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Research in Computational Molecular Biology

Overview of attention for book
Cover of 'Research in Computational Molecular Biology'

Table of Contents

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    Book Overview
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    Chapter 1 The Second Decade of the International Conference on Research in Computational Molecular Biology (RECOMB)
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    Chapter 2 A MAD-Bayes Algorithm for State-Space Inference and Clustering with Application to Querying Large Collections of ChIP-Seq Data Sets
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    Chapter 3 Accurate Recovery of Ribosome Positions Reveals Slow Translation of Wobble-Pairing Codons in Yeast
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    Chapter 4 Multitask Matrix Completion for Learning Protein Interactions Across Diseases
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    Chapter 5 pathTiMEx: Joint Inference of Mutually Exclusive Cancer Pathways and Their Dependencies in Tumor Progression
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    Chapter 6 Clonality Inference from Single Tumor Samples Using Low Coverage Sequence Data
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    Chapter 7 Flexible Modelling of Genetic Effects on Function-Valued Traits
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    Chapter 8 MetaFlow: Metagenomic Profiling Based on Whole-Genome Coverage Analysis with Min-Cost Flows
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    Chapter 9 LUTE (Local Unpruned Tuple Expansion): Accurate Continuously Flexible Protein Design with General Energy Functions and Rigid-rotamer-like Efficiency
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    Chapter 10 Improving Bloom Filter Performance on Sequence Data Using \(k\) -mer Bloom Filters
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    Chapter 11 Safe and Complete Contig Assembly Via Omnitigs
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    Chapter 12 Long Single-Molecule Reads Can Resolve the Complexity of the Influenza Virus Composed of Rare, Closely Related Mutant Variants
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    Chapter 13 Structural Variation Detection with Read Pair Information—An Improved Null-Hypothesis Reduces Bias
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    Chapter 14 On Computing Breakpoint Distances for Genomes with Duplicate Genes
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    Chapter 15 New Genome Similarity Measures Based on Conserved Gene Adjacencies
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    Chapter 16 Fast Phylogenetic Biodiversity Computations Under a Non-uniform Random Distribution
Attention for Chapter 10: Improving Bloom Filter Performance on Sequence Data Using \(k\) -mer Bloom Filters
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (72nd percentile)
  • Good Attention Score compared to outputs of the same age and source (79th percentile)

Mentioned by

blogs
1 blog

Citations

dimensions_citation
4 Dimensions

Readers on

mendeley
19 Mendeley
citeulike
1 CiteULike
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Chapter title
Improving Bloom Filter Performance on Sequence Data Using \(k\) -mer Bloom Filters
Chapter number 10
Book title
Research in Computational Molecular Biology
Published in
Lecture notes in computer science, April 2016
DOI 10.1007/978-3-319-31957-5_10
Book ISBNs
978-3-31-931956-8, 978-3-31-931957-5
Authors

David Pellow, Darya Filippova, Carl Kingsford, Pellow, David, Filippova, Darya, Kingsford, Carl

Editors

Mona Singh

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 19 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 5%
Germany 1 5%
France 1 5%
Unknown 16 84%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 47%
Researcher 5 26%
Student > Doctoral Student 1 5%
Student > Master 1 5%
Student > Postgraduate 1 5%
Other 0 0%
Unknown 2 11%
Readers by discipline Count As %
Computer Science 12 63%
Biochemistry, Genetics and Molecular Biology 3 16%
Mathematics 1 5%
Materials Science 1 5%
Unknown 2 11%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 13 April 2016.
All research outputs
#5,756,433
of 22,860,626 outputs
Outputs from Lecture notes in computer science
#1,872
of 8,127 outputs
Outputs of similar age
#81,844
of 300,819 outputs
Outputs of similar age from Lecture notes in computer science
#24
of 122 outputs
Altmetric has tracked 22,860,626 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 8,127 research outputs from this source. They receive a mean Attention Score of 5.0. This one has done well, scoring higher than 76% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 300,819 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.
We're also able to compare this research output to 122 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 79% of its contemporaries.