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We have adopted FARI to compare allocations of multi-membership genes produced by separate runs of our algorithms on the same microarray dataset, given that each arrangement may place a gene in one or more pathways.
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Ideker et al. [ 2] have developed a method called Active Modules, which employs simulated annealing to search for connected components of a protein-protein interaction (PPI) network that are significantly changed according to some microarray dataset, thus giving an insight into how a coordinated response occurs in the PPI network without the need for such predefined pathways or gene-sets.
All microarray datasets have been uploaded to the GEO database.
To this end, we took advantage of a recently developed computational algorithm, named GENOMICA [19], which makes it possible to quantify and statistically evaluate the enrichment of one or more gene sets in all samples of a given microarray dataset compendium (Figure 3a).
The given microarray dataset was used as the total gene dataset.
Gene expression signatures, which are capable of predicting the state of a sample from a given microarray dataset, are the emerging technology for developing cancer therapeutics.
The mean and standard deviation of the total difference metric values for a given microarray dataset are calculated for all genes.
To obtain data per cycling cells from a given microarray dataset (Fig. 1B, total gene dataset), a subdataset is created by extracting the expression values of CCScycling genes (Fig. 1B, cycling gene dataset).
Data for many probesets in a given expression microarray dataset are comprised of noise but little or no biological signal.
Given a microarray dataset for ER+ breast cancer tumors S, based on the Affymetrix platform hgu133a or hgu133plus2, the accelerated progression subtype is defined as follows: 1.
Given a microarray dataset for a cohort S and a subset of samples S0 balanced for relapse, thresholds are determined for the 4 probes in AP4 using the expression vectors restricted to S0.
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