<HashMap><database>biostudies-literature</database><scores/><additional><omics_type>Unknown</omics_type><volume>25(1)</volume><submitter>Huang J</submitter><pubmed_abstract>&lt;h4>Background&lt;/h4>Protein residue-residue distance maps are used for remote homology detection, protein information estimation, and protein structure research. However, existing prediction approaches are time-consuming, and hundreds of millions of proteins are discovered each year, necessitating the development of a rapid and reliable prediction method for protein residue-residue distances. Moreover, because many proteins lack known homologous sequences, a waiting-free and alignment-free deep learning method is needed.&lt;h4>Result&lt;/h4>In this study, we propose a learning framework named FreeProtMap. In terms of protein representation processing, the proposed group pooling in FreeProtMap effectively mitigates issues arising from high-dimensional sparseness in protein representation. In terms</pubmed_abstract><journal>BMC bioinformatics</journal><pagination>176</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC11069170</full_dataset_link><repository>biostudies-literature</repository><pubmed_title>Freeprotmap: waiting-free prediction method for protein distance map.</pubmed_title><pmcid>PMC11069170</pmcid><pubmed_authors>Tang J</pubmed_authors><pubmed_authors>Chen Q</pubmed_authors><pubmed_authors>Li J</pubmed_authors><pubmed_authors>Huang J</pubmed_authors><pubmed_authors>Chen G</pubmed_authors><pubmed_authors>Wang X</pubmed_authors></additional><is_claimable>false</is_claimable><name>Freeprotmap: waiting-free prediction method for protein distance map.</name><description>&lt;h4>Background&lt;/h4>Protein residue-residue distance maps are used for remote homology detection, protein information estimation, and protein structure research. However, existing prediction approaches are time-consuming, and hundreds of millions of proteins are discovered each year, necessitating the development of a rapid and reliable prediction method for protein residue-residue distances. Moreover, because many proteins lack known homologous sequences, a waiting-free and alignment-free deep learning method is needed.&lt;h4>Result&lt;/h4>In this study, we propose a learning framework named FreeProtMap. In terms of protein representation processing, the proposed group pooling in FreeProtMap effectively mitigates issues arising from high-dimensional sparseness in protein representation. In terms</description><dates><release>2024-01-01T00:00:00Z</release><publication>2024 May</publication><modification>2026-06-03T03:54:13.474Z</modification><creation>2026-04-24T03:09:21.66Z</creation></dates><accession>S-EPMC11069170</accession><cross_references><pubmed>38704533</pubmed><doi>10.1186/s12859-024-05771-0</doi></cross_references></HashMap>