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In this study, we have adapted our module network algorithm to take as input a heterogeneous dataset of both miRNA and messenger RNA (mRNA) expression data measured on the same samples.
It has been shown to outperform the original module network algorithm [ 8].
In this study, we have applied a module network algorithm to a large expression data set measured on lymphoblastoid cell lines coming from patients having different forms of prostate cancer.
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We designed and tested the LeMoNe (Learning Module Networks) algorithm in previous studies (Joshi et al., 2008, 2009; Michoel et al., 2007).
Using the Module Networks algorithm [36], they identified 80 modules or gene sets of tightly coexpressed genes with distinct expression patterns and enrichment for specific biological functions, which they termed induction patterns.
Examples of the latter approach include the LeMoNe (Learning Module Networks) algorithm of Joshi et al. [ 16] which generates a number of possible models explaining regulation activity and with every single model containing many regulators.
The LeMoNe (Learning Module Networks) algorithm [ 8] uses probabilistic, ensemble-based optimization techniques [ 8, 9] to infer high-quality module networks [ 3], where genes are first partitioned into coexpression modules and regulators are assigned to modules based on how well they explain the condition-dependent expression behavior of the module.
We have applied a robust and unbiased module network inference algorithm to a cancer-related expression data set of both mRNAs and miRNAs.
During the last decade, much progress has been made in the development of robust and powerful module network inference algorithms.
This module exploits a neural network algorithm to find the best location for placing the spheres within the atomic coordinates [33].
The module includes temperatures as inputs, neural network algorithm for computing the thermal deformations errors, 'C' programming for real-time calculations and integration with open architecture CNC controller.
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