, 涵盖了线性表、栈、队列、串、树、图等结构, 以及索引结构和散列技术, 该部分在介绍知识点的同时, 通过具体实例的分析和设计, 帮助读者更深刻地理解所学知识, 循序渐进培养学生设计复杂程序的能力。第二部分介绍了常用的经典算法, 如分治策略、动态规划、贪心策略、回溯法、分支界限法等, 还介绍了软件设计中一些常用的排序和查找算法。书中每章后均附有习题, 其中的基本概念题提供参考答案, 部分算法设计题附带分析和解析, 供读者参考。本书对部分算法提供了微课视频, 其动画效果的演示有助于读者理解书中的重点和难点。

关键词： 数据结构  

ISBN: (纸本)9787121402616

摘要： 本书是作者积多年讲授与研究数据结构课程并结合指导学生上机实践经验基础上编写而成。作者力求通过实践的角度，帮助学生深入学习、理解和掌握数据结构知识并能灵活应用数据结构知识。全书的实践部分涵盖了数据结构课程的全部内容，对数据结构所有的理论知识均对应给出了程序实现，并且这些程序都在VC++6.0环境下调试通过。

摘要： Causal inference analysis is one of the most significant and well researched topics in the analysis of observational studies. It addresses the challenge of estimating the relationship between the treatment of interest and the outcome variable (i.e., the treatment effect) in the presence of background covariates. This study aims to determine how causal inference in observational studies can be extended to cover data sets where post treatment variables exist, cluster level covariates exist, or the number of observations in the treatment groups is very unbalanced. More specifically, this study can be summarized into the following two sub-topics.1. The first part of this study focuses on estimating the treatment effect when both the post-treatment variable and cluster level variables exist in the data sets. In previous literature, one popular method, called, the principal stratification method, can properly handle the problem with a post-treatment variable to reach an unbiased treatment effect estimate. The traditional method to consider the cluster level effect is to include the cluster labels as random effects in the model. When either the size of clusters is large, or each cluster only contains small number of observations, we find this method often results in poor estimates of the treatment effect.2. The second part of this study focuses on estimating the treatment effect estimation when the number of observations is very unbalanced in the two treatment groups (i.e., the treatment or control is a rare event) and background covariates and cluster effects exist in the data sets. Propensity score analysis is the traditional method to adjust for the covariate imbalance be-tween the treated and control groups. The logit or probit links are often used in the propensity score for balancing the background covariates between the treatment groups. When there is severe imbalance between the treatments, we find this method often results in poor estimates of the treatment effect