Emerging Trends in Computational Biology, Bioinformatics, and Systems Biology - 1st Edition - ISBN: 9780128025086, 9780128026465

Emerging Trends in Computational Biology, Bioinformatics, and Systems Biology

1st Edition

Algorithms and Software Tools

Authors: Hamid Arabnia Quoc Nam Tran
eBook ISBN: 9780128026465
Paperback ISBN: 9780128025086
Imprint: Morgan Kaufmann
Published Date: 7th August 2015
Page Count: 670
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Table of Contents

  • Preface
  • Acknowledgments
  • Introduction
  • Chapter 1: Supervised Learning with the Artificial Neural Networks Algorithm for Modeling Immune Cell Differentiation
    • Abstract
    • Acknowledgments
    • 1 Introduction
    • 2 Related work
    • 3 Modeling immune cell differentiation
    • 4 Discussion
    • 5 Conclusion
  • Chapter 2: Accelerating Techniques for Particle Filter Implementations on FPGA
    • Abstract
    • 1 Introduction
    • 2 PF and SLAM algorithms
    • 3 Computational bottleneck identification and hardware/software partitioning
    • 4 PF acceleration techniques
    • 5 Hardware implementation
    • 6 Hardware/software Architecture
    • 7 Results and discussion
    • 8 Conclusions
  • Chapter 3: Biological Study on Pulsatile Flow of Herschel-Bulkley Fluid in Tapered Blood Vessels
    • Abstract
    • 1 Introduction
    • 2 Formulation of the problem
    • 3 Solution
    • 4 Discussion
    • 5 Conclusion
  • Chapter 4: Hierarchical k-Means: A Hybrid Clustering Algorithm and Its Application to Study Gene Expression in Lung Adenocarcinoma
    • Abstract
    • 1 Introduction
    • 2 Methods
    • 3 Data set
    • 4 Results and Discussion
    • 5 Conclusions
    • Supplementary materials
  • Chapter 5: Molecular Classification of N-Aryloxazolidinone-5-carboxamides as Human Immunodeficiency Virus Protease Inhibitors
    • Abstract
    • Acknowledgments
    • 1 Introduction
    • 2 Computational method
    • 3 Classification algorithm
    • 4 Information entropy
    • 5 The EC of entropy production
    • 6 Learning procedure
    • 7 Calculation results and discussion
    • 8 Conclusions
  • Chapter 6: Review of Recent Protein-Protein Interaction Techniques
    • Abstract
    • 1 Introduction
    • 2 Technical challenges and open issues
    • 3 Performance measures
    • 4 Computational approaches
    • 5 Conclusion
  • Chapter 7: Genetic Regulatory Networks: Focus on Attractors of Their Dynamics
    • Abstract
    • Acknowledgments
    • 1 Introduction
    • 2 Immunetworks
    • 3 The iron control network
    • 4 Morphogenetic networks
    • 5 Biliary atresia control network
    • 6 Conclusion and perspectives
    • Mathematical Annex
  • Chapter 8: Biomechanical Evaluation for Bone Allograft in Treating the Femoral Head Necrosis: Thorough Debridement or not?
    • Abstract
    • 1 Introduction
    • 2 Materials and methods
    • 3 Results
    • 4 Discussion
    • 5 Conclusion
    • 6 Disclaimer
  • Chapter 9: Diels-Alderase Catalyzing the Cyclization Step in the Biosynthesis of Spinosyn A: Reality or Fantasy?
    • Abstract
    • Acknowledgments
    • Graphical Abstract
    • 1 Introduction
    • 2 Computational methods
    • 3 Results and discussion
    • 4 Conclusions
    • Supplementary Material: Diels-Alderase Catalyzing the Cyclization Step in the Biosynthesis of Spinosyn A: Reality or Fantasy?
    • 1 Conformational analysis of macrocyclic lactone (4)
    • 2 Modelling of a theozyme for the conversion of macrocyclic lactone (4) into tricyclic compound (5)
    • 3 ELF bonding analysis of the conversion of macrocyclic lactone (4) into the tricyclic compound (5)
  • Chapter 10: CLAST: Clustering Biological Sequences
    • Abstract
    • Acknowledgments
    • 1 Introduction
    • 2 Methods
    • 3 Evaluation and discussion
    • 4 Conclusions
  • Chapter 11: Computational Platform for Integration and Analysis of MicroRNA Annotation
    • Abstract
    • 1 Introduction
    • 2 Material
    • 3 MIRIA Database
    • 4 MiRNA CFSim
    • 5 Web Framework
    • 6 Results
    • 7 Conclusions
  • Chapter 12: Feature Selection and Analysis of Gene Expression Data Using Low-Dimensional Linear Programming
    • Abstract
    • Acknowledgments
    • 1 Introduction
    • 2 LP formulation of separability
    • 3 Offline approach
    • 4 Incremental approach
    • 5 Gene selection
    • 6 A new methodology for gene selection
    • 7 Results and discussion
    • 8 Conclusions
  • Chapter 13: The Big ORF Theory: Algorithmic, Computational, and Approximation Approaches to Open Reading Frames in Short- and Medium-Length dsDNA Sequences
    • Abstract
    • Acknowledgments
    • 1 Introduction
    • 2 Molecular genetic and bioinformatic considerations
    • 3 Algorithmic and programming considerations
    • 4 Analytical and random sampling solutions to L > 25 sequences: Triplet-based approximations
    • 5 Alternative genetic codes
    • 6 Implications for the evolution of ORF size
  • Chapter 14: Intentionally Linked Entities: A Detailed Look at a Database System for Health Care Informatics
    • Abstract
    • Acknowledgments
    • 1 Introduction
    • 2 Introducing ILE for Health Care Applications
    • 3 ILE and epidemiological data modeling
    • 4 Other nonrelational approaches to keeping medical records
    • 5 Inside the ILE database system
    • 6 An example of the Importance of an EHR implemented in ILE
    • 7 Conclusions
  • Chapter 15: Region Growing in Nonpictorial Data for Organ-Specific Toxicity Prediction
    • Abstract
    • 1 Introduction
    • 2 Related works
    • 3 Basic foundation
    • 4 Methodology
    • 5 Empirical results
    • 6 Conclusions and future research
  • Chapter 16: Contribution of Noise Reduction Algorithms: Perception Versus Localization Simulation in the Case of Binaural Cochlear Implant (BCI) Coding
    • Abstract
    • Acknowledgments
    • 1 Introduction
    • 2 Materials and Methods
    • 3 Results
    • 4 Discussion
    • 5 Conclusions
  • Chapter 17: Lowering the Fall Rate of the Elderly from Wheelchairs
    • Abstract
    • Acknowledgments
    • 1 Introduction
    • 2 Current solutions
    • 3 A systems solution
    • 4 The sparrow design
    • 5 Assessment algorithm
    • 6 Assessment decision algorithm
    • 7 The future
    • 8 Conclusion
  • Chapter 18: Occipital and Left Temporal EEG Correlates of Phenomenal Consciousness
    • Abstract
    • Acknowledgments
    • 1 Introduction
    • 2 Participants
    • 3 Apparatus and stimuli
    • 4 Procedure
    • 5 EEG recording
    • 6 Experiment I
    • 7 Experiment II
    • 8 The grand average occipital and temporal electrical activity correlated with a contrast in access
    • 9 Behavioral data
    • 10 The grand average occipital and temporal electrical activity correlated with a contrast in phenomenology
    • 11 The grand average occipital and temporal electrical activity co-occurring with unconsciousness
  • Chapter 19: Chaotic Dynamical States in the Izhikevich Neuron Model
    • Abstract
    • 1 Introduction
    • 2 Fundamental description
    • 3 Chaotic properties of Izhikevich neuron model
    • 4 Response efficiency in chaotic resonance
    • 5 Conclusions
  • Chapter 20: Analogy, Mind, and Life
    • Abstract
    • Acknowledgements
    • 1 Introduction
    • 2 The artificial mind and cognitive science
    • 3 Consciousness
    • 4 The classic watchmaker analogy
    • 5 The classic watchmaker analogy is fragile, remote and reductive
    • 6 The analogy between life and information seems to suggest some type of reductionism
    • 7 Conclusion
  • Chapter 21: Copy Number Networks to Guide Combinatorial Therapy of Cancer and Proliferative Disorders
    • Abstract
    • Acknowledgments
    • 1 Introduction
    • 2 A diminishing drug pipeline
    • 3 Using genome data to replenish the pipeline by drug repositioning
    • 4 The small-world properties of networks expedite combination therapies
    • 5 Molecular networks can be used to guide drug combinations
    • 6 Copy number alterations as a disease driver
    • 7 Using correlated copy number alterations to construct survival networks
    • 8 A pan-cancer CNA interaction network
    • 9 Mapping genetic survival networks using correlated CNAs in radiation hybrid cells
    • 10 A survival network for GBM at single-gene resolution
    • 11 Using CNA networks to guide combination therapies
    • 12 Targeting multiple drugs to single-disease genes in cancer
    • 13 Targeting multiple drugs to a single-disease gene in autoimmunity
    • 14 Targeting multiple genes in a single pathway for cancer
    • 15 Targeting genes in parallel pathways converging on atherosclerosis
    • 16 Using CNA networks to synergize drug combinations and minimize side effects
    • 17 Disclaimer
  • Chapter 22: DNA Double-Strand Break–Based Nonmonotonic Logic
    • Abstract
    • 1 Introduction
    • 2 DNA DSBs
    • 3 Logical model for system biology
    • 4 Completing the signaling pathways by default abduction
    • 5 Logic representation of a signaling pathway with the goal of reducing computational complexity
    • 6 Algorithm and implementation
    • 7 Results
    • 8 Conclusions
  • Chapter 23: An Updated Covariance Model for Rapid Annotation of Noncoding RNA
    • Abstract
    • 1 Introduction
    • 2 Method
    • 3 Test results
    • 4 Conclusions
  • Chapter 24: SMIR: A Web Server to Predict Residues Involved in the Protein Folding Core
    • Abstract
    • Acknowledgments
    • 1 Introduction
    • 2 Methods
    • 3 Results
    • 4 Conclusion
  • Chapter 25: Predicting Extinction of Biological Systems with Competition
    • Abstract
    • Acknowledgments
    • 1 Introduction
    • 2 A Model of Competing Species
    • 3 Density function of extinction time
    • 4 Estimation of parameters
    • 5 Numerical results
    • 6 Summary
  • Chapter 26: Methodologies for the Diagnosis of the Main Behavioral Syndromes for Parkinson’s Disease with Bayesian Belief Networks
    • Abstract
    • 1 Introduction
    • 2 Diagnosis of FoG
    • 3 Diagnosis of handwriting and speech
    • 4 Toward a global methodology for PD
    • 5 Conclusions and future work
  • Chapter 27: Practical Considerations in Virtual Screening and Molecular Docking
    • Abstract
    • 1 Introduction
    • 2 Receptor structure preparation
    • 3 Accurately predicting the pose of solved crystal structures and differentiating decoys from actives
    • 4 Side-chain flexibility and ensemble docking
    • 5 Consensus docking
    • 6 MM-GBSA
    • 7 Incorporating pharmacophoric constraints within the virtual screen
    • 8 Conclusion
  • Chapter 28: Knowledge Discovery in Proteomic Mass Spectrometry Data
    • Abstract
    • 1 Introduction
    • 2 Technical background
    • 3 Computational workflow
    • 4 Analysis tool
    • 5 Conclusion
  • Chapter 29: A Comparative Analysis of Read Mapping and Indel Calling Pipelines for Next-Generation Sequencing Data
    • Abstract
    • 1 Introduction
    • 2 Mapping and calling software
    • 3 Methods
    • 4 Real data
    • 5 Results and discussion
    • 6 Conclusions
  • Chapter 30: Two-Stage Evolutionary Quantification of In Vivo MRS Metabolites
    • Abstract
    • Acknowledgments
    • 1 Introduction
    • 2 Proposed methodology
    • 3 Experiment
    • 4 Conclusions
  • Chapter 31: Keratoconus Disease and Three-Dimensional Simulation of the Cornea throughout the Process of Cross-Linking Treatment
    • Abstract
    • Acknowledgments
    • 1 Introduction
    • 2 Methodology
    • 3 Conclusions and Recommendations
  • Chapter 32: Emerging Business Intelligence Framework for a Clinical Laboratory Through Big Data Analytics
    • Abstract
    • 1 Introduction
    • 2 Motivation
    • 3 Material and methods
    • 4 Use-cases
    • 5 Case Study 1: Clinical laboratory test usage patterns visualization
    • 6 Data source and methodology
    • 7 Results and discussion
    • 8 Limitations
    • 9 Case Study 2: Provincial laboratory clinical test volume estimation
    • 10 Data source and methodology
    • 11 Results and discussion
    • 12 Limitations
    • 13 Conclusion and future work
  • Chapter 33: A Codon Frequency Obfuscation Heuristic for Raw Genomic Data Privacy
    • Abstract
    • 1 Introduction
    • 2 Background
    • 3 Related work
    • 4 Methodology
    • 5 Experiment and results
    • 6 Conclusion
  • Index

Description

Emerging Trends in Computational Biology, Bioinformatics, and Systems Biology discusses the latest developments in all aspects of computational biology, bioinformatics, and systems biology and the application of data-analytics and algorithms, mathematical modeling, and simu- lation techniques.

• Discusses the development and application of data-analytical and theoretical methods, mathematical modeling, and computational simulation techniques to the study of biological and behavioral systems, including applications in cancer research, computational intelligence and drug design, high-performance computing, and biology, as well as cloud and grid computing for the storage and access of big data sets.

• Presents a systematic approach for storing, retrieving, organizing, and analyzing biological data using software tools with applications to general principles of DNA/RNA structure, bioinformatics and applications, genomes, protein structure, and modeling and classification, as well as microarray analysis.

• Provides a systems biology perspective, including general guidelines and techniques for obtaining, integrating, and analyzing complex data sets from multiple experimental sources using computational tools and software. Topics covered include phenomics, genomics, epigenomics/epigenetics, metabolomics, cell cycle and checkpoint control, and systems biology and vaccination research.

• Explains how to effectively harness the power of Big Data tools when data sets are so large and complex that it is difficult to process them using conventional database management systems or traditional data processing applications.

Key Features

  • Discusses the development and application of data-analytical and theoretical methods, mathematical modeling and computational simulation techniques to the study of biological and behavioral systems.
  • Presents a systematic approach for storing, retrieving, organizing and analyzing biological data using software tools with applications.
  • Provides a systems biology perspective including general guidelines and techniques for obtaining, integrating and analyzing complex data sets from multiple experimental sources using computational tools and software.

Readership

Researchers and graduate students in Computer Science, Bioinformatics researchers and Systems biologists


Details

No. of pages:
670
Language:
English
Copyright:
© Morgan Kaufmann 2015
Published:
Imprint:
Morgan Kaufmann
eBook ISBN:
9780128026465
Paperback ISBN:
9780128025086

Reviews

"This is a valuable resource for students, clinicians, and researchers who wish to keep abreast of the emerging trends in computational biology, bioinformatics, and systems biology.  Score: 76 - 3 Stars" --Doody's


About the Authors

Hamid Arabnia Author

Hamid R. Arabnia is currently a Full Professor of Computer Science at University of Georgia where he has been since October 1987. His research interests include Parallel and distributed processing techniques and algorithms, interconnection networks, and applications in Computational Science and Computational Intelligence (in particular, in image processing, medical imaging, bioinformatics, and other computational intensive problems). Dr. Arabnia is Editor-in-Chief of The Journal of is Associate Editor of IEEE Transactions on Information Technology in Biomedicine . He has over 300 publications (journals, proceedings, editorship) in his area of research in addition he has edited two titles Emerging Trends in ICT Security (Elsevier 2013), and Advances in Computational Biology (Springer 2012).

Affiliations and Expertise

Professor of Computer Science, University of Georgia, Athens, GA, USA

Quoc Nam Tran Author

Professor Quoc-Nam Tran is currently Chair and Full Professor of Computer Science at University of South Dakota. He previously served as Chair and Full Professor of Computer Science at the University of Texas at Tyler. His previous positions include: Professor of Computer Science at Lamar University; Visiting Professor at Rice University; Scientist at Wolfram Research, Champaign-Urbana ; and Assistant Professor at University of Linz (Linz, Austria). Professor Tran's research interests include: computational methods and algorithmic foundations; theory of Groebner bases; bioinformatics and computational biology. He has published extensively in his areas of expertise. He has co-edited a number of books, including: "Software Tools and Algorithms for Biological Systems" (2011) and "Advances in Computational Biology" (2010) (Springer) Professor Tran has served on a number of editorial boards and has organized and chaired sessions for premier conferences such as the IEEE International Conference on Bioinformatics and Biomedicine Workshop.

Affiliations and Expertise

Chair and Professor of Computer Science, University of South Dakota, Vermillion, SD, USA