Data Mining for Bioinformatics Applications

✍ Scribed by He Zengyou

Publisher: Woodhead Publishing
Year: 2015
Tongue: English
Leaves: 100
Edition: 1
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

Data Mining for Bioinformatics Applications provides valuable information on the data mining methods have been widely used for solving real bioinformatics problems, including problem definition, data collection, data preprocessing, modeling, and validation.

The text uses an example-based method to illustrate how to apply data mining techniques to solve real bioinformatics problems, containing 45 bioinformatics problems that have been investigated in recent research. For each example, the entire data mining process is described, ranging from data preprocessing to modeling and result validation.

Provides valuable information on the data mining methods have been widely used for solving real bioinformatics problems
Uses an example-based method to illustrate how to apply data mining techniques to solve real bioinformatics problems
Contains 45 bioinformatics problems that have been investigated in recent research

📜 SIMILAR VOLUMES

Data Mining for Bioinformatics Applicati

📁 Data Mining for Bioinformatics Applications

✍ He Zengyou 📂 Library 📅 2015 🏛 Woodhead Publishing 🌐 English

Data Mining for Bioinformatics Applications provides valuable information on the data mining methods have been widely used for solving real bioinformatics problems, including problem definition, data collection, data preprocessing, modeling, and validation. The text uses an example-

Data Mining for Geoinformatics: Methods

📁 Data Mining for Geoinformatics: Methods and Applications

✍ Mark Salvador, Ron Resmini (auth.), Guido Cervone, Jessica Lin, Nigel Waters (ed 📂 Library 📅 2014 🏛 Springer-Verlag New York 🌐 English

The rate at which geospatial data is being generated exceeds our computational capabilities to extract patterns for the understanding of a dynamically changing world. Geoinformatics and data mining focuses on the development and implementation of computational algorithms to solve these problems.

Data Mining for Bioinformatics

📁 Data Mining for Bioinformatics

✍ Sumeet Dua, Pradeep Chowriappa 📂 Library 📅 2012 🏛 CRC Press 🌐 English

Covering theory, algorithms, and methodologies, as well as data mining technologies, Data Mining for Bioinformatics provides a comprehensive discussion of data-intensive computations used in data mining with applications in bioinformatics. It supplies a broad, yet in-depth, overv

Multiobjective Genetic Algorithms for Cl

📁 Multiobjective Genetic Algorithms for Clustering: Applications in Data Mining and Bioinformatics

✍ Ujjwal Maulik, Sanghamitra Bandyopadhyay, Anirban Mukhopadhyay (auth.) 📂 Library 📅 2011 🏛 Springer-Verlag Berlin Heidelberg 🌐 English

This is the first book primarily dedicated to clustering using multiobjective genetic algorithms with extensive real-life applications in data mining and bioinformatics. The authors first offer detailed introductions to the relevant techniques – genetic algorithms, multiobjective optimization,

Data Mining in Bioinformatics

📁 Data Mining in Bioinformatics

✍ Jason T. L. Wang, Mohammed J. Zaki, Hannu T. T. Toivonen, Dennis Shasha (auth.), 📂 Library 📅 2005 🏛 Springer-Verlag London 🌐 English

8. 1. 1 Protein Subcellular Location The life sciences have entered the post-genome era where the focus of biologicalresearchhasshiftedfromgenomesequencestoproteinfunctionality. Withwhole-genomedraftsofmouseandhumaninhand,scientistsareputting more and more e?ort into obtaining information about t

Data Mining in Bioinformatics

📁 Data Mining in Bioinformatics

✍ Jason T. L. Wang, Mohammed J. Zaki, Hannu Toivonen, Dennis E. Shasha 📂 Library 📅 2004 🏛 Springer 🌐 English