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Conference ISBRA (Conference) (11th : 2015 : Norfolk, Virginia)
Title Bioinformatics research and applications : 11th International Symposium, ISBRA 2015 Norfolk, USA, June 7-10, 2015 Proceedings / Robert Harrison, Yaohang Li, Ion Mandoiu (eds.).
Imprint Cham : Springer, 2015.

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Conference ISBRA (Conference) (11th : 2015 : Norfolk, Virginia)
Series Lecture notes in bioinformatics, 0302-9743 ; 9096
LNCS sublibrary. SL 8, Bioinformatics
Lecture notes in computer science. Lecture notes in bioinformatics ; 9096.
LNCS sublibrary. SL 8, Bioinformatics.
Subject Bioinformatics -- Congresses.
Computational Biology -- methods.
Alt Name Harrison, Robert (Computer scientist),
Li, Yaohang,
Măndoiu, Ion,
Add Title ISBRA 2015
Description 1 online resource (xxii, 446 pages) : illustrations.
polychrome rdacc
Note International conference proceedings.
Bibliography Note Includes bibliographical references and author index.
Summary This book constitutes the refereed proceedings of the 11th International Symposium on Bioinformatics Research and Applications, ISBRA 2015, held in Norfolk, VA, USA, in June 2015. The 34 revised full papers and 14 two-page papers included in this volume were carefully reviewed and selected from 98 submissions. The papers cover a wide range of topics in bioinformatics and computational biology and their applications.
Contents Intro; Preface; Symposium Organization; Contents; Deriving Protein Backbone Using Traces Extracted from Density Maps at Medium Resolutions; 1 Introduction; 2 Methods; 3 Results; 4 Conclusions; References; Binary Contingency Table Method for Analyzing Gene Mutation in Cancer Genome; 1 Introduction; 2 Method; 2.1 Binary Contingency Tables; 2.2 BCT-Sampling; 2.3 PBCT-Sampling; 2.4 Computation of; 3 Result; 3.1 Simulation Study; 3.2 Real Data Experiment; 4 Conclusion and Discussion; References; A Filter-Based Approach for Approximate Circular Pattern Matching; 1 Introduction
1.1 Applications and Motivations1.2 Our Contribution; 1.3 Road Map; 2 Preliminaries; 3 Brief Literature Review; 4 Filtering Algorithm; 4.1 Overview of Our Approach; 4.2 Our Filters; 4.3 Reduction of Search Space in the Text; 4.4 The Combined Algorithm; 5 Experimental Results; 6 Conclusions; References; Fast Algorithms for Inferring Gene-Species Associations; 1 Introduction; 2 Basic Definitions and Preliminaries; 2.1 Problems: Resolving Unknown Gene-Species Mappings; 3 Methods; 3.1 Inferring Gene-Species Distributions; 4 Experimental Evaluation; 4.1 Reconstruction Quality and Runtime Analysis
4.2 Empirical Dataset Evaluation5 Conclusion and Future Outlook; References; Couplet Supertree Based Species TreeEstimation; 1 Introduction; 2 Refinement of S into Binary Tree S; 3 Experimental Results; 3.1 Datasets Used; 3.2 Performance Measures; 3.3 Performance Comparison; References; A Novel Computational Method for Deriving Protein Secondary Structure Topologies Using Cryo-EM Density Maps and Multiple Secondary Structure Predictions; 1 Introduction; 2 Methods; 3 Results; 4 Conclusions; References; Managing Reproducible Computational Experiments with Curated Proteinsin KINARI-2
1 Introduction2 Methods and Design; 2.1 System Design; 2.2 Application Design; 2.3 Managing an Experiment; 2.4 Step Design; 3 Results; 4 Conclusion; References; Protein Crystallization Screening Using Associative Experimental Design; 1 Introduction; 2 Background; 2.1 Phase Diagram; 2.2 Hampton Scoring; 3 Proposed Method: Associative Experimental Design(AED); 3.1 Motivation; 3.2 Method; 4 Experiments; 4.1 Dataset; 4.2 Results and Discussion; 5 Conclusion and Future Work; MINED: An Efficient Mutual Information Based Epistasis Detection Method to Improve Quantitative Genetic Trait Prediction
1 Introduction2 Preliminaries; 3 Methods; 3.1 MINED: Mutual Information Based Epistasis Detection; 3.2 Dynamic Significance Threshold; 3.3 Compute Marker Probability; 3.4 Epistasis Detection with Constraints; 4 Experimental Results; 4.1 Maize Data; 4.2 Rice Data; 4.3 Pine Data; 5 Conclusions; References; Domain Adaptation with Logistic Regression for the Task of Splice Site Prediction; 1 Introduction; 2 Related Work; 3 Methods and Materials; 3.1 Logistic Regression with Regularized Parameters; 3.2 Logistic Regression for Domain Adaptation Setting with
Note Online resource; title from PDF title page (SpringerLink, viewed May 7, 2015).
ISBN 9783319190488 (electronic bk.)
3319190482 (electronic bk.)
ISBN/ISSN 10.1007/978-3-319-19048-8
OCLC # 908563581
Additional Format Printed edition: 9783319190471

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