Libros UCLV { BETA }

The book is aimed at both students and more advanced researchers, with diverse backgrounds. We have tried to provide a succinct description of the main biological concepts and problems for the readers with a stronger background in mathematics, statistics, and computer science. Likewise, the book is tailored to the biologists and biochemists who will often know more about the biological problems than the text explains, but need some help to understand the new data-driven algorithms, in the context of biological data. It should in principle provide enough insights while remaining sufficiently simple for the reader to be able to implement the algorithms described, or adapt them to a particular problem. The book, however, does not cover the informatics needed for the management of large databases and sequencing projects, or the processing of raw fluorescence data. The technical prerequisites for the book are basic calculus, algebra, and discrete probability theory, at the level of an undergraduate course. Any prior knowledge of DNA, RNA, and proteins is of course helpful, but not required.

This project presents demonstrations of selected computer science algorithms important in bioinformatics, implemented in the spreadsheet program Microsoft Excel. Spreadsheets provide an interesting platform for demonstration of algorithms, since various steps of the calculations can be exposed in a manner that is easily comprehensible to users with little programming experience. The algorithms demonstrated include two approaches to approximate string matching (dynamic programming and Shift-AND numeric approximate matching), Hierarchical Clustering (used in phylogenetic studies and microarray analysis of gene expression), a Naive Bayes Classifier for simulated microarray gene expression data, and a simple Neural Network. These demonstrations are designed to serve as instructional aids in bioinformatics courses