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E-grāmata: Emerging Trends in Computational Biology, Bioinformatics, and Systems Biology: Algorithms and Software Tools

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(Professor of Computer Science, University of Georgia, Athens, GA, USA), (Chair and Professor of Computer Science, University of South Dakota, Vermillion, SD, USA)
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Emerging Trends in Computational Biology, Bioinformatics, and Systems Biologydiscusses the latest in all aspects of computational biology, bioinformatics, and systems biology and the application of data-analytical and algorithms, mathematical modeling and simulation techniques.

Part I: Computational Biology discusses the development and application of data-analytical and theoretical methods, mathematical modeling and computational simulation techniques. Part II: Bioinformatics: Databases, Data Mining and Pattern Discovery focuses on how to use methods for storing, retrieving, organizing and analyzing biological data which are fundamentally extensions of techniques used in computing. Part III: Systems Biology explains how to obtain, integrate and analyze complex data sets from multiple experimental sources using interdisciplinary tools while taking into consideration the evolving nature of the field. Part IV: Big Data and Data Analytics in Computational Biology and Informatics presents strategies and techniques using robust Big Data tools for dealing with the collection of data sets so large and complex that they are difficult to process using conventional database management systems or traditional data processing applications.

  • 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.

Recenzijas

"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

Papildus informācija

An overview of computational biology, bioinformatics and system biology from a computer science perspective, emphasizing Big Data using a data-analytical driven approach
Contributors xix
Preface xxvii
Acknowledgments xxxiii
Introduction xxxv
Chapter 1 Supervised Learning with the Artificial Neural Networks Algorithm for Modeling Immune Cell Differentiation
1(18)
1 Introduction
1(4)
1.A Immune Cell Differentiation and Modeling
1(2)
1.B MSM and Model Reduction
3(1)
1.C ANN Algorithm and its Applications
4(1)
2 Related Work
5(4)
3 Modeling Immune Cell Differentiation
9(5)
3.3 T Cell Differentiation Process as a use Case
9(1)
3.3 Data for Training and Testing Models
9(1)
3.3 ANN Model
9(2)
3.3 Comparative Analysis with the LR Model and SVM
11(2)
3.3 Capability of ANN Model to Analyze Data with Noise
13(1)
4 Discussion
14(1)
5 Conclusion
15(4)
Acknowledgments
15(1)
References
16(3)
Chapter 2 Accelerating Techniques for Particle Filter Implementations on FPGA
19(20)
1 Introduction
19(2)
2 PF and SLAM Algorithms
21(4)
2.2 Particle Filtering
21(2)
2.2 Application of PF to SLAM
23(2)
3 Computational Bottleneck Identification and Hardware/software Partitioning
25(1)
4 PF Acceleration Techniques
26(4)
4.4 CORDIC Acceleration Technique
26(2)
4.4 Ziggurat Acceleration Technique
28(2)
5 Hardware Implementation
30(1)
6 Hardware/Software Architecture
31(3)
7 Results and Discussion
34(1)
8 Conclusions
35(4)
References
35(4)
Chapter 3 Biological Study on Pulsatile Flow of Herschel-Bulkley Fluid in Tapered Blood Vessels
39(12)
1 Introduction
39(2)
2 Formulation of the Problem
41(2)
3 Solution
43(2)
4 Discussion
45(3)
5 Conclusion
48(3)
References
49(2)
Chapter 4 Hierarchical k-Means: A Hybrid Clustering Algorithm and its Application to Study Gene Expression in Lung Adenocarcinoma
51(18)
1 Introduction
51(2)
2 Methods
53(4)
3 Data Set
57(1)
4 Results and Discussion
57(7)
5 Conclusions
64(5)
References
65(2)
Supplementary Materials
67(2)
Chapter 5 Molecular Classification of N-Aryloxazolidinone-5-carboxamides as Human Immunodeficiency Virus Protease Inhibitors
69(30)
1 Introduction
69(2)
2 Computational Method
71(1)
3 Classification Algorithm
71(2)
4 Information Entropy
73(1)
5 The EC of Entropy Production
74(1)
6 Learning Procedure
74(1)
7 Calculation Results and Discussion
75(18)
8 Conclusions
93(6)
Acknowledgments
94(1)
References
94(5)
Chapter 6 Review of Recent Protein-Protein Interaction Techniques
99(24)
1 Introduction
99(1)
2 Technical Challenges and Open Issues
100(1)
3 Performance Measures
101(1)
4 Computational Approaches
102(13)
4.4 Sequence-based Approaches
103(7)
4.4 Structure-based Approaches
110(5)
5 Conclusion
115(8)
References
116(7)
Chapter 7 Genetic Regulatory Networks: Focus on Attractors Of Their Dynamics
123(32)
1 Introduction
123(1)
2 Immunetworks
124(3)
2.2 The Immunetwork Responsible of the Toll-Like Receptor (TLR) expression
124(1)
2.2 The Links with the microRNAs
124(1)
2.2 The Adaptive Immunetworks
125(2)
3 The Iron Control Network
127(3)
4 Morphogenetic Networks
130(3)
5 Biliary Atresia Control Network
133(4)
6 Conclusion and Perspectives
137(18)
Mathematical Annex
138(1)
A1 Definitions
138(2)
A2 First Propositions
140(2)
A3 Tangent and Intersecting Circuits
142(3)
A4 State-dependent Updating Schedule
145(1)
A5 The Circular Hamming Distance
146(1)
A6 The ArchetypaL Sequence AL
147(2)
Acknowledgments
149(1)
References
149(6)
Chapter 8 Biomechanical Evaluation for Bone Allograft in Treating the Femoral Head Necrosis: Thorough Debridement or Not?
155(14)
1 Introduction
155(1)
2 Materials and Methods
156(3)
2.2 JIC Classification
156(1)
2.2 Generation of Intact Finite Element Models
156(3)
3 Results
159(5)
3.3 Stress Transfer Path
159(1)
3.3 Stress of the Anterolateral Column
160(1)
3.3 Peak Stress of the Residual Necrotic Bone
161(1)
3.3 Model Validation
162(2)
4 Discussion
164(1)
5 Conclusion
165(1)
6 Disclaimer
165(4)
6.6 Funding
165(1)
References
165(4)
Chapter 9 Diels-Alderase Catalyzing the Cyclization Step In the Biosynthesis of Spinosyn A: Reality or Fantasy?
169(34)
1 Introduction
170(4)
2 Computational Methods
174(1)
3 Results and Discussion
174(5)
4 Conclusions
179(4)
Acknowledgments
181(1)
References
181(2)
Supplementary Material: Diels-Alderase Catalyzing the Cyclization Step in the Biosynthesis of Spinosyn A: Reality or Fantasy?
183(1)
1 Conformational Analysis of Macrocyclic Lactone (4)
183(1)
2 Modelling of a Theozyme for the Conversion of Macrocyclic Lactone (4) into Tricyclic Compound (5)
184(2)
3 ELF Bonding Analysis of the Conversion of Macrocyclic Lactone (4) Into the Tricyclic Compound (5)
186(17)
References
201(2)
Chapter 10 CLAST: Clustering Biological Sequences
203(18)
1 Introduction
203(2)
1.1 Related Work
204(1)
2 Methods
205(6)
2.2 Hashing
205(1)
2.2 Matching
206(4)
2.2 Clustering
210(1)
3 Evaluation and Discussion
211(8)
4 Conclusions
219(2)
Acknowledgments
220(1)
References
220(1)
Chapter 11 Computational Platform for Integration and Analysis of MicroRNA Annotation
221(14)
1 Introduction
221(2)
2 Material
223(2)
3 MIRIA Database
225(1)
4 MiRNA CFSim
226(1)
5 Web Framework
227(1)
6 Results
227(4)
7 Conclusions
231(4)
References
232(3)
Chapter 12 Feature Selection and Analysis of Gene Expression Data Using Low-Dimensional Linear Programming
235(30)
1 Introduction
235(2)
2 LP Formulation of Separability
237(2)
3 Offline Approach
239(1)
4 Incremental Approach
240(7)
4.4 Incremental Approach---2D
241(2)
4.4 Incremental Approach---3D
243(1)
4.4 Linear Programming Formulation of Unger and Chor's Incremental Algorithm
244(3)
5 Gene Selection
247(1)
5.5 Background
247(1)
6 A New Methodology for Gene Selection
248(1)
6.6 Coarse Filtration
248(1)
6.6 Fine Filtration
249(1)
7 Results and Discussion
249(13)
8 Conclusions
262(3)
Acknowledgments
263(1)
References
263(2)
Chapter 13 The Big ORF Theory: Algorithmic, Computational, and Approximation Approaches to Open Reading Frames in Short- and Medium-Length dsDNA Sequences
265(10)
1 Introduction
265(1)
2 Molecular Genetic and Bioinformatic Considerations
266(1)
2.2 Molecular Genetics of DNA →RNA →Protein
266(1)
2.2 Bioinformatic Data-mining
267(1)
3 Algorithmic and Programming Considerations
267(2)
4 Analytical and Random Sampling Solutions to L>25 Sequences: Triplet-based Approximations
269(2)
5 Alternative Genetic Codes
271(1)
6 Implications for the Evolution of ORF Size
272(3)
Acknowledgments
273(1)
References
273(2)
Chapter 14 Intentionally Linked Entities: A Detailed Look At a Database System for Health Care Informatics
275(20)
1 Introduction
275(3)
2 Introducing ILE for Health Care Applications
278(3)
3 ILE and Epidemiological Data Modeling
281(2)
4 Other Nonrelational Approaches to Keeping Medical Records
283(3)
5 Inside the ILE Database System
286(6)
6 An Example of the Importance of An EHR Implemented in ILE
292(1)
7 Conclusions
292(3)
Acknowledgments
293(1)
References
293(2)
Chapter 15 Region Growing in Nonpictorial Data for Organ-Specific Toxicity Prediction
295(12)
1 Introduction
295(1)
2 Related Works
296(1)
3 Basic Foundation
296(2)
4 Methodology
298(4)
4.4 Neighborly SOM
298(3)
4.4 A Region-based-Prediction Methodology
301(1)
5 Empirical Results
302(2)
6 Conclusions and Future Research
304(3)
References
305(2)
Chapter 16 Contribution of Noise Reduction Algorithms: Perception Versus Localization Simulation in the Case of Binaural Cochlear Implant (BCI) Coding
307(18)
1 Introduction
307(2)
2 Materials and Methods
309(7)
2.2 Signal Processing
309(4)
2.2 Phoneme Recognition Session
313(2)
2.2 Localization Task
315(1)
2.2 Listeners
316(1)
3 Results
316(1)
3.3 Localization
316(1)
3.3 Phoneme Recognition
317(1)
4 Discussion
317(5)
4.4 Source Localization (PCL)
317(3)
4.4 Recognition (PCRP)
320(1)
4.4 Other Influences
321(1)
4.4 Simulation with Normal Hearing Listeners
321(1)
5 Conclusions
322(3)
Acknowledgments
322(1)
References
323(2)
Chapter 17 Lowering the Fall Rate of the Elderly From Wheelchairs
325(10)
1 Introduction
325(1)
1.1 The Fundamental Problem
325(1)
1.1 An Imagined Solution
326(1)
2 Current Solutions
326(2)
3 A Systems Solution
328(1)
4 The Sparrow Design
329(2)
5 Assessment Algorithm
331(1)
6 Assessment Decision Algorithm
331(1)
7 The Future
332(1)
8 Conclusion
333(2)
Acknowledgments
333(1)
References
333(2)
Chapter 18 Occipital and Left Temporal EEG Correlates of Phenomenal Consciousness
335(20)
1 Introduction
335(1)
2 Participants
335(1)
3 Apparatus and Stimuli
335(1)
4 Procedure
336(1)
5 EEG Recording
337(1)
6 Experiment I
337(1)
7 Experiment II
338(2)
8 The Grand Average Occipital and Temporal Electrical Activity Correlated with a Contrast in Access
340(2)
8.8 ERP Data
340(2)
9 Behavioral Data
342(2)
10 The Grand Average Occipital and Temporal Electrical Activity Correlated with a Contrast in Phenomenology
344(6)
11 The Grand Average Occipital and Temporal Electrical Activity Co-occurring with Unconsciousness
350(5)
Acknowledgments
354(1)
References
354(1)
Chapter 19 Chaotic Dynamical States in the Izhikevich Neuron Model
355(22)
1 Introduction
355(1)
2 Fundamental Description
356(5)
2.2 SR and CR
356(2)
2.2 Izhikevich Neuron Model
358(3)
3 Chaotic Properties of Izhikevich Neuron Model
361(5)
3.3 Evaluation Indices
361(1)
3.3 Chaotic Behaviors in Izhikevich Neuron Model
362(4)
4 Response Efficiency in Chaotic Resonance
366(6)
4.4 Extended Izhikevich Neuron Model with a Periodic Signal
367(1)
4.4 Dependence on Parameter d
368(3)
4.4 Dependence on Signal Strength A
371(1)
5 Conclusions
372(5)
References
373(4)
Chapter 20 Analogy, Mind, and Life
377(12)
1 Introduction
377(1)
2 The Artificial Mind and Cognitive Science
377(1)
3 Consciousness
378(4)
4 The Classic Watchmaker Analogy
382(1)
5 The Classic Watchmaker Analogy Is Fragile, Remote and Reductive
383(1)
6 The Analogy Between Life and Information Seems to Suggest Some Type of Reductionism
384(1)
7 Conclusion
385(4)
Acknowledgements
387(1)
References
387(2)
Chapter 21 Copy Number Networks to Guide Combinatorial Therapy of Cancer and Proliferative Disorders
389(20)
1 Introduction
389(1)
2 A Diminishing Drug Pipeline
389(1)
3 Using Genome Data to Replenish the Pipeline by Drug Repositioning
390(1)
4 The Small-world Properties of Networks Expedite Combination Therapies
390(1)
5 Molecular Networks can be Used to Guide Drug Combinations
391(1)
6 Copy Number Alterations as a Disease Driver
391(1)
7 Using Correlated Copy Number Alterations to Construct Survival Networks
392(1)
8 A Pan-cancer CNA Interaction Network
392(1)
9 Mapping Genetic Survival Networks Using Correlated CNAs in Radiation Hybrid Cells
393(1)
10 A Survival Network for GBM At Single-gene Resolution
393(1)
11 Using CNA Networks to Guide Combination Therapies
394(1)
12 Targeting Multiple Drugs to Single-disease Genes in Cancer
395(3)
13 Targeting Multiple Drugs to a Single-disease Gene in Autoimmunity
398(1)
14 Targeting Multiple Genes in a Single Pathway for Cancer
399(1)
15 Targeting Genes in Parallel Pathways Converging on Atherosclerosis
400(1)
16 Using CNA Networks to Synergize Drug Combinations and Minimize Side Effects
401(1)
17 Disclaimer
401(8)
Acknowledgments
401(1)
References
401(8)
Chapter 22 DNA Double-Strand Break--Based Nonmonotonic Logic
409(20)
1 Introduction
409(1)
2 DNA DSBs
410(2)
3 Logical Model for System Biology
412(5)
3.3 Declarative Representation of Signaling Pathway
412(1)
3.3 Logic Representation
413(1)
3.3 Causality and Classical Inference
414(1)
3.3 Causality and Nonmonotonic Logics
415(1)
3.3 Default Logic
415(1)
3.3 Extensions and Choice of Extensions
416(1)
4 Completing the Signaling Pathways by Default Abduction
417(1)
5 Logic Representation of a Signaling Pathway with the Goal of Reducing Computational Complexity
418(4)
5.5 Clauses and Horn Clauses
419(1)
5.5 Language Syntax
420(1)
5.5 Hard Rules and Default Rules
421(1)
5.5 Cell Signaling Pathway Representation
421(1)
6 Algorithm and Implementation
422(1)
7 Results
423(2)
8 Conclusions
425(4)
References
426(3)
Chapter 23 An Updated Covariance Model for Rapid Annotation of Noncoding RNA
429(8)
1 Introduction
429(1)
2 Method
430(2)
2.2 Conventional CM
430(1)
2.2 The Updated CM
430(1)
2.2 Sequence-structure Alignment
431(1)
2.2 Computing the Length Restrictions
431(1)
3 Test Results
432(1)
4 Conclusions
433(4)
References
434(3)
Chapter 24 SMIR: A Web Server to Predict Residues Involved in the Protein Folding Core
437(18)
1 Introduction
437(2)
2 Methods
439(2)
3 Results
441(10)
3.3 Model
441(1)
3.3 SMIR
442(1)
3.3 Submitting a Protein
443(2)
3.3 Use Case and Discussion
445(6)
4 Conclusion
451(4)
Acknowledgments
452(1)
References
452(3)
Chapter 25 Predicting Extinction of Biological Systems with Competition
455(12)
1 Introduction
455(2)
2 A Model of Competing Species
457(1)
3 Density Function of Extinction Time
458(2)
4 Estimation of Parameters
460(1)
5 Numerical Results
461(3)
6 Summary
464(3)
Acknowledgments
464(1)
References
464(3)
Chapter 26 Methodologies for the Diagnosis of the Main Behavioral Syndromes for Parkinson's Disease with Bayesian Belief Networks
467(20)
1 Introduction
467(1)
2 Diagnosis of FoG
468(7)
2.2 Data Acquisition and Preparation
469(3)
2.2 Causality and Methodology
472(1)
2.2 Modeling with BNCs
473(1)
2.2 Results and Discussion
474(1)
3 Diagnosis of Handwriting and Speech
475(3)
3.3 Experimental Protocol
476(1)
3.3 Clustering with BBNs
476(2)
4 Toward a Global Methodology for PD
478(3)
4.4 Handwriting and Speech Link
478(1)
4.4 Results and Discussion
479(2)
5 Conclusions and Future Work
481(6)
References
482(5)
Chapter 27 Practical Considerations in Virtual Screening and Molecular Docking
487(16)
1 Introduction
487(1)
2 Receptor Structure Preparation
488(3)
2.2 Protonation States
488(2)
2.2 Selecting Important Active Site Water Molecules
490(1)
3 Accurately Predicting the Pose of Solved Crystal Structures and Differentiating Decoys From Actives
491(1)
4 Side-chain Flexibility and Ensemble Docking
492(1)
5 Consensus Docking
493(1)
6 MM-GBSA
494(2)
7 Incorporating Pharmacophoric Constraints Within the Virtual Screen
496(1)
8 Conclusion
496(7)
References
497(6)
Chapter 28 Knowledge Discovery in Proteomic Mass Spectrometry Data
503(18)
1 Introduction
503(1)
2 Technical Background
504(1)
3 Computational Workflow
505(8)
3.3 Preprocessing
505(7)
3.3 Identification of Biomarker Candidates
512(1)
4 Analysis Tool
513(4)
4.4 KD3 Composition
513(3)
4.4 KD3 Functional Object
516(1)
4.4 KD3 Workflow
516(1)
5 Conclusion
517(4)
References
517(4)
Chapter 29 A Comparative Analysis of Read Mapping and Indel Calling Pipelines for Next-Generation Sequencing Data
521(16)
1 Introduction
521(1)
2 Mapping and Calling Software
522(2)
2.2 Hash Tables
522(1)
2.2 BWT
522(1)
2.2 Smith-Waterman Algorithm
523(1)
2.2 Dindel Indel Calling Model
524(1)
3 Methods
524(3)
3.3 Software Workflow
524(2)
3.3 Simulated Data
526(1)
4 Real Data
527(1)
4.4 Indel Detection
527(1)
5 Results and Discussion
528(6)
5.5 Analysis of F1-score and Coverage on Simulated Data
528(1)
5.5 Precision and Recall on Simulated Data with Smaller and Longer Indels
529(2)
5.5 Effect of Read Length on Simulated Data
531(1)
5.5 Accuracy of Sanger Real Data
531(1)
5.5 Run-time Performance on Sanger Data
531(1)
5.5 Accuracy of 1000 Genomes Real Data
531(3)
6 Conclusions
534(3)
References
534(3)
Chapter 30 Two-Stage Evolutionary Quantification of In Vivo MRS Metabolites
537(24)
1 Introduction
537(1)
2 Proposed Methodology
538(8)
2.2 Methodology Description
539(1)
2.2 Stage 1: MRS Signal Preprocessing
539(6)
2.2 Stage 2: GA Quantification
545(1)
3 Experiment
546(12)
3.3 Scenario 1: Complete Prior Knowledge
549(1)
3.3 Scenario 2: Limited Prior Knowledge
550(8)
4 Conclusions
558(3)
Acknowledgments
559(1)
References
559(2)
Chapter 31 Keratoconus Disease and Three-Dimensional Simulation of the Cornea Throughout the Process of Cross-Linking Treatment
561(16)
1 Introduction
561(3)
2 Methodology
564(7)
2.2 Data
564(1)
2.2 Application
564(7)
3 Conclusions and Recommendations
571(6)
Acknowledgments
574(1)
References
574(3)
Chapter 32 Emerging Business Intelligence Framework for a Clinical Laboratory Through Big Data Analytics
577(26)
1 Introduction
577(1)
2 Motivation
578(1)
3 Material and Methods
579(5)
3.3 Data Source
579(1)
3.3 MapReduce Framework
579(1)
3.3 The Hadoop Distributed File System
580(1)
3.3 The Emerging Framework
580(2)
3.3 Laboratory Management System Components
582(1)
3.3 Lab Management Application Interface
583(1)
3.3 Administration Services
583(1)
3.3 Test Procedure Services
583(1)
3.3 Operational Management Services
583(1)
3.3 Service Infrastructure-Hadoop Platform and Hadoop Enabled Automated Laboratory Transformation Hub (HEALTH) Cluster
583(1)
3.3 Data Warehouse Management Service
583(1)
3.3 Ubuntu Juju as a Service Orchestration and Bundling
584(1)
3.3 Typical Framework Usage Scenario in a Clinical Laboratory Setting
584(1)
4 Use-cases
584(1)
5 Case Study 1: Clinical Laboratory Test Usage Patterns Visualization
585(1)
6 Data Source and Methodology
585(2)
7 Results and Discussion
587(1)
8 Limitations
588(1)
9 Case Study 2: Provincial Laboratory Clinical Test Volume Estimation
589(1)
10 Data Source and Methodology
589(5)
10.10 Holt-Winters Model
589(1)
10.10 ARIMA Model
590(2)
10.10 Stationary Testing
592(1)
10.10 ARIMA Model Selection
592(2)
10.10 Performance Comparison and Model Selection
594(1)
11 Results and Discussion
594(4)
12 Limitations
598(1)
13 Conclusion and Future Work
598(5)
References
599(4)
Chapter 33 A Codon Frequency Obfuscation Heuristic for Raw Genomic Data Privacy
603(18)
1 Introduction
603(1)
2 Background
603(1)
3 Related Work
604(2)
4 Methodology
606(4)
5 Experiment and Results
610(6)
6 Conclusion
616(5)
References
618(3)
Index 621
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). 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.
Biežāk uzdotie jautājumi par e-grāmatām Biežāk uzdotie jautājumi par e-grāmatām
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