Algorithms for Computational Biology
7th International Conference, AlCoB 2020, Missoula, MT, USA, April 13-15, 2020, Proceedings
(Sprache: Englisch)
This book constitutes the proceedings of the 7th International Conference on Algorithms for Computational Biology, AlCoB 2020, was planned to be held in Missoula, MT, USA in April 2020. Due to the corona pandemic the conference was postponed to be held...
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This book constitutes the proceedings of the 7th International Conference on Algorithms for Computational Biology, AlCoB 2020, was planned to be held in Missoula, MT, USA in April 2020. Due to the corona pandemic the conference was postponed to be held together with AlCoB 2021.The 15 full papers included in this volume were carefully reviewed and selected from 24 submissions. They were organized in topical sections on genomics, phylogenetics, and RNA-Seq and other biological processes.
Inhaltsverzeichnis zu „Algorithms for Computational Biology “
Genomics.- Parallel Generalized Su x Tree Construction for Genomic Data.- A 3.5-Approximation Algorithm for Sorting by Intergenic Transpositions.- Heuristics for Reversal Distance between Genomes with Duplicated Genes.- Extending Maximal Perfect Haplotype Blocks to the Realm of Pangenomics.- Gaps and Runs in Syntenic Alignments.- Phylogenetics.- Comparing Integer Linear Programming to SAT-Solving for Hard Problems in Computational and Systems Biology.- Combining Networks Using Cherry Picking Sequences.- Linear Time Algorithm for Tree-Child Network Containment.- PathOGiST: A Novel Method for Clustering Pathogen Isolates by Combining Multiple Genotyping Signals.- TreeSolve: Rapid Error-Correction of Microbial Gene Trees.- RNA-Seq and Other Biological Processes.- Time Series Adjustment Enhancement of Hierarchical Modeling of Arabidopsis Thaliana Gene Interactions.- BESTox: A Convolutional Neural Network Regression Model Based on Binary-Encoded SMILES for Acute Oral Toxicity Predictionof Chemical Compounds.- Stratified Test Alleviates Batch Effects in Single-Cell Data.- A Topological Data Analysis Approach on Predicting Phenotypes from Gene Expression Data.- BOAssembler: A Bayesian Optimization Framework to Improve RNA-Seq Assembly Performance.
Bibliographische Angaben
- 2020, 1st ed. 2020, XVIII, 199 Seiten, 34 farbige Abbildungen, Maße: 16,1 x 23,7 cm, Kartoniert (TB), Englisch
- Herausgegeben: Carlos Martín-Vide, Miguel A. Vega-Rodríguez, Travis Wheeler
- Verlag: Springer, Berlin
- ISBN-10: 3030422658
- ISBN-13: 9783030422653
Sprache:
Englisch
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