{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"medRxiv"}],"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T01:44:20Z","timestamp":1783475060000,"version":"3.55.0"},"posted":{"date-parts":[[2026,3,2]]},"group-title":"Health Informatics","reference-count":46,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T00:00:00Z","timestamp":1772409600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2026,5,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:sec>\n                  <jats:title>Objective<\/jats:title>\n                  <jats:p>Systematic clinical phenotyping using Human Phenotype Ontology (HPO) is central to rare disease diagnosis. However, current disease prioritization (ranking candidate diseases from HPO for a patient) methods face key challenges: they often fail to account for the hierarchical structure of HPO terms, ignore dependencies among correlated terms, and do not adjust for batch effects arising from systematic differences in phenotype documentation across cohorts, institutions, or clinicians. We aim to develop a scalable and statistically principled framework to address these limitations for rare disease prediction and patient stratification.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>We developed PhenoSS, a Gaussian copula-based framework that models disease-specific marginal prevalence of HPO terms while capturing their joint dependencies through a multivariate normal distribution. Phenotype frequencies were estimated using external curated resources, including OARD (Open Annotations for Rare Diseases) and HPO annotations. PhenoSS supports both pair-wise phenotype similarity calculation for patient clustering and posterior odds estimation for patient-specific disease prioritization. A batch-effect correction module mitigates systematic phenotyping differences across datasets.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Across diverse simulation scenarios, PhenoSS demonstrated robust disease-prediction performance and consistently improved accuracy after batch-effect correction. In real electronic health record (EHR) data, PhenoSS identified clinically meaningful patient clusters and effectively distinguished patients with different rare diseases. In disease prioritization tasks, PhenoSS achieved competitive performance with existing methods, particularly for patients exhibiting sparse or noisy phenotype annotations.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Conclusion<\/jats:title>\n                  <jats:p>PhenoSS provides a statistically interpretable framework for modeling phenotypic heterogeneity in rare disease research and is adaptable to other structured clinical vocabularies such as SNOMED-CT and ICD codes.<\/jats:p>\n                <\/jats:sec>","DOI":"10.64898\/2026.02.26.26347219","type":"posted-content","created":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T15:00:21Z","timestamp":1772463621000},"source":"Crossref","is-referenced-by-count":1,"title":["PhenoSS: Phenotype semantic similarity-based approach for rare disease prediction and patient clustering"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-9106-7474","authenticated-orcid":false,"given":"Shihan","family":"Chen","sequence":"first","affiliation":[{"name":"Graduate Group of Applied Mathematics and Computational Science, University of Pennsylvania, PA 19104, USA"},{"name":"Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children\u2019s Hospital of Philadelphia, Philadelphia, PA 19104, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-4181-7943","authenticated-orcid":false,"given":"Quan M.","family":"Nguyen","sequence":"additional","affiliation":[{"name":"Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children\u2019s Hospital of Philadelphia, Philadelphia, PA 19104, USA"},{"name":"Bioengineering Graduate Group, University of Pennsylvania, PA 19104, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4163-7166","authenticated-orcid":false,"given":"Yu","family":"Hu","sequence":"additional","affiliation":[{"name":"Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children\u2019s Hospital of Philadelphia, Philadelphia, PA 19104, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6024-3037","authenticated-orcid":false,"given":"Cong","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Pediatrics, Boston Children\u2019s Hospital and Harvard Medical School, Boston, MA 02115, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9624-0214","authenticated-orcid":false,"given":"Chunhua","family":"Weng","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, Columbia University Irving Medicine Center, New York, NY 10032, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5585-982X","authenticated-orcid":false,"given":"Kai","family":"Wang","sequence":"additional","affiliation":[{"name":"Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children\u2019s Hospital of Philadelphia, Philadelphia, PA 19104, USA"},{"name":"Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"54368","reference":[{"key":"2026050510100452000_2026.02.26.26347219v2.1","doi-asserted-by":"publisher","DOI":"10.1056\/nejmoa2314761"},{"key":"2026050510100452000_2026.02.26.26347219v2.2","doi-asserted-by":"publisher","DOI":"10.1016\/j.gim.2023.100948"},{"key":"2026050510100452000_2026.02.26.26347219v2.3","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkad1005"},{"key":"2026050510100452000_2026.02.26.26347219v2.4","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-genom-083118-015345"},{"key":"2026050510100452000_2026.02.26.26347219v2.5","doi-asserted-by":"publisher","DOI":"10.1016\/j.tig.2022.07.002"},{"key":"2026050510100452000_2026.02.26.26347219v2.6","doi-asserted-by":"publisher","DOI":"10.1186\/s12864-017-3910-4"},{"key":"2026050510100452000_2026.02.26.26347219v2.7","doi-asserted-by":"publisher","DOI":"10.1038\/gim.2017.119"},{"key":"2026050510100452000_2026.02.26.26347219v2.8","doi-asserted-by":"publisher","DOI":"10.1186\/s13073-022-01026-w"},{"key":"2026050510100452000_2026.02.26.26347219v2.9","doi-asserted-by":"publisher","DOI":"10.1093\/nargab\/lqab078"},{"key":"2026050510100452000_2026.02.26.26347219v2.10","doi-asserted-by":"publisher","DOI":"10.1186\/s13073-015-0199-2"},{"key":"2026050510100452000_2026.02.26.26347219v2.11","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajhg.2018.05.010"},{"key":"2026050510100452000_2026.02.26.26347219v2.12","doi-asserted-by":"publisher","DOI":"10.1186\/s12864-016-2722-2"},{"key":"2026050510100452000_2026.02.26.26347219v2.13","doi-asserted-by":"publisher","DOI":"10.1038\/nmeth.3484"},{"key":"2026050510100452000_2026.02.26.26347219v2.14","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajhg.2009.09.003"},{"key":"2026050510100452000_2026.02.26.26347219v2.15","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajhg.2014.03.010"},{"key":"2026050510100452000_2026.02.26.26347219v2.16","doi-asserted-by":"publisher","DOI":"10.1038\/nprot.2015.124"},{"key":"2026050510100452000_2026.02.26.26347219v2.17","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-019-10649-4"},{"key":"2026050510100452000_2026.02.26.26347219v2.18","doi-asserted-by":"publisher","DOI":"10.1038\/s41436-018-0381-1"},{"key":"2026050510100452000_2026.02.26.26347219v2.19","doi-asserted-by":"publisher","DOI":"10.1093\/nargab\/lqaa032"},{"key":"2026050510100452000_2026.02.26.26347219v2.20","doi-asserted-by":"publisher","DOI":"10.1136\/jamia.2009.001560"},{"key":"2026050510100452000_2026.02.26.26347219v2.21","doi-asserted-by":"publisher","DOI":"10.1002\/humu.22347"},{"key":"2026050510100452000_2026.02.26.26347219v2.22","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkz386"},{"key":"2026050510100452000_2026.02.26.26347219v2.23","doi-asserted-by":"publisher","DOI":"10.1111\/cge.13471"},{"key":"2026050510100452000_2026.02.26.26347219v2.24","doi-asserted-by":"publisher","DOI":"10.1186\/s12911-022-01927-1"},{"key":"2026050510100452000_2026.02.26.26347219v2.25","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btab019"},{"key":"2026050510100452000_2026.02.26.26347219v2.26","doi-asserted-by":"publisher","DOI":"10.1109\/TCBB.2022.3170301"},{"key":"2026050510100452000_2026.02.26.26347219v2.27","doi-asserted-by":"publisher","DOI":"10.1016\/j.patter.2023.100887"},{"issue":"1","key":"2026050510100452000_2026.02.26.26347219v2.28","first-page":"25","article-title":"Gene ontology: tool for the unification of biology","volume":"25","year":"2000","journal-title":"The Gene Ontology Consortium. Nat. Genet"},{"issue":"9","key":"2026050510100452000_2026.02.26.26347219v2.29","first-page":"518","article-title":"[Orphanet: a European database for rare diseases]","volume":"152","year":"2008","journal-title":"Ned Tijdschr Geneeskd"},{"key":"2026050510100452000_2026.02.26.26347219v2.30","unstructured":"Principal Component Analysis for Special Types of Data. Principal Component Analysis. New York, NY: Springer New York, 2002:338\u201372."},{"key":"2026050510100452000_2026.02.26.26347219v2.31","doi-asserted-by":"publisher","DOI":"10.1198\/jasa.2010.tm08487"},{"key":"2026050510100452000_2026.02.26.26347219v2.32","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajhg.2022.08.002"},{"key":"2026050510100452000_2026.02.26.26347219v2.33","doi-asserted-by":"crossref","unstructured":"Francisco MC , Andre L. Semantic Similarity Definition. In: Shoba R , Michael G , Kenta N , Christian S , eds. Encyclopedia of Bioinformatics and Computational Biology. Oxford: Academic Press, 2019:870\u201376.","DOI":"10.1016\/B978-0-12-809633-8.20401-9"},{"key":"2026050510100452000_2026.02.26.26347219v2.34","doi-asserted-by":"publisher","DOI":"10.18637\/jss.v077.i08"},{"key":"2026050510100452000_2026.02.26.26347219v2.35","doi-asserted-by":"publisher","DOI":"10.1093\/genetics\/iyaf215"},{"key":"2026050510100452000_2026.02.26.26347219v2.36","unstructured":"Resnik P. Using information content to evaluate semantic similarity in a taxonomy. Proceedings of the 14th international joint conference on Artificial intelligence \u2013 Volume 1. Montreal, Quebec, Canada: Morgan Kaufmann Publishers Inc., 1995:448\u201353."},{"key":"2026050510100452000_2026.02.26.26347219v2.37","unstructured":"Lin D. An Information-Theoretic Definition of Similarity. Proceedings of the Fifteenth International Conference on Machine Learning: Morgan Kaufmann Publishers Inc., 1998:296\u2013304."},{"key":"2026050510100452000_2026.02.26.26347219v2.38","unstructured":"Jiang JJ , Conrath DW . Semantic Similarity Based on Corpus Statistics and Lexical Taxonomy. Proceedings of the 10th Research on Computational Linguistics International Conference. Taipei, Taiwan: The Association for Computational Linguistics and Chinese Language Processing (ACLCLP), 1997:19\u201333."},{"key":"2026050510100452000_2026.02.26.26347219v2.39","article-title":"Effectively integrating information content and structural relationship to improve the GO-based similarity measure between proteins","year":"2010","journal-title":"ArXiv"},{"key":"2026050510100452000_2026.02.26.26347219v2.40","doi-asserted-by":"crossref","unstructured":"Trevor FC . Ch. 16. Multidimensional scaling in process control. Statistics in Industry: Elsevier, 2003:609\u201323.","DOI":"10.1016\/S0169-7161(03)22018-6"},{"key":"2026050510100452000_2026.02.26.26347219v2.41","unstructured":"Van der Maaten L , Hinton G. Visualizing data using t-SNE. Journal of machine learning research 2008;9(11)."},{"key":"2026050510100452000_2026.02.26.26347219v2.42","article-title":"Umap: Uniform manifold approximation and projection for dimension reduction","journal-title":"arXiv preprint"},{"key":"2026050510100452000_2026.02.26.26347219v2.43","unstructured":"Anderson MJ . Permutational multivariate analysis of variance (PERMANOVA). Wiley statsref: statistics reference online 2014:1\u201315."},{"key":"2026050510100452000_2026.02.26.26347219v2.44","doi-asserted-by":"publisher","DOI":"10.1038\/s41431-023-01368-y"},{"key":"2026050510100452000_2026.02.26.26347219v2.45","doi-asserted-by":"publisher","DOI":"10.1016\/j.xhgg.2024.100371"},{"key":"2026050510100452000_2026.02.26.26347219v2.46","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajhg.2015.11.005"}],"container-title":[],"original-title":[],"link":[{"URL":"https:\/\/syndication.highwire.org\/content\/doi\/10.64898\/2026.02.26.26347219","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T17:10:24Z","timestamp":1778001024000},"score":1,"resource":{"primary":{"URL":"http:\/\/medrxiv.org\/lookup\/doi\/10.64898\/2026.02.26.26347219"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,2]]},"references-count":46,"URL":"https:\/\/doi.org\/10.64898\/2026.02.26.26347219","relation":{},"subject":[],"published":{"date-parts":[[2026,3,2]]},"subtype":"preprint"}}