GraphAlignment: Bayesian pairwise alignment of biological networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68378050%3A_____%2F12%3A00441728" target="_blank" >RIV/68378050:_____/12:00441728 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1186/1752-0509-6-144" target="_blank" >http://dx.doi.org/10.1186/1752-0509-6-144</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1186/1752-0509-6-144" target="_blank" >10.1186/1752-0509-6-144</a>

Jazyk výsledku

angličtina

Název v původním jazyce

GraphAlignment: Bayesian pairwise alignment of biological networks

Popis výsledku v původním jazyce

BACKGROUND: With increased experimental availability and accuracy of bio-molecular networks, tools for their comparative and evolutionary analysis are needed. A key component for such studies is the alignment of networks. RESULTS: We introduce the Bioconductor package GraphAlignment for pairwise alignment of bio-molecular networks. The alignment incorporates information both from network vertices and network edges and is based on an explicit evolutionary model, allowing inference of all scoring parameters directly from empirical data. We compare the performance of our algorithm to an alternative algorithm, Gr?mlin 2.0.On simulated data, GraphAlignment outperforms Gr?mlin 2.0 in several benchmarks except for computational complexity. When there is little or no noise in the data, GraphAlignment is slower than Gr?mlin 2.0. It is faster than Gr?mlin 2.0 when processing noisy data containing spurious vertex associations. Its typical case complexity grows approximately as O(N2.6).On empirica

Název v anglickém jazyce

GraphAlignment: Bayesian pairwise alignment of biological networks

Popis výsledku anglicky

BACKGROUND: With increased experimental availability and accuracy of bio-molecular networks, tools for their comparative and evolutionary analysis are needed. A key component for such studies is the alignment of networks. RESULTS: We introduce the Bioconductor package GraphAlignment for pairwise alignment of bio-molecular networks. The alignment incorporates information both from network vertices and network edges and is based on an explicit evolutionary model, allowing inference of all scoring parameters directly from empirical data. We compare the performance of our algorithm to an alternative algorithm, Gr?mlin 2.0.On simulated data, GraphAlignment outperforms Gr?mlin 2.0 in several benchmarks except for computational complexity. When there is little or no noise in the data, GraphAlignment is slower than Gr?mlin 2.0. It is faster than Gr?mlin 2.0 when processing noisy data containing spurious vertex associations. Its typical case complexity grows approximately as O(N2.6).On empirica

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

EB - Genetika a molekulární biologie

OECD FORD obor

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Projekt

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Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2012

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

BMC Sytems Biology

ISSN

1752-0509

e-ISSN

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Svazek periodika

6

Číslo periodika v rámci svazku

November 21

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

12

Strana od-do

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Kód UT WoS článku

000314922900001

EID výsledku v databázi Scopus

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