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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 Alignment Free Sequence Comparison with Bounded Mismatches
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    Chapter 2 DockStar: A Novel ILP Based Integrative Method for Structural Modelling of Multimolecular Protein Complexes (Extended Abstract)
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    Chapter 3 CRISPR Detection from Short Reads Using Partial Overlap Graphs
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    Chapter 4 HapTree-X: An Integrative Bayesian Framework for Haplotype Reconstruction from Transcriptome and Genome Sequencing Data
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    Chapter 5 Read Clouds Uncover Variation in Complex Regions of the Human Genome
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    Chapter 6 Learning Microbial Interaction Networks from Metagenomic Count Data
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    Chapter 7 Immunoglobulin Classification Using the Colored Antibody Graph
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    Chapter 8 CIDANE: Comprehensive Isoform Discovery and Abundance Estimation
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    Chapter 9 Diffusion Component Analysis: Unraveling Functional Topology in Biological Networks
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    Chapter 10 Fragmentation Trees Reloaded
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    Chapter 11 KGSrna: Efficient 3D Kinematics-Based Sampling for Nucleic Acids
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    Chapter 12 Locating a Tree in a Phylogenetic Network in Quadratic Time
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    Chapter 13 Constructing Structure Ensembles of Intrinsically Disordered Proteins from Chemical Shift Data
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    Chapter 14 Comets (Constrained Optimization of Multistate Energies by Tree Search): A Provable and Efficient Algorithm to Optimize Binding Affinity and Specificity with Respect to Sequence
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    Chapter 15 Efficient and Accurate Multiple-Phenotypes Regression Method for High Dimensional Data Considering Population Structure
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    Chapter 16 BWM*: A Novel, Provable, Ensemble-Based Dynamic Programming Algorithm for Sparse Approximations of Computational Protein Design
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    Chapter 17 An Efficient Nonlinear Regression Approach for Genome-Wide Detection of Marginal and Interacting Genetic Variations
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    Chapter 18 Exploration of Designability of Proteins Using Graph Features of Contact Maps: Beyond Lattice Models
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    Chapter 19 CoMEt: A Statistical Approach to Identify Combinations of Mutually Exclusive Alterations in Cancer
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    Chapter 20 Deep Feature Selection: Theory and Application to Identify Enhancers and Promoters
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    Chapter 21 Protein Contact Prediction by Integrating Joint Evolutionary Coupling Analysis and Supervised Learning
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    Chapter 22 ScaffMatch: Scaffolding Algorithm Based on Maximum Weight Matching
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    Chapter 23 A Symmetric Length-Aware Enrichment Test
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    Chapter 24 Functional Alignment of Metabolic Networks
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    Chapter 25 Joint Inference of Genome Structure and Content in Heterogeneous Tumor Samples
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    Chapter 26 Ultra-Large Alignments Using Ensembles of Hidden Markov Models
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    Chapter 27 Topological Signatures for Population Admixture
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    Chapter 28 Haplotype Allele Frequency (HAF) Score: Predicting Carriers of Ongoing Selective Sweeps Without Knowledge of the Adaptive Allele
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    Chapter 29 Gap Filling as Exact Path Length Problem
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    Chapter 30 Deconvolution of Ensemble Chromatin Interaction Data Reveals the Latent Mixing Structures in Cell Subpopulations
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    Chapter 31 A Fast and Exact Algorithm for the Exemplar Breakpoint Distance
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    Chapter 32 Deciding When to Stop: Efficient Experimentation to Learn to Predict Drug-Target Interactions (Extended Abstract)
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    Chapter 33 On the Sample Complexity of Cancer Pathways Identification
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    Chapter 34 A Novel Probabilistic Methodology for eQTL Analysis of Signaling Networks
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    Chapter 35 Rapidly Registering Identity-by-Descent Across Ancestral Recombination Graphs
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    Chapter 36 Computational Protein Design Using AND/OR Branch-and-Bound Search
Attention for Chapter 14: Comets (Constrained Optimization of Multistate Energies by Tree Search): A Provable and Efficient Algorithm to Optimize Binding Affinity and Specificity with Respect to Sequence
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Chapter title
Comets (Constrained Optimization of Multistate Energies by Tree Search): A Provable and Efficient Algorithm to Optimize Binding Affinity and Specificity with Respect to Sequence
Chapter number 14
Book title
Research in Computational Molecular Biology
Published in
Lecture notes in computer science, March 2015
DOI 10.1007/978-3-319-16706-0_14
Book ISBNs
978-3-31-916705-3, 978-3-31-916706-0
Authors

Hallen, Mark A., Donald, Bruce R., Mark A. Hallen, Bruce R. Donald

Editors

Teresa M. Przytycka

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 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 1 Mendeley reader of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 1 100%

Demographic breakdown

Readers by professional status Count As %
Student > Doctoral Student 1 100%
Readers by discipline Count As %
Agricultural and Biological Sciences 1 100%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 28 September 2015.
All research outputs
#15,350,522
of 22,833,393 outputs
Outputs from Lecture notes in computer science
#4,648
of 8,127 outputs
Outputs of similar age
#157,049
of 263,397 outputs
Outputs of similar age from Lecture notes in computer science
#56
of 89 outputs
Altmetric has tracked 22,833,393 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 8,127 research outputs from this source. They receive a mean Attention Score of 5.0. This one is in the 27th percentile – i.e., 27% of its peers scored the same or lower than it.
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 263,397 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 31st percentile – i.e., 31% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 89 others from the same source and published within six weeks on either side of this one. This one is in the 24th percentile – i.e., 24% of its contemporaries scored the same or lower than it.