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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 Efficient Algorithms for Detecting Signaling Pathways in Protein Interaction Networks
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    Chapter 2 Towards an Integrated Protein-Protein Interaction Network
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    Chapter 3 The Factor Graph Network Model for Biological Systems
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    Chapter 4 Pairwise Local Alignment of Protein Interaction Networks Guided by Models of Evolution
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    Chapter 5 Finding Novel Transcripts in High-Resolution Genome-Wide Microarray Data Using the GenRate Model
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    Chapter 6 Efficient Calculation of Interval Scores for DNA Copy Number Data Analysis
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    Chapter 7 A Regulatory Network Controlling Drosophila Development
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    Chapter 8 Yeast Cells as a Discovery Platform for Neurodegenerative Disease
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    Chapter 9 RIBRA–An Error-Tolerant Algorithm for the NMR Backbone Assignment Problem
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    Chapter 10 Avoiding Local Optima in Single Particle Reconstruction
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    Chapter 11 A High-Throughput Approach for Associating microRNAs with Their Activity Conditions
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    Chapter 12 RNA-RNA Interaction Prediction and Antisense RNA Target Search
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    Chapter 13 Consensus Folding of Unaligned RNA Sequences Revisited
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    Chapter 14 Discovery and Annotation of Genetic Modules
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    Chapter 15 Efficient q -Gram Filters for Finding All ε -Matches over a Given Length
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    Chapter 16 A Polynomial Time Solvable Formulation of Multiple Sequence Alignment
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    Chapter 17 A Fundamental Decomposition Theory for Phylogenetic Networks and Incompatible Characters
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    Chapter 18 Reconstruction of Reticulate Networks from Gene Trees
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    Chapter 19 A Hybrid Micro-Macroevolutionary Approach to Gene Tree Reconstruction
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    Chapter 20 Constructing a Smallest Refining Galled Phylogenetic Network
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    Chapter 21 Mapping Molecular Landscapes Inside Cells
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    Chapter 22 Information Theoretic Approaches to Whole Genome Phylogenies
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    Chapter 23 Maximum Likelihood of Evolutionary Trees Is Hard
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    Chapter 24 Graph Theoretical Insights into Evolution of Multidomain Proteins
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    Chapter 25 Peptide Sequence Tags for Fast Database Search in Mass-Spectrometry
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    Chapter 26 A Hidden Markov Model Based Scoring Function for Mass Spectrometry Database Search
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    Chapter 27 EigenMS: De Novo Analysis of Peptide Tandem Mass Spectra by Spectral Graph Partitioning
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    Chapter 28 Biology as Information
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    Chapter 29 Using Multiple Alignments to Improve Gene Prediction
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    Chapter 30 Learning Interpretable SVMs for Biological Sequence Classification
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    Chapter 31 Segmentation Conditional Random Fields (SCRFs): A New Approach for Protein Fold Recognition
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    Chapter 32 Rapid Protein Side-Chain Packing via Tree Decomposition
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    Chapter 33 Recognition of Binding Patterns Common to a Set of Protein Structures
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    Chapter 34 Predicting Protein-Peptide Binding Affinity by Learning Peptide-Peptide Distance Functions
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    Chapter 35 Amino Acid Sequence Control of the Folding of the Parallel β -Helix, the Simplest β -Sheet Fold
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    Chapter 36 A Practical Approach to Significance Assessment in Alignment with Gaps
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    Chapter 37 Alignment of Optical Maps
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    Chapter 38 Engineering Gene Regulatory Networks: A Reductionist Approach to Systems Biology
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    Chapter 39 Modeling the Combinatorial Functions of Multiple Transcription Factors
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    Chapter 40 Predicting Transcription Factor Binding Sites Using Structural Knowledge
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    Chapter 41 Motif Discovery Through Predictive Modeling of Gene Regulation
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    Chapter 42 HAPLOFREQ – Estimating Haplotype Frequencies Efficiently
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    Chapter 43 Improved Recombination Lower Bounds for Haplotype Data
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    Chapter 44 A Linear-Time Algorithm for the Perfect Phylogeny Haplotyping (PPH) Problem
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    Chapter 45 Human Genome Sequence Variation and the Inherited Basis of Common Disease
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    Chapter 46 Stability of Rearrangement Measures in the Comparison of Genome Sequences
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    Chapter 47 On Sorting by Translocations
Attention for Chapter 23: Maximum Likelihood of Evolutionary Trees Is Hard
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (75th percentile)
  • Good Attention Score compared to outputs of the same age and source (66th percentile)

Mentioned by

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8 X users

Citations

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Chapter title
Maximum Likelihood of Evolutionary Trees Is Hard
Chapter number 23
Book title
Research in Computational Molecular Biology
Published in
Lecture notes in computer science, January 2005
DOI 10.1007/11415770_23
Book ISBNs
978-3-54-025866-7, 978-3-54-031950-4
Authors

Benny Chor, Tamir Tuller

X Demographics

X Demographics

The data shown below were collected from the profiles of 8 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 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Sweden 1 6%
Unknown 16 94%

Demographic breakdown

Readers by professional status Count As %
Student > Master 4 24%
Researcher 3 18%
Professor 3 18%
Student > Ph. D. Student 3 18%
Other 1 6%
Other 1 6%
Unknown 2 12%
Readers by discipline Count As %
Computer Science 7 41%
Biochemistry, Genetics and Molecular Biology 4 24%
Business, Management and Accounting 1 6%
Arts and Humanities 1 6%
Agricultural and Biological Sciences 1 6%
Other 1 6%
Unknown 2 12%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 15 March 2013.
All research outputs
#6,389,271
of 22,701,287 outputs
Outputs from Lecture notes in computer science
#2,112
of 8,125 outputs
Outputs of similar age
#29,025
of 139,383 outputs
Outputs of similar age from Lecture notes in computer science
#42
of 132 outputs
Altmetric has tracked 22,701,287 research outputs across all sources so far. This one has received more attention than most of these and is in the 70th percentile.
So far Altmetric has tracked 8,125 research outputs from this source. They receive a mean Attention Score of 5.0. This one has gotten more attention than average, scoring higher than 72% 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 139,383 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 75% of its contemporaries.
We're also able to compare this research output to 132 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 66% of its contemporaries.