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Soft Computing for Biological Systems

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Cover of 'Soft Computing for Biological Systems'

Table of Contents

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    Book Overview
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    Chapter 1 Current Scenario on Application of Computational Tools in Biological Systems
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    Chapter 2 Diagnostic Prediction Based on Gene Expression Profiles and Artificial Neural Networks
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    Chapter 3 Soft Computing Approaches to Extract Biologically Significant Gene Network Modules
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    Chapter 4 A Hybridization of Artificial Bee Colony with Swarming Approach of Bacterial Foraging Optimization for Multiple Sequence Alignment
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    Chapter 5 Construction of Gene Networks Using Expression Profiles
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    Chapter 6 Bioinformatics Tools for Shotgun Metagenomic Data Analysis
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    Chapter 7 Protein-protein Interactions: Basics, Characteristics, and Predictions
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    Chapter 8 Machine Learning Framework: Predicting Protein Structural Features
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    Chapter 9 Drug Transporters as Therapeutic Targets: Computational Models, Challenges, and Future Perspective
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    Chapter 10 Module-Based Knowledge Discovery for Multiple-Cytosine-Variant Methylation Profile
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    Chapter 11 Outlook of Various Soft Computing Data Preprocessing Techniques to Study the Pest Population Dynamics in Integrated Pest Management
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    Chapter 12 Genomics for Oral Cancer Biomarker Research
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    Chapter 13 Soft Computing Methods and Tools for Bacteria DNA Barcoding Data Analysis
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    Chapter 14 Fish DNA Barcoding: A Comprehensive Survey of Bioinformatics Tools and Databases
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    Chapter 15 Microbes and Mountains: The Mid-Domain Effect on Mt. Fuji, Japan
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    Chapter 16 Integration of Soft Computing Approach in Plant Biology and Its Applications in Agriculture
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    Chapter 17 Future Perspectives of Computational Biology: Demanding Shifts in Analytical Thinking to Unfold Biological Complexities
Overall attention for this book and its chapters
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Title
Soft Computing for Biological Systems
Published by
Springer, Singapore, March 2018
DOI 10.1007/978-981-10-7455-4
ISBNs
978-9-81-107454-7, 978-9-81-107455-4
Editors

Hemant J. Purohit, Vipin Chandra Kalia, Ravi Prabhakar More

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 34 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 34 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 24%
Student > Bachelor 6 18%
Student > Postgraduate 4 12%
Researcher 4 12%
Student > Master 3 9%
Other 6 18%
Unknown 3 9%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 8 24%
Agricultural and Biological Sciences 7 21%
Computer Science 4 12%
Medicine and Dentistry 2 6%
Environmental Science 1 3%
Other 4 12%
Unknown 8 24%