ArticleProceedings of the ... Symposium on Applied Computing. Symposium on Applied Computing2017
Ranking Novel Regulatory Genes in Gene Expression Profiles using NetExpress.
Article in Proceedings of the ... Symposium on Applied Computing. Symposium on Applied Computing, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
3 authors.
Funding
Abstract
Understanding gene regulation by identifying gene products and determining their roles in regulatory networks is a complex process. A common computational method is to reverse engineer a regulatory network from gene expression profile, and sanitize the network using known information about the genes, their interactions and other properties to filter out unlikely interactors. Unfortunately, due to limited resources most gene expression studies have a limited and small number of time points, and most reverse engineering tools are unable to handle large numbers of genes. Both of these factors play significant roles in influencing the accuracy of the process. In this paper, we present a new gene ranking algorithm from gene expression profiles with a small number of time points so that the most relevant genes can be selected for reverse engineering. We also present a graphical interface called
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.