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  • Produktbild: Research in Computational Molecular Biology
  • Produktbild: Research in Computational Molecular Biology
Band 11467

Research in Computational Molecular Biology 23rd Annual International Conference, RECOMB 2019, Washington, DC, USA, May 5-8, 2019, Proceedings

Aus der Reihe Lecture Notes in Computer Science

63,99 €

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

02.04.2019

Abbildungen

XIV, 337 p. 146 illus., 67 illus. in color.

Herausgeber

Lenore J. Cowen

Verlag

Springer

Seitenzahl

337

Maße (L/B/H)

23,5/15,5/2 cm

Gewicht

534 g

Auflage

1st ed. 2019

Sprache

Englisch

ISBN

978-3-030-17082-0

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

02.04.2019

Abbildungen

XIV, 337 p. 146 illus., 67 illus. in color.

Herausgeber

Lenore J. Cowen

Verlag

Springer

Seitenzahl

337

Maße (L/B/H)

23,5/15,5/2 cm

Gewicht

534 g

Auflage

1st ed. 2019

Sprache

Englisch

ISBN

978-3-030-17082-0

Herstelleradresse

Springer-Verlag GmbH
Tiergartenstr. 17
69121 Heidelberg
DE

Email: ProductSafety@springernature.com

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  • Produktbild: Research in Computational Molecular Biology
  • Produktbild: Research in Computational Molecular Biology

  • An Efficient, Scalable and Exact Representation of High-Dimensional Color Information Enabled Via de Bruijn Graph Search.- Identifying Clinical Terms in Free-Text Notes Using Ontology-Guided Machine Learning.-  ModHMM: A Modular Supra-Bayesian Genome Segmentation Method.-  Learning Robust Multi-Label Sample Specific Distances for Identifying HIV-1 Drug Resistance.-  MethCP: Differentially Methylated Region Detection with Change Point Models.-  On the Complexity of Sequence to Graph Alignment.-  Minimization-Aware Recursive K* (MARK*): A Novel, Provable Algorithm that Accelerates Ensemble-based Protein Design and Provably Approximates the Energy Landscape.- Sparse Binary Relation Representations for Genome Graph Annotation.- How Many Subpopulations is Too Many? Exponential Lower Bounds for Inferring Population Histories.- Efficient Construction of a Complete Index for Pan-Genomics Read Alignment.- Tumor Copy Number Deconvolution Integrating Bulk and Single-CellSequencing Data.- OMGS: Optical Map-based Genome Scaffolding.- Fast Approximation of Frequent k-mers and Applications to Metagenomics.-  De Novo Clustering of Long-Read Transcriptome Data Using a Greedy, Quality-Value Based Algorithm.- A Sticky Multinomial Mixture Model of Strand-Coordinated Mutational Processes in Cancer.- Disentangled Representations of Cellular Identity.-  RENET: A Deep Learning Approach for Extracting Gene-Disease Associations from Literature.- APPLES: Fast Distance Based Phylogenetic Placement.- De Novo Peptide Sequencing Reveals a Vast Cyclopeptidome in Human Gut and Other environments.- Biological Sequence Modeling with Convolutional Kernel Networks.- Dynamic Pseudo-Time Warping of Complex Single-Cell Trajectories.- netNMF-sc: A Network Regularization Algorithm for Dimensionality Reduction and Imputation of Single-Cell Expression Data.-  Geometric Sketching of Single-Cell Data Preserves Transcriptional Structure.- Sketching Algorithms for GenomicData Analysis and Querying in a Secure Enclave.-  Mitigating Data Scarcity in Protein Binding Prediction Using Meta-Learning.- Efficient  Estimation and Applications of Cross-Validated Genetic Predictions.- Inferring Tumor Evolution from Longitudinal Samples.- Scalable Multi-Component Linear Mixed Models with Application to SNP Heritability Estimation.- A Note on Computing Interval Overlap Statistics.-  Distinguishing Biological from Technical Sources of Variation by Leveraging Multiple Methylation Datasets.- GRep: Gene Set Representation via Gaussian Embedding.-  Accurate Sub-Population Detection and Mapping Across Single Cell Experiments with PopCorn.- Fast Estimation of Genetic Correlation for Biobank-Scale Data.- Distance-Based Protein Folding Powered by Deep Learning.- Comparing 3D Genome Organization in Multiple Species Using Phylo-HMRF.- Towards a Post-Clustering Test for Didderential Expression.- AdaFDR: a Fast, Powerful and Covariate-Adaptive Approach for Multiple Hypothesis Testing.