Comparing the Similarity of Time-Series Gene Expression Using Signal Processing Metrics is a research paper published in Journal of Biomedical Informatics (2001). On theSindex it has a DataRank of 2.1. It has been cited 35 times, with 32 citing works in its 1-hop citation network.
Many algorithms have been used to cluster genes measured by microarray across a time series. Instead of clustering, our goal was to compare all pairs of genes to determine whether there was evidence of a phase shift between them. We describe a technique where gene expression is treated as a discrete time-invariant signal, allowing the use of digital signal-processing tools, including power spectral density, coherence, and transfer gain and phase shift. We used these on a public RNA expression set of 2467 genes measured every 7 min for 119 min and found 18 putative associations. Two of these were known in the biomedical literature and may have been missed using correlation coefficients. Digital signal processing tools can be embedded and enhance existing clustering algorithms.
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Base Score Contribution
0.538
From this paper's citation signal
Citation Network Contribution
1.6
From 26 citing papers with measurable signal
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 26% comes from its base citations and 74% from the citation network (26 citing papers contributed measurable signal).
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.
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