Written especially for computer scientists, all necessary biology is explained.

Presents new techniques on gene expression data mining, gene mapping for disease detection, and phylogenetic knowledge discovery.

Statistics is the activity of inferring results about a population given a sample. Historically, statistics books assume an underlying distribution to the data (typically, the normal distribution) and derive results under that assumption. Unfortunately, in real life, one cannot normally be sure of the underlying distribution. For that reason, this book presents a distribution-independent approach to statistics based on a simple computational counting idea called resampling. This book explains the basic concepts of resampling, then system atically presents the standard statistical measures along with programs (in the language Python) to calculate them using resampling, and finally illustrates the use of the measures and programs in a case study. The text uses junior high school algebra and many examples to explain the concepts. Th e ideal reader has mastered at least elementary mathematics, likes to think procedurally, and is comfortable with computers. Table of Contents: The Basic Idea / Pragmatic Considerations when Using Resampling / Terminology / The Essential Stats / Case Study: New Mexico's 2004 Presidential Ballots / References / Bias Corrected Confidence Intervals / Appendix B

The Fourth International Workshop on Database Programming Languages - Object Models and Languages (DBPL-4) took place in Manhattan, New York City, 30 August-1 September 1993. The areas of interest and the format of DBPL-4 focused on the integration of programming languages, object models, type systems and database systems. As in the previous DBPL workshops, the setting was informal, allowing the participants to actively discuss and argue about the ideas presented in the talks. The comments and remarks made by the participants during and after the presentations were taken into account in the preparation of the final versions of the papers. The result, we believe, is a set of excellent papers. The DBPL sequence is closely related to the sequence of International Workshops on Persistent Object Systems (POS), first started in 1985. While the DBPL workshops focus on language and model issues, the POS workshops have focused on implementation issues; thus the two sequences complement each other. Many researchers participate in both workshop series. The eight sessions of the technical program of DBPL-4 were as follows: 1. Bulk types and their query languages (two sessions). 2. Object models and languages. 3. Data types with order. 4. Mechanisms to support persistence, reflection, and extensibility. 5. Query optimization and integrity constraints. 6. Logic-based models. 7. Implementation and performance issues.

Computational analysis of natural science experiments often confronts noisy data due to natural variability in environment or measurement. Drawing conclusions in the face of such noise entails a statistical analysis. Parametric statistical methods assume that the data is a sample from a population that can be characterized by a specific distribution (e.g., a normal distribution). When the assumption is true, parametric approaches can lead to high confidence predictions. However, in many cases particular distribution assumptions do not hold. In that case, assuming a distribution may yield false conclusions. The companion book Statistics is Easy, gave a (nearly) equation-free introduction to nonparametric (i.e., no distribution assumption) statistical methods. The present book applies data preparation, machine learning, and nonparametric statistics to three quite different life science datasets. We provide the code as applied to each dataset in both R and Python 3. We also include exercises for self-study or classroom use.

Inferring gene regulatory networks is a difficult problem to solve due to the relative scarcity of data compared to the potential size of the networks. While researchers have developed techniques to find some of the underlying network structure, there is still no one-size-fits-all algorithm for every data set. Network Inference in Molecular Biology examines the current techniques used by researchers, and provides key insights into which algorithms best fit a collection of data. Through a series of in-depth examples, the book also outlines how to mix-and-match algorithms, in order to create one tailored to a specific data situation. Network Inference in Molecular Biology is intended for advanced-level students and researchers as a reference guide. Practitioners and professionals working in a related field will also find this book valuable.

The goal of this book is to help readers understand state-of-the-art techniques in biological data mining and data management and includes topics such as:

- preprocessing tasks such as data cleaning and data integration as applied to biological data

- classification and clustering techniques for microarrays

- comparison of RNA structures based on string properties and energetics

- discovery of the sequence characteristics of different parts of the genome

- mining of haplotypes to find disease markers

- sequencing of events leading to the folding of a protein

- inference of the subcellular location of protein activity

- classification of chemical compounds based on structure

- special purpose metrics and index structures for phylogenetic applications

- a new query language for protein searching based on the shape of proteins

- very fast indexing schemes for sequences and pathways

Aimed at computer scientists, necessary biology is explained.

This best-selling book is now available in an inexpensive softcover format.
Imagine living during the Renaissance and being able to interview that eras greatest scientists about their inspirations, discoveries, and personal interests. The latter half of our century has seen its own Renaissance - informations technology has changed irrevocable the way we live, work, and think about the world. We are fortunate, therefore, that the authors of Out of Their Minds have been able to talk so candidly with the founders of computer science.

Tuning your database for optimal performance means more than following a few short steps in a vendor-specific guide. For maximum improvement, you need a broad and deep knowledge of basic tuning principles, the ability to gather data in a systematic way, and the skill to make your system run faster. This is an art as well as a science, and "Database Tuning: Principles, Experiments, and Troubleshooting Techniques" will help you develop portable skills that will allow you to tune a wide variety of database systems on a multitude of hardware and operating systems. Further, these skills, combined with the scripts provided for validating results, are exactly what you need to evaluate competing database products and to choose the right one.
* Forward by Jim Gray, with invited chapters by Joe Celko and Alberto Lerner
* Includes industrial contributions by Bill McKenna (RedBrick/Informix), Hany Saleeb (Oracle), Tim Shetler (TimesTen), Judy Smith (Deutsche Bank), and Ron Yorita (IBM)
* Covers the entire system environment: hardware, operating system, transactions, indexes, queries, table design, and application analysis
* Contains experiments (scripts available on the author's site) to help you verify a system's effectiveness in your own environment
* Presents special topics, including data warehousing, Web support, main memory databases, specialized databases, and financial time series
* Describes performance-monitoring techniques that will help you recognize and troubleshoot problems

Dr. Ecco is a mathematical detective and puzzle solver. In this book readers are invited to join him in solving nearly 40 puzzles inspired by methods in computer science and mathematics, including The Tower of Lego, the Odd Doors Problem, Spies and Double Agents, Gossiping Defenders, Code Breaking and many more. No special skills needed, just a clear head and a little imagination. Solutions.