Research in Computational Molecular Biology : 21st Annual International Conference, RECOMB 2017, Hong Kong, China, May 3-7, 2017, Proceedings /
| Autor Corporativo: | |
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| Otros Autores: | |
| Formato: | eBook |
| Lenguaje: | English |
| Publicado: |
Cham :
Springer International Publishing : Imprint: Springer,
2017.
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| Edición: | 1st ed. 2017. |
| Colección: | Lecture Notes in Bioinformatics ;
10229 |
| Materias: |
Tabla de Contenidos:
- 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. .