{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["23(1)"],"submitter":["Zhang Z"],"pubmed_abstract":["<h4>Objectives</h4>Immune microenvironment was closely related to the occurrence and progression of colorectal cancer (CRC). The objective of the current research was to develop and verify a Machine learning survival predictive system for CRC based on immune gene expression data and machine learning algorithms.<h4>Methods</h4>The current study performed differentially expressed analyses between normal tissues and tumor tissues. Univariate Cox regression was used to screen prognostic markers for CRC. Prognostic immune genes and transcription factors were used to construct an immune-related regulatory network. Three machine learning algorithms were used to create an Machine learning survival predictive system for CRC. Concordance indexes, calibration curves, and Brier scores were used to eva"],"journal":["BMC bioinformatics"],"pagination":["124"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8991575"],"repository":["biostudies-literature"],"pubmed_title":["Bioinformatics analysis reveals immune prognostic markers for overall survival of colorectal cancer patients: a novel machine learning survival predictive system."],"pmcid":["PMC8991575"],"pubmed_authors":["Li J","Wang P","Zhang Z","Huang L"],"additional_accession":[]},"is_claimable":false,"name":"Bioinformatics analysis reveals immune prognostic markers for overall survival of colorectal cancer patients: a novel machine learning survival predictive system.","description":"<h4>Objectives</h4>Immune microenvironment was closely related to the occurrence and progression of colorectal cancer (CRC). The objective of the current research was to develop and verify a Machine learning survival predictive system for CRC based on immune gene expression data and machine learning algorithms.<h4>Methods</h4>The current study performed differentially expressed analyses between normal tissues and tumor tissues. Univariate Cox regression was used to screen prognostic markers for CRC. Prognostic immune genes and transcription factors were used to construct an immune-related regulatory network. Three machine learning algorithms were used to create an Machine learning survival predictive system for CRC. Concordance indexes, calibration curves, and Brier scores were used to eva","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Apr","modification":"2025-04-26T15:14:52.03Z","creation":"2025-04-06T14:51:02.883Z"},"accession":"S-EPMC8991575","cross_references":{"pubmed":["35395711"],"doi":["10.1186/s12859-022-04657-3"]}}