Improved Motif Detection in Large Sequence Sets with Random Sampling in a Kepler workflow

Author:

Köhler Sven,Seitzer Phillip,Facciotti Marc T.,Ludäscher Bertram

Publisher

Elsevier BV

Subject

General Engineering

Reference3 articles.

1. http://www.bme.ucdavis.edu/facciotti/resources data/software/.

2. P. Seitzer, D. Larsen, E. Wilbanks, M. Facciotti, A monte carlo-based framework to enhance the discovery and interpretation of regulatory sequence motifs.

3. J. Wang, D. Crawl, I. Altintas, Kepler + Hadoop: a general architecture facilitating data-intensive applications in scientific workflow systems, in: Proceedings of the 4th Workshop on Workflows in Support of Large-Scale Science, WORKS’09, ACM, New York, NY, USA, 2009, pp. 12:1-12:8.

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1. Conversational Pattern Mining Using Motif Detection;Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering;2023

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