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By integrating gene expression array and ChIP-on-chip data, the authors show that NOTCH1 directly activates multiple biosynthetic routes and induces c-MYC gene expression.
In this paper we describe an analysis strategy which addresses this issue by integrating gene expression profiling measurements in the logical framework of QSAR analysis.
In this study, we identified the signaling networks where these genes are involved by integrating gene expression profiling data, derived from bone marrow of lupus patients and healthy individuals [5], using bioinformatic approaches.
Next, we used the Web-based on line tool IPA to identify the set of biological pathways that were regulated in our experiment by integrating gene expression profiles using gene ontology (GO).
We therefore propose that by integrating gene expression profiles with chemical feature information it may be possible to isolate a sub-group of features that are highly important in characterizing specific phenotypic effects allowing for a much better characterization of yet untested chemicals.
Gao et al. identified CRGs by integrating gene expression and transcription factor binding data [ 18].
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We believe that our research shed new light on similar gene expression profile search over rapidly growing large microarray databases by showing that integrating gene expression profile with external data such as pathway could improve search accuracy.
The database is handled by a PostgreSQL server, which integrates gene expression and clinical data simultaneously.
It provides gene lists and hierarchical matrices using co-expression analysis by distance-base clustering (k-means, hierarchical clustering), as well as integrated gene expression analyses by mapping observed gene expression changes onto specific signaling and metabolic pathways.
To identify a tissue specific set of metabolic reactions we applied the algorithm by Shlomi et al. [ 6], which integrates gene expression data with linear optimization problem.
The difference in the number of genes found in our study compared to previous studies [ 1, 38, 47, 48] could be explained by the high resolution aCGH 244K we used (whereas 2464 BAC arrays [OncoBAC array] and 32K BAC arrays were previously used [[ 1] and [ 38, 47, 48], respectively]) and by integrating gene level with gene expression analysis.
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