<HashMap><database>biostudies-literature</database><scores/><additional><omics_type>Unknown</omics_type><volume>14(1)</volume><submitter>Islam MT</submitter><pubmed_abstract>Remarkable advances in single cell genomics have presented unique challenges and opportunities for interrogating a wealth of biomedical inquiries. High dimensional genomic data are inherently complex because of intertwined relationships among the genes. Existing methods, including emerging deep learning-based approaches, do not consider the underlying biological characteristics during data processing, which greatly compromises the performance of data analysis and hinders the maximal utilization of state-of-the-art genomic techniques. In this work, we develop an entropy-based cartography strategy to contrive the high dimensional gene expression data into a configured image format, referred to as genomap, with explicit integration of the genomic interactions. This unique cartography casts th</pubmed_abstract><journal>Nature communications</journal><pagination>679</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC9908983</full_dataset_link><repository>biostudies-literature</repository><pubmed_title>Cartography of Genomic Interactions Enables Deep Analysis of Single-Cell Expression Data.</pubmed_title><pmcid>PMC9908983</pmcid><pubmed_authors>Xing L</pubmed_authors><pubmed_authors>Islam MT</pubmed_authors></additional><is_claimable>false</is_claimable><name>Cartography of Genomic Interactions Enables Deep Analysis of Single-Cell Expression Data.</name><description>Remarkable advances in single cell genomics have presented unique challenges and opportunities for interrogating a wealth of biomedical inquiries. High dimensional genomic data are inherently complex because of intertwined relationships among the genes. Existing methods, including emerging deep learning-based approaches, do not consider the underlying biological characteristics during data processing, which greatly compromises the performance of data analysis and hinders the maximal utilization of state-of-the-art genomic techniques. In this work, we develop an entropy-based cartography strategy to contrive the high dimensional gene expression data into a configured image format, referred to as genomap, with explicit integration of the genomic interactions. This unique cartography casts th</description><dates><release>2023-01-01T00:00:00Z</release><publication>2023 Feb</publication><modification>2026-03-18T13:46:39.069Z</modification><creation>2025-04-04T18:43:37.178Z</creation></dates><accession>S-EPMC9908983</accession><cross_references><pubmed>36755047</pubmed><doi>10.1038/s41467-023-36383-6</doi></cross_references></HashMap>