Research in Computational Molecular Biology: 21st Annual International Conference, RECOMB 2017, Hong Kong, China, May 3-7, 2017, Proceedings 1st ed. 2017 [Mīkstie vāki]

Boosting alignment accuracy by adaptive local realignment.- A concurrent
subtractive assembly approach for identification of disease associated
sub-meta-genomes.- A flow procedure for the linearization of genome variation
graphs.- Dynamic alignment-free and reference-free read compression.- A fast
approximate algorithm for mapping long reads to large reference databases.-
Determining the consistency of resolved triplets and fan triplets.-
Progressive calibration and averaging for tandem mass spectrometry
statistical confidence estimation: Why settle for a single decoy.- Resolving
multi-copy duplications de novo using polyploid phasing.- A Bayesian active
learning experimental design for inferring signaling networks.- BBK* (Branch
and Bound over K*): A provable and efficient ensemble-based algorithm to
optimize stability and binding affinity over large sequence spaces.-
Super-bubbles, ultra-bubbles and cacti.- EPR-dictionaries: A practical and
fast data structure for constant time searches in unidirectional and
bidirectional FM indices.- A Bayesian framework for estimating cell type
composition from DNA methylation without the need for methylation reference.-
Towards recovering Allele-specific cancer genome graphs.- Using stochastic
approximation techniques to efficiently construct confidence intervals for
heritability.- Improved search of large transcriptomic sequencing databases
using split sequence bloom trees.- All some sequence bloom trees.-
Longitudinal genotype-phenotype association study via temporal structure
auto-learning predictive model.- Improving imputation accuracy by inferring
causal variants in genetic studies.- The copy-number tree mixture
deconvolution problem and applications to multi-sample bulk sequencing tumor
data.- Quantifying the impact of non-coding variants on transcription
factor-DNA binding.- aBayesQR: A Bayesian method for reconstruction of viral
populations characterized by low diversity.- BeWith: A between-within method
for module discovery in cancer using integrated analysis of mutual
exclusivity, co-occurrence and functional interactions.- K-mer Set Memory
(KSM) motif representation enables accurate prediction of the impact of
regulatory variants.- Network-based coverage of mutational profiles reveals
cancer genes.- Ultra-accurate complex disorder prediction: case study of
neurodevelopmental disorders.- Inference of the human polyadenylation Code.-
Folding membrane proteins by deep transfer learning.- A network integration
approach for drug-target interaction prediction and computational drug
repositioning from heterogeneous information.- Epistasis in genomic and
survival data of cancer patients.- Ultra-fast identity by descent detection
in biobank-scale cohorts using positional burrows-wheeler transform.- Joker
de Bruijn: sequence libraries to cover all k-mers using joker characters.-
GATTACA: Lightweight metagenomic binning using kmer counting.- Species tree
estimation using ASTRAL: how many genes are enough.-Reconstructing antibody
repertoires from error-prone immune-sequencing datasets.- NetREX: Network
rewiring using EXpression - Towards context specific regulatory networks.- E
pluribus unum: United States of single cells.- ROSE: a deep learning based
framework for predicting ribosome stalling.