Preface

Bioinformatics is a catalyzer of modern life sciences research. Its development and impact in life sciences is fundamental to understand the scientific progress in the last decades. Bioinformatics fosters the development of computational solutions that facilitate a qualitative and quantitative understanding of life, that is, it supports the interpretation of data coming from life sciences experiments. It is a multidisciplinary area which requires a collaborative effort.

This book describes several of the most important areas in Bioinformatics, grouped into five main sections. In the first section, the importance and relevance of biological networks and their relevance is explained and its potential is exploited in different research areas. The second one describes the latest developments and applications in the active field of next generation sequencing. Since Bioinformatics studies requires the use of high performance computing resources, the third section describes its exploitation in different scenarios. Detailed reviews of molecular modeling and advanced aspects of its application in drug discovery scenarios are described in the fourth section. Exposition of the relevance of structural bioinformatics is described in the fifth section, and the last part of the book shows different studies where the application of intelligent data analysis techniques has been elegantly employed.

The objective of the book is to give a general view of the different areas of Bioinformatics, and each of them both introduces basic concepts and then explains its application to problems of great relevance, so both novice and expert readers can benefit from the information and research works presented here.

> **Dr. Horacio Pérez-Sánchez** Computer Engineering Department, School of Computer Science, University of Murcia, Spain

**Section 1** 

**Analysis of Biological Networks** 

**Section 1** 

**Analysis of Biological Networks** 

**Chapter 1** 

© 2012 Kher et al., licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© 2012 Kher et al., licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

**Hierarchical Biological Pathway** 

Shubhalaxmi Kher, Jianling Peng, Eve Syrkin Wurtele and Julie Dickerson

Biological pathway data is the key resource for biologists worldwide. Interestingly, most of these sources that generate, update, and analyze data are open source. One of the observations that motivated this research work is that, the repositories of data created by a variety of laboratories and research units worldwide represent same pathways with significant details. Generally, if the pathway data has resulted from experimentation, then it is expected that across different resources, under similar conditions, pathways would be exactly identical and biologists may pickup from any source. Interestingly, almost all of the biological data sources refer to data integration of some kind. It may involve rigorous integration mechanisms within the data source and the purpose of integration may change

These efforts in integration may be either local to the source or lack details associated with integration within a pathway, across pathways, or from various data sources etc. Further, the key attributes or design criteria may not be well documented and or may not be readily available to the biologist. In other words, the integration may be achieved as vertical integration (within the data source), or horizontal integration (across data sources). Since most of the extensively integrated data sources (plants or humans) like BioCyc-level-I, Reactome are human curated, it is hard to identify the integration done by the sources like; BioCyc. Also, on a similar note, it may not be apparent to find exactly when the data was

Data in general refers to a collection of results, including the results of experience, observation, or experiment, or a set of premises and can be utilized at the maximum when made available to all in a common format. Different organizations and research laboratories around the world store the data in their own formats; this diversity of data sources is caused due to many factors including lack of coordination among the organizations and research

**Data Integration and Mining** 

Additional information is available at the end of the chapter

the perspective of looking at the integration.

integrated looking at a pathway.

http://dx.doi.org/10.5772/49974

**1. Introduction** 
