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SVEN: Informative Visual Representation of Complex Dynamic Structure
[article]
2014
arXiv
pre-print
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SVEN encodes time in a natural manner, along the horizontal axis, and optimizes the vertical placement of storylines to decrease clutter (line crossings, straightness, and bends) in the drawing. ...
However, both of these classical techniques have significant drawbacks, so a new approach, Storyline Visualization of Events on a Network (SVEN) is proposed. ...
Acknowledgements The authors would like to thank Paul Havig at AFRL for his guidance and helpful comments throughout the project. ...
arXiv:1412.6706v1
fatcat:agdo5nq7h5cfdfud46cs54bty4
Scalability of Parallel Genetic Algorithm for Two-mode Clustering
2014
International Journal of Computer Applications
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With the popularity of use of two-mode data matrices where the rows and columns have different sets of entities, the need for simultaneous clustering of rows and columns popularly known as two-mode clustering ...
Data matrix having the same set of entity in the rows and cloumns is known as one-mode data matrix, and traditional one-mode clustering algorithms can be used to cluster the rows (or columns) separately ...
Fund for Doctoral Studies and Internationalisation Programme DoRa. ...
doi:10.5120/16411-5829
fatcat:w3fkmrkzhbdivaug3qxnd3u42a
Building pattern recognition applications with the SPARE library
[article]
2015
arXiv
pre-print
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The library follows the requirement of the generality: most of the implemented algorithms are able to process user-defined input data types transparently, such as labeled graphs and sequences of objects ...
Here we present a high-level picture of the SPARE library characteristics, focusing instead on the specific practical possibility of constructing pattern recognition systems for different input data types ...
Sirabella, and A. Colosimo. Nonlinear Signal Analysis
Methods in the Elucidation of Protein Sequence—Structure Relationships. ChemInform, 33(28):1471–1492, 2002. ...
arXiv:1410.5263v2
fatcat:h3n5jobr5jfwva5imx3wgi755i
Toward a multilevel representation of protein molecules: Comparative approaches to the aggregation/folding propensity problem
2016
Information Sciences
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2015 pre-print arXiv:1407.7559v2 fatcat:6id2ftl6yve6nhwko2vxj6ipaqOther Versions
of contact graph spectra for folding behavior discrimination and characterization of the E. coli solubility data. ...
The soundness of the experimental results presented in this paper is proved by the statistically relevant relationships discovered among the chemico-physical description of proteins and the developed cost ...
The application of Guimerà and Amaral network cartography [17] on a large set of proteins, demonstrated the existence of a common amino acid residues role subdivision for all protein molecules [42] ...
doi:10.1016/j.ins.2015.07.043
fatcat:ss77btll7zbznmbvcxffs7hrcu
A comparison of vertex ordering algorithms for large graph visualization
2007
2007 6th International Asia-Pacific Symposium on Visualization
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We also provide a detailed discussion of the results for each algorithm across the different graph types and include a discussion of some strategies for using ordering algorithms for data analysis based ...
In this study, we examine the use of graph ordering algorithms for visual analysis of data sets using visual similarity matrices. ...
Doug Gregor provided support for the Boost Graph Library and its Python bindings. ...
doi:10.1109/apvis.2007.329289
dblp:conf/apvis/MuellerML07
fatcat:vmv4fgekmzajriawtw5xp3m7dy
One-Class Classifiers Based on Entropic Spanning Graphs
2017
IEEE Transactions on Neural Networks and Learning Systems
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In addition, we apply the method to the protein solubility recognition problem by considering several representations for the input samples. ...
Experimental results demonstrate the effectiveness and versatility of the proposed method with respect to other state-of-the-art approaches. ...
Analysis of Computational Complexity We assume to perform the global optimization in Algorithm 1 with a genetic algorithm. ...
doi:10.1109/tnnls.2016.2608983
pmid:28114079
fatcat:eprjle7rpzdgzhosow2xfm6wxq
A Deep Generative Model for Reordering Adjacency Matrices
[article]
2022
arXiv
pre-print
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2021 pre-print arXiv:2110.04971v1 fatcat:6l22zzcq3zfrtalf3ewfhcxw7mOther Versions
However, this trial-and-error approach is laborious and disorganized, which is especially challenging for novices. ...
Most existing research in this area generally focused on developing algorithms that can compute "better" matrix reorderings for particular circumstances. ...
INTRODUCTION G RAPH-STRUCTURED data are widely found in many disciplines; examples include protein-protein interaction networks in biological science, brain networks in neuroscience , and friendship networks ...
arXiv:2110.04971v2
fatcat:3ac4h5ggyzagnevhs3be4wmw2m
Differential expression analysis for sequence count data
2010
Genome Biology
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High-throughput DNA sequencing is a powerful and versatile new technology for obtaining comprehensive and quantitative data about RNA expression (RNA-Seq), protein-DNA binding (ChIP-Seq), and genetic variations ...
It addresses essentially all of the use cases that microarrays were applied to in the past, but produces more detailed and more comprehensive results. ...
The talk will present a family of criteria and related optimization algorithms which can be used to choose the category orders for 2-and k-dimensional categorical classification data with respect to their ...
doi:10.1186/gb-2010-11-10-r106
pmid:20979621
pmcid:PMC3218662
fatcat:ala2sfzzbnfzfgeompuuveneca
COMBat: Visualizing co-occurrence of annotation terms
2013
2013 IEEE Symposium on Biological Data Visualization (BioVis)
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By re-arranging the rows and columns of this matrix, and color-coding the cell contents, patterns become visible. ...
about the location and size of this zoomed region relative to the whole matrix. ...
Key to revealing structure is to permute the rows and columns by suitable reordering algorithms, referred to as seriation methods. ...
doi:10.1109/biovis.2013.6664342
dblp:conf/biovis/BrakelFFWW13
fatcat:3mijirvjebawlfm4rdwmh3c36a
Single cell proteomics in biomedicine: High-dimensional data acquisition, visualization, and analysis
2017
Proteomics
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The resultant high-dimensional single cell data generated by these tools require new theoretical approaches and analytical algorithms for effective visualization and interpretation. ...
and computational approaches developed to date for dissecting the high-dimensional single cell data. ...
Acknowledgement The authors acknowledge the following funding agencies and grants for support some of the work ...
doi:10.1002/pmic.201600267
pmid:28128880
pmcid:PMC5554115
fatcat:3vd2xx5hdrghbptr63tb3oc5wu
A primer on high-dimensional data analysis workflows for studying visual cortex development and plasticity
[article]
2019
bioRxiv
pre-print
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Here we discuss two workflows and provide example R code for analyzing high-dimensional changes in a group of proteins (or genes) using two data sets. ...
New techniques for quantifying large numbers of proteins or genes are transforming the study of plasticity mechanisms in visual cortex (V1) into the era of big data. ...
The analysis was done in R Studio using a series of packages available for download at the Comprehensive R Archive Network (CRAN). ...
doi:10.1101/554378
fatcat:hzsvzlusenb5tawpsm3mkscpsi
Visual Approaches for Exploratory Data Analysis: A Survey of the Visual Assessment of Clustering Tendency (VAT) Family of Algorithms
2020
IEEE Systems Man and Cybernetics Magazine
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Web analytics is the measurement, collection, analysis, and reporting of web data for purposes of understanding and optimizing web usage. ...
They propose an extraction of clusters from the ordered dissimilarity data (CLODD) algorithm, which uses particle-swarm optimization to find optimal clusters from the aligned partitions. ...
doi:10.1109/msmc.2019.2961163
fatcat:jvqnvhjgjndyrn3cx6ehcigueu
TSP- Infrastructure for the Traveling Salesperson Problem
2007
Journal of Statistical Software
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The package features S3 classes for specifying a TSP and its (possibly optimal) solution as well as several heuristics to find good solutions. ...
The main application in statistics is combinatorial data analysis, e.g., reordering rows and columns of data matrices or identifying clusters. ...
Acknowledgments The authors of this paper want to thank Roger Bivand for providing the code to correctly draw tours and paths on a projected map. ...
doi:10.18637/jss.v023.i02
fatcat:v66e5drlarhtvbr3dhf54hjmfi
The consensus molecular subtypes of colorectal cancer
2015
Nature Medicine
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Acknowledgments The authors would like to thank the goodwill and generosity of the colorectal research community who made this study possible. J.G. and S.H. ...
The network generated is partitioned into clusters using MCL (Markov cluster algorithm) 11, 12 , which is a scalable and efficient unsupervised cluster algorithm for networks. c. ...
), overexpression of extracellular matrix proteins on RPPA analysis (SupplementaryTable 7), and higher admixture with non-cancer cells, as measured by the ABSOLUTE algorithm 20 (Supplementary Fig. 7, ...
doi:10.1038/nm.3967
pmid:26457759
pmcid:PMC4636487
fatcat:aiw3ky5zarad7nb6abs2hh5bbu
Review of statistical network analysis: models, algorithms, and software
2012
Statistical analysis and data mining
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The analysis of network data is an area that is rapidly growing, both within and outside of the discipline of statistics. ...
This review provides a concise summary of methods and models used in the statistical analysis of network data, including the Erdős-Renyi model, the exponential family class of network models and recently ...
Network analysis has also become a popular method of investigation in the biological sciences in terms of protein-protein, gene-gene and gene-protein interaction networks. ...
doi:10.1002/sam.11146
fatcat:x6ongeghznf6doecsyuyxjrdpm
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