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SPICi specifications

Information


Unique identifier OMICS_21738
Name SPICi
Alternative name Speed and Performance In Clustering
Interface Web user interface
Restrictions to use None
Input data A network.
Output data A cluster.
Computer skills Basic
Stability Stable
Maintained Yes

Maintainer


  • person_outline Mona Singh

Publication for Speed and Performance In Clustering

SPICi citations

 (28)
library_books

HipMCL: a high performance parallel implementation of the Markov clustering algorithm for large scale networks

2018
Nucleic Acids Res
PMCID: 5888241
PMID: 29315405
DOI: 10.1093/nar/gkx1313

[…] g of large-scale networks a real challenge.Indeed, despite the great variety of graph-based clustering algorithms available today (,), only a few manage to handle networks of million nodes and edges. SPICi () for example, is a fast, local network clustering algorithm that detects densely connected communities within a network. It is one of the fastest graph-based clustering algorithms and runs in […]

library_books

Protein complex prediction for large protein protein interaction networks with the CoreandPeel method

2016
BMC Bioinformatics
PMCID: 5123419
PMID: 28185552
DOI: 10.1186/s12859-016-1191-6

[…] ). Large PPIN can be challenging for clustering algorithms as many of them have been designed and tested in the original publication with PPIN of small and medium size (with the possible exception of SPICi ([]), that was designed intentionally for large PPIN). Greedy methods that optimize straightforward local conditions may be fast but speed may penalize quality. Thus, although more than a decade […]

library_books

LPRP: A Gene–Gene Interaction Network Construction Algorithm and Its Application in Breast Cancer Data Analysis

2016
Interdiscip Sci
PMCID: 5838217
PMID: 27640171
DOI: 10.1007/s12539-016-0185-4

[…] ular functions. By detecting clusters in both the normal and tumor final GGI networks, specific tumor functional modules can be revealed. Many cluster detection algorithms have been proposed, such as SPICi [], GECluster [], MCODE [] and MINE []. MINE outperforms MCODE, SPICi and many other methods in identifying non-exclusive, high modularity clusters and can be easily run on Cytoscape software. M […]

library_books

Protein complex detection based on partially shared multi view clustering

2016
BMC Bioinformatics
PMCID: 5022186
PMID: 27623844
DOI: 10.1186/s12859-016-1164-9

[…] In this section, we compare PSMVC with 9 existing state-of-the-art graph clustering algorithms that detect protein complexes from PI data, which include CMC [], ClusterONE [], MCODE [], MINE [], SPICi [], Linkcomm [], MF-PINCoC [], PINCoC [] and RANCoC []. As only few methods can handle weighted networks, we apply these methods on the original unweighted PPI network. We also compare PSMVC wit […]

library_books

Identification of protein complexes from multi relationship protein interaction networks

2016
Hum Genomics
PMCID: 4965713
PMID: 27461193
DOI: 10.1186/s40246-016-0069-z

[…] ormance among all these methods in terms of precision, recall, and F-measure. The F-measure of our method is 0.5, which is 68.63, 33.52, 45.53, 69.71, and 47.73 % higher than that of CMC, COACH, RRW, SPICi, and ClusterONE, respectively. […]

library_books

Differential expression of Spiroplasma citri surface protein genes in the plant and insect hosts

2016
BMC Microbiol
PMCID: 4804543
PMID: 27005573
DOI: 10.1186/s12866-016-0666-y

[…] n of them significantly changed once the spiroplasmas were introduced in insects, indicating the strong transcriptional response of S. citri to environmental changes. Among these genes, 12 (including SPICI11_003 (sc76) and SPICI12 _021 (oppA)) were up-regulated, while 3 were down-regulated in insects. These were operationally defined as “insect-up-regulated” and “insect-down-regulated”, respective […]

Citations

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SPICi institution(s)
Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA; Department of Computer Science, Princeton University, Princeton, NJ, USA
SPICi funding source(s)
Supported by National Science Foundation (ABI-0850063, in part); National Institutes of Health (GM076275, in part); National Institute of Health Center of Excellence (grant P50 GM071508, in part).

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