Gradient Boosting Machine Learning Model for Defective Endometrial Receptivity Prediction by Macrophage-Endometrium Interaction Modules.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35603216", "kind": "science-record", "revision": 5, "content_hash": "sha256:1d3ff9898335ba1e6a959da228fb931db60ddfc12fff820a34efd869decb08ca", "created_at": "2026-11-01T18:14:31Z", "updated_at": "2026-11-01T22:49:07Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Gradient Boosting Machine Learning Model for Defective Endometrial Receptivity Prediction by Macrophage-Endometrium Interaction Modules.", "doi": "10.3389/fimmu.2022.842607", "pmid": "35603216", "authors": ["Bohan Li", "Hua Duan", "Sha Wang", "Jiajing Wu", "Yazhu Li"], "journal": "Frontiers in immunology", "year": 2022, "abstract": "Background: A receptive endometrium is a prerequisite for successful embryo implantation. Mounting evidence shows that nearly one-third of infertility and implantation failures are caused by defective endometrial receptivity. This study pooled 218 subjects from multiple datasets to investigate the association of the immune infiltration level with reproductive outcome. Additionally, macrophage-endometrium interaction modules were constructed to explore an accurate and cost-effective approach to endometrial receptivity assessment.\nMethods: Immune-infiltration levels in 4 GEO datasets (n=218) were analyzed and validated through meta-analysis. Macrophage-endometrium interaction modules were selected based on the weighted gene co-expression network in GSE58144 and differentially expressed genes dominated by GSE19834 dataset. Xgboost, random forests, and regression algorithms were applied to predictive models. Subsequently, the efficacy of the models was compared and validated in the GSE165004 dataset. Forty clinical samples (RT-PCR and western blot) were performed for expression and model validation, and the results were compared to those of endometrial thickness in clinical pregnancy assessment.\nResults: Altered levels of Mϕs infiltration were shown to critically influence embryo implantation. The three selected modules, manifested as macrophage-endometrium interactions, were enrichment in the immunoreactivity, decidualization, and signaling functions and pathways. Moreover, hub genes within the modules exerted significant reproductive prognostic effects. The xgboost algorithm showed the best performance among the machine learning models, with AUCs of 0.998 (95% CI 0.994-1) and 0.993 (95% CI 0.979-1) in GSE58144 and GSE165004 datasets, respectively. These results were significantly superior to those of the other two models (random forest and regression). Similarly, the model was significantly superior to ultrasonography (endometrial thickness) with a better cost-benefit ratio in the population.\nConclusion: Successful embryo implantation is associated with infiltration levels of Mϕs, manifested in genetic modules involved in macrophage-endometrium interactions. Therefore, utilizing the hub genes in these modules can provide a platform for establishing excellent machine learning models to predict reproductive outcomes in patients with defective endometrial receptivity.", "claims": [{"id": "claim-method", "statement": "Machine learning algorithms (xgboost, random forest, and regression) were used to evaluate the predictive power of modules for defective endometrial receptivity.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35603216", "locator": "sections[15] \"Construction and Validation of Machine Learning Models\" paragraph 2", "quote": "Machine learning algorithms (xgboost, random forest, and regression) were used to evaluate the predictive power of modules for defective endometrial receptivity."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/ Supplementary Material . 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