<HashMap><database>biostudies-literature</database><scores/><additional><omics_type>Unknown</omics_type><volume>5(1)</volume><submitter>Aho L</submitter><pubmed_abstract>&lt;h4>Summary&lt;/h4>We present a metric embedding that captures spatiotemporal patterns of cell signaling dynamics in 5D (&lt;i>x&lt;/i>, &lt;i>y&lt;/i>, &lt;i>z&lt;/i>, channel,time) live cell microscopy movies. The embedding uses a metric distance called the normalized information distance (NID) based on Kolmogorov complexity theory, an absolute measure of information content between digital objects. The NID uses statistics of lossless compression to compute a theoretically optimal metric distance between pairs of 5D movies, requiring no &lt;i>a priori&lt;/i> knowledge of expected pattern dynamics, and no training data. The cell signaling structure function (SSF) is defined using a class of metric 3D image filters that compute at each spatiotemporal cell centroid the voxel intensity configuration of the nucleus w.r</pubmed_abstract><journal>Bioinformatics advances</journal><pagination>vbaf063</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC12502908</full_dataset_link><repository>biostudies-literature</repository><pubmed_title>A Kolmogorov metric embedding for live cell microscopy signaling patterns.</pubmed_title><pmcid>PMC12502908</pmcid><pubmed_authors>Aho L</pubmed_authors><pubmed_authors>Cohen AR</pubmed_authors><pubmed_authors>Blum Y</pubmed_authors><pubmed_authors>DeCarlo M</pubmed_authors><pubmed_authors>Gagliardi PA</pubmed_authors><pubmed_authors>Winter M</pubmed_authors><pubmed_authors>Frismantiene A</pubmed_authors><pubmed_authors>Pertz O</pubmed_authors></additional><is_claimable>false</is_claimable><name>A Kolmogorov metric embedding for live cell microscopy signaling patterns.</name><description>&lt;h4>Summary&lt;/h4>We present a metric embedding that captures spatiotemporal patterns of cell signaling dynamics in 5D (&lt;i>x&lt;/i>, &lt;i>y&lt;/i>, &lt;i>z&lt;/i>, channel,time) live cell microscopy movies. The embedding uses a metric distance called the normalized information distance (NID) based on Kolmogorov complexity theory, an absolute measure of information content between digital objects. The NID uses statistics of lossless compression to compute a theoretically optimal metric distance between pairs of 5D movies, requiring no &lt;i>a priori&lt;/i> knowledge of expected pattern dynamics, and no training data. The cell signaling structure function (SSF) is defined using a class of metric 3D image filters that compute at each spatiotemporal cell centroid the voxel intensity configuration of the nucleus w.r</description><dates><release>2025-01-01T00:00:00Z</release><publication>2025</publication><modification>2026-06-04T07:53:40.43Z</modification><creation>2026-05-07T03:08:35.17Z</creation></dates><accession>S-EPMC12502908</accession><cross_references><pubmed>41064112</pubmed><doi>10.1093/bioadv/vbaf063</doi></cross_references></HashMap>