@article{
 id = {0a8b4bd2-ff46-37f9-8b5f-35d3f48a7c28},
 title = {Kavosh: a new algorithm for finding network motifs.},
 type = {article},
 year = {2009},
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 keywords = {Algorithms,Computational Biology,Computational Biology: methods,Escherichia coli,Escherichia coli: genetics,Neural Networks (Computer),Saccharomyces cerevisiae,Saccharomyces cerevisiae: genetics,Software},
 created = {2011-10-20T01:06:25.000Z},
 volume = {10},
 websites = {http://www.biomedcentral.com/1471-2105/10/318/},
 month = {1},
 accessed = {2011-06-20},
 file_attached = {false},
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 last_modified = {2014-01-29T14:39:06.000Z},
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 citation_key = {Kavosh},
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 abstract = {Complex networks are studied across many fields of science and are particularly important to understand biological processes. Motifs in networks are small connected sub-graphs that occur significantly in higher frequencies than in random networks. They have recently gathered much attention as a useful concept to uncover structural design principles of complex networks. Existing algorithms for finding network motifs are extremely costly in CPU time and memory consumption and have practically restrictions on the size of motifs.},
 bibtype = {article},
 author = {Kashani, Zahra Razaghi Moghadam and Ahrabian, Hayedeh and Elahi, Elahe and Nowzari-Dalini, Abbas and Ansari, Elnaz Saberi and Asadi, Sahar and Mohammadi, Shahin and Schreiber, Falk and Masoudi-Nejad, Ali},
 journal = {BMC bioinformatics},
 number = {318}
}

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