<HashMap><database>biostudies-literature</database><scores/><additional><omics_type>Unknown</omics_type><volume>16(1)</volume><submitter>Li A</submitter><funding>Key Clinical Projects of Affiliated Hospital of Guangdong Medical University</funding><funding>Supported Projects of Zhanjiang</funding><journal>Scientific reports</journal><pagination>7006</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC12920713</full_dataset_link><repository>biostudies-literature</repository><pubmed_title>Automated quantification of tumor-infiltrating lymphocytes by machine learning reveals prognostic and immunogenomic features in lung cancer.</pubmed_title><pmcid>PMC12920713</pmcid><funding_grant_id>LCYJ2022DL003</funding_grant_id><funding_grant_id>2021A05076</funding_grant_id><pubmed_authors>He Z</pubmed_authors><pubmed_authors>Chen J</pubmed_authors><pubmed_authors>Zhang H</pubmed_authors><pubmed_authors>Liang Z</pubmed_authors><pubmed_authors>Li F</pubmed_authors><pubmed_authors>Li A</pubmed_authors><pubmed_authors>Wu D</pubmed_authors><pubmed_authors>Pang Y</pubmed_authors><pubmed_authors>Lin L</pubmed_authors></additional><is_claimable>false</is_claimable><name>Automated quantification of tumor-infiltrating lymphocytes by machine learning reveals prognostic and immunogenomic features in lung cancer.</name><description/><dates><release>2026-01-01T00:00:00Z</release><publication>2026 Feb</publication><modification>2026-07-16T12:22:51.027Z</modification><creation>2026-07-09T10:55:24.12Z</creation></dates><accession>S-EPMC12920713</accession><cross_references><pubmed>41629429</pubmed><doi>10.1038/s41598-026-37076-y</doi></cross_references></HashMap>