Algorithms for Computational Biology: 7th International Conference, Alcob 2020, Missoula, Mt, Usa, April 13-15, 2020, Proceedings
Martín-Vide, Carlos, Vega-Rodríguez, Miguel A., Wheeler, Travis
- 出版商: Springer
- 出版日期: 2020-02-20
- 售價: $2,370
- 貴賓價: 9.5 折 $2,252
- 語言: 英文
- 頁數: 199
- 裝訂: Quality Paper - also called trade paper
- ISBN: 3030422658
- ISBN-13: 9783030422653
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相關分類:
Algorithms-data-structures
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相關主題
商品描述
This book constitutes the proceedings of the 7th International Conference on Algorithms for Computational Biology, AlCoB 2020, held in Missoula, MT, USA in April 2020.
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.
目錄大綱
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 Prediction of 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.