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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
Overall attention for this book and its chapters
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (83rd percentile)
  • Good Attention Score compared to outputs of the same age and source (76th percentile)

Mentioned by

blogs
1 blog
twitter
3 X users

Citations

dimensions_citation
4 Dimensions

Readers on

mendeley
22 Mendeley
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Title
Research in Computational Molecular Biology
Published by
Lecture notes in computer science, January 2016
DOI 10.1007/978-3-319-31957-5
ISBNs
978-3-31-931956-8, 978-3-31-931957-5
Editors

Mona Singh

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 22 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 22 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 4 18%
Student > Bachelor 3 14%
Student > Ph. D. Student 1 5%
Student > Postgraduate 1 5%
Unknown 13 59%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 4 18%
Computer Science 2 9%
Linguistics 1 5%
Agricultural and Biological Sciences 1 5%
Physics and Astronomy 1 5%
Other 0 0%
Unknown 13 59%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 9. 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 23 January 2017.
All research outputs
#4,306,167
of 26,017,215 outputs
Outputs from Lecture notes in computer science
#889
of 8,229 outputs
Outputs of similar age
#66,755
of 405,194 outputs
Outputs of similar age from Lecture notes in computer science
#140
of 585 outputs
Altmetric has tracked 26,017,215 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 8,229 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.3. This one has done well, scoring higher than 89% 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 405,194 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 83% of its contemporaries.
We're also able to compare this research output to 585 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 76% of its contemporaries.