Computational intelligence and pattern analysis in biology informatics - Info and Reading Options
By Ujjwal Maulik
"Computational intelligence and pattern analysis in biology informatics" was published by John Wiley & Sons in 2010 - Hoboken, N.J and the language of the book is English.
“Computational intelligence and pattern analysis in biology informatics” Metadata:
- Title: ➤ Computational intelligence and pattern analysis in biology informatics
- Author: Ujjwal Maulik
- Language: English
- Publisher: John Wiley & Sons
- Publish Date: 2010
- Publish Location: Hoboken, N.J
“Computational intelligence and pattern analysis in biology informatics” Subjects and Themes:
- Subjects: ➤ Gene Expression Profiling - Computational Biology - Pattern recognition systems - Computational biology - Automated Pattern Recognition - Artificial Intelligence - Bioinformatics - Artificial intelligence - Computational intelligence
Edition Specifications:
- Pagination: p. ;
Edition Identifiers:
- The Open Library ID: OL24422601M - OL15455531W
- Online Computer Library Center (OCLC) ID: 495781594
- Library of Congress Control Number (LCCN): 2010028353
- ISBN-13: 9780470581599
- All ISBNs: 9780470581599
AI-generated Review of “Computational intelligence and pattern analysis in biology informatics”:
"Computational intelligence and pattern analysis in biology informatics" Table Of Contents:
- 1- (Publisher-supplied data) Part I: Introduction
- 2- Computational Intelligence: Basic Principles, Issues and Challenges / Swagatam Das, Ajith Abraham, and B. K. Panigrahi
- 3- Fundamentals of Pattern Analysis Techniques / Basabi Chakraborty
- 4- Biological Informatics: Methods, Tools and Applications / Kevin Byron, Miguel Cervantes-Cervantes and Jason T. L. Wang
- 5- Part II: Sequence Analysis
- 6- A Neuro fuzzy approach for Promoter Identification and Gene finding / T. Sobha Rani, S. Durga Bhavani and S. Bapi Raju
- 7- Predicting micro RNA Prostate Cancer Target Genes / F. Masulli, S. Rovetta and G. Russo
- 8- Part III: Structure Analysis
- 9- Structural search in RNA Motif Databases Dongrong Wen and Jason T. L. Wang
- 10- Kernels on Protein Structures / Sourangshu Bhattacharya, Chiranjib Bhattacharyya and Nagasuma R. Chandra
- 11- Characterization of conformational patterns in active and inactive forms of kinases using protein blocks approach / Garima Agarwal, Dhurvas Chandrasekaran Dinesh, Narayanaswamy Srinivasan, Alexandre G. de Brevern
- 12- Kernel function for graph classification / Aaron Smalter and Jun Huan
- 13- In-Silico Drug Design Using a Computational Intelligence Technique / Soumi Sengupta, S. Bandyopadhyay
- 14- Part IV: Microarray Data Analysis
- 15- Microarray analysis by fuzzy DE based clustering / Indrajit Saha and Ujjwal Maulik
- 16- Cancer Classification from Gene Expression Microarray Data using Multiobjective supervised learning / Anirban Mukhopadhyay, Ujjwal Maulik and Sanghamitra Bandyopadhyay
- 17- Parallel Algorithms for Clustering Gene Expression Data / Ujjwal Maulik and Anasua Sarker
- 18- Part V: Systems Biology
- 19- Techniques for prioritization of possible disease genes / Jieun Jeong and Jake Y. Chen
- 20- Prediction of Protein Protein Interactions / Angshuman Bagchi
- 21- Analyzing Topological Properties of Protein-protein Interaction Networks: A Perspective Towards Systems Biology / Malay Bhattacharyya and Sanghamitra Bandyopadhyay.
"Computational intelligence and pattern analysis in biology informatics" Description:
The Open Library:
"Computational Intelligence (CI) is a successor of artificial intelligence combining elements of learning, adaptation, evolution and logic to create programs that are, in some sense, intelligent. CI exhibits an ability to learn or deal with new situations, such that the system is perceived to possess one or more attributes of reason. The different methodologies in CI work synergistically and provide, in one form or another, flexible information processing capabilities for handling a huge volume of real life data with noises, ambiguity, and missing values. Solving problems often involves search for useful regularities or patterns in large amounts of data. A typical characteristic of biological data is high dimensionality with low sample size. This poses grand challenges to traditional pattern analysis techniques, necessitating the development of sophisticated approaches--Provided by publisher.
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