Research in Computational Molecular Biology

20th Annual Conference, RECOMB 2016, Santa Monica, CA, USA, April 17-21, 2016, Proceedings

AvMona Singh

Häftad, Engelska, 2016

Del 9649 i serien Lecture Notes in Computer Science

544 kr

Beställningsvara. Skickas inom 10-15 vardagar. Fri frakt över 249 kr.

Beskrivning

Thisbook constitutes the proceedings of the 20th Annual Conference on Research inComputational Molecular Biology, RECOMB 2016, held in Santa Monica, CA, USA, inApril 2016. The 15 regular papers presented in this volume were carefullyreviewed and selected from 172 submissions.

Produktinformation

Utgivningsdatum:2016-04-08
Mått:155 x 235 x 17 mm
Vikt:470 g
Format:Häftad
Språk:Engelska
Serie:Lecture Notes in Computer Science
Antal sidor:274
Förlag:Springer International Publishing AG
ISBN:9783319319568

Utforska kategorier

Systemvetenskap och AI inom Data och IT
Databaser inom Data och IT
Biovetenskap inom Naturvetenskap och teknik

Innehållsförteckning

Recomb Retrospectives.- The Second Decade of theInternational Conference on Research in Computational Molecular Biology(RECOMB).- Extended Abstracts.- A MAD-Bayes Algorithm for State-space Inferenceand Clustering with Application to Querying Large Collections of ChIP-Seq DataSets.- Accurate Recovery of Ribosome Positions Reveals Slow Translation of Wobble-PairingCodons in Yeast.- Multitask matrix completion for learning protein interactionsacross diseases.- pathTiMEx: Joint Inference of Mutually Exclusive CancerPathways and their Dependencies in Tumor Progression.- Clonality inference fromsingle tumor samples using low coverage sequence data.- Flexible Modelling ofGenetic Effects on Function-Valued Traits.- MetaFlow: Metagenomic profilingbased on whole-genome coverage analysis with min-cost flows.- LUTE (LocalUnpruned Tuple Expansion): Accurate continuously flexible protein design withgeneral energy functions and rigid-rotamerlike efficiency.- Improving Bloom filterperformance on sequence data using k-mer Bloom filters.-Safe and completecontig assembly via omnitigs.- Long single-molecule reads can resolve thecomplexity of the Influenza virus composed of rare, closely related mutantvariants.-Structural variation detection with read pair information { An improvednull-hypothesis reduces bias.- On Computing Breakpoint Distances for Genomeswith Duplicate Genes.- New Genome Similarity Measures based on Conserved GeneAdjacencies.- Fast Phylogenetic Biodiversity Computations Under a Non-Uniform RandomDistribution.- Short Abstracts.- SLICER: Inferring Branched, Nonlinear CellularTrajectories from Single Cell RNA-seq Data.- Multi-track modeling forgenome-scale reconstruction of 3D chromatin structure from Hi-C data.- Revealingthe Genetic Basis of Immune Traits in the Absence of ExperimentalImmunophenotyping.- Shall we dense? Comparing design strategies for time seriesexpression experiments.- Enabling Privacy-Preserving GWAS in HeterogeneousHuman Populations.- Efficient Privacy-Preserving Read Mapping Using LocalitySensitive Hashing and Secure Kmer Voting.- Finding Mutated Subnetworks Associatedwith Survival Time in Cancer.- Multi-State Perfect Phylogeny MixtureDeconvolution and Applications to Cancer Sequencing.- Tree inference forsingle-cell data.- mLDM: a new hierarchical Bayesian statistical model forsparse microbial association discovery.- Low-density locality-sensitive hashingboosts metagenomic binning.- metaSPAdes: a new versatile de novo metagenomicsassembler.- Distributed Gradient Descent in Bacterial Food Search.-AptaTRACE:Elucidating Sequence-Structure Binding Motifs by Uncovering Selection Trends inHT-SELEX Experiments.- Fast Bayesian Inference of Copy Number Variants usingHidden Markov Models with Wavelet Compression.- Allele-Specific Quantificationof Structural Variations in Cancer Genomes.- Assembly of Long Error-Prone ReadsUsing de Bruijn Graphs.- Locating a Tree in a Reticulation-Visible Network inCubic Time.- Joint alignment of multiple protein-protein interaction networksvia convex optimization.- Complexes Detection in Biological Networks viaDiversified Dense Subgraphs Mining.