{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,11]],"date-time":"2026-11-11T16:29:31Z","timestamp":1794414571548,"version":"4.9.1"},"reference-count":16,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Bioinform Biotech"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec sec-type=\"background\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Xeroderma pigmentosum group D (XPD), caused by ERCC2 gene dysfunction, leads to defective nucleotide excision repair and hypersensitivity to UV radiation. Robust experimental models for variant-level functional assessment remain limited.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"objective\">\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study aimed to use aiAtlas version 1.2, a mechanistic large-concept simulation model, to evaluate the functional consequences of ERCC2 (XPD) variants and identify quantitative thresholds for functional rescue under graded gene function restoration.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"methods\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We simulated 136 virtual artificially induced pluripotent stem cell\u2013derived cell lines spanning wild-type, single-mutation, multi-mutation, human tumor\u2013derived, and gene fusion ERCC2 states. Twenty-five features encompassing DNA damage and repair, replication stress, pluripotency, and epigenetic remodeling were analyzed using nonparametric statistics with Bonferroni correction, Hodges-Lehmann estimates, and Cliff \u03b4 with bootstrap CIs. Graded ERCC2 restoration (0%\u2010100%) was simulated to evaluate rescue thresholds. Cross-validation, bagging, and bootstrap resampling tested robustness.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"results\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Simulations identified 3 nonlinear ERCC2 (XPD) rescue thresholds: approximately 20% to 30% (initial stabilization of repair), approximately 50% (near normalization), and more than 80% (full convergence). Reduced-function variants required substantially less restoration than strict loss-of-function variants to cross each threshold. Group comparisons across variant classes showed consistent differences in DNA repair, replication stress, and epigenetic remodeling features supported by effect size metrics and CIs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"conclusions\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>aiAtlas version 1.2 provides a simulation-based mechanistic framework for variant-level functional modeling in ERCC2 (XPD). The simulations identified quantitative thresholds that support mechanistic hypothesis generation regarding variant-dependent restoration of function. These findings provide a computational basis for future studies and require independent validation before clinical application.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.2196\/88672","type":"journal-article","created":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T21:01:27Z","timestamp":1790110887000},"page":"e88672-e88672","source":"Crossref","is-referenced-by-count":0,"title":["Application of aiAtlas Version 1.2 to Simulate Variant Gene Function Restoration and Rescue Thresholds in Rare Diseases: Validation Case Study"],"prefix":"10.2196","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9297-4405","authenticated-orcid":false,"given":"Wayne","family":"Danter","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2026,9,22]]},"reference":[{"issue":"1","key":"R1","doi-asserted-by":"publisher","DOI":"10.1038\/s41392-024-01809-0","article-title":"Induced pluripotent stem cells (iPSCs): molecular mechanisms of induction and applications","volume":"9","author":"Cerneckis","journal-title":"Signal Transduct Target Ther"},{"issue":"11","key":"R2","doi-asserted-by":"publisher","DOI":"10.18103\/mra.v11i11.4784","article-title":"The challenges to advancing induced pluripotent stem cell-dependent cell replacement therapy","volume":"11","author":"Moy","journal-title":"Med Res Arch"},{"key":"R3","doi-asserted-by":"publisher","DOI":"10.1016\/j.mcp.2018.01.001","article-title":"Modeling rare diseases with induced pluripotent stem cell technology","volume":"40","author":"Anderson","journal-title":"Mol Cell Probes"},{"key":"R4","doi-asserted-by":"publisher","DOI":"10.3389\/fimmu.2018.02243","article-title":"Modeling hematological diseases and cancer with patient-specific induced pluripotent stem cells","volume":"9","author":"Kim","journal-title":"Front Immunol"},{"key":"R5","doi-asserted-by":"publisher","DOI":"10.1038\/ncomms5846","article-title":"The landscape of kinase fusions in cancer","volume":"5","author":"Stransky","journal-title":"Nat Commun"},{"key":"R6","unstructured":"Barrault L Duquenne PA Elbayad M Kozhevnikov A Alastruey B Andrews P et al. Large concept models: language modeling in a sentence representation space. arXiv. Preprint posted online on Dec 11, 2024. [doi: 10.48550\/arXiv.2412.08821]"},{"issue":"6","key":"R7","doi-asserted-by":"publisher","first-page":"636","DOI":"10.1016\/j.cels.2021.05.015","article-title":"Advances in systems biology modeling: 10 years of crowdsourcing DREAM challenges","volume":"12","author":"Meyer","journal-title":"Cell Syst"},{"key":"R8","doi-asserted-by":"crossref","unstructured":"Danter WR . aiCRISPRL: an artificial intelligence platform for stem cell and organoid simulation with extensive gene editing capabilities. bioRxiv. Preprint posted online on Jun 13, 2024. [doi: 10.1101\/2022.06.18.496679]","DOI":"10.1101\/2022.06.18.496679"},{"issue":"1","key":"R9","doi-asserted-by":"publisher","first-page":"13","DOI":"10.20982\/tqmp.04.1.p013","article-title":"The Mann-Whitney U: a test for assessing whether two independent samples come from the same distribution","volume":"4","author":"Nachar","journal-title":"Tutor Quant Methods Psychol"},{"issue":"6973","key":"R10","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1136\/bmj.310.6973.170","article-title":"Multiple significance tests: the Bonferroni method","volume":"310","author":"Bland","journal-title":"BMJ"},{"key":"R11","doi-asserted-by":"crossref","unstructured":"Hollander M Wolfe DA Chicken E . Nonparametric Statistical Methods. 3rd ed. John Wiley & Sons; 2014. [doi: 10.1002\/9781119196037]","DOI":"10.1002\/9781119196037"},{"issue":"3","key":"R12","doi-asserted-by":"publisher","first-page":"494","DOI":"10.1037\/0033-2909.114.3.494","article-title":"Dominance statistics: ordinal analyses to answer ordinal questions","volume":"114","author":"Cliff","journal-title":"Psychol Bull"},{"issue":"2","key":"R13","first-page":"263","volume":"58","author":"Takayama","journal-title":"Am J Hum Genet"},{"issue":"22","key":"R14","doi-asserted-by":"publisher","first-page":"2539","DOI":"10.1093\/hmg\/10.22.2539","article-title":"Two individuals with features of both xeroderma pigmentosum and trichothiodystrophy highlight the complexity of the clinical outcomes of mutations in the XPD gene","volume":"10","author":"Broughton","journal-title":"Hum Mol Genet"},{"issue":"4","key":"R15","doi-asserted-by":"publisher","first-page":"1036","DOI":"10.1086\/302063","article-title":"Analysis of mutations in the XPD gene in Italian patients with trichothiodystrophy: site of mutation correlates with repair deficiency, but gene dosage appears to determine clinical severity","volume":"63","author":"Botta","journal-title":"Am J Hum Genet"},{"key":"R16","unstructured":"Kraemer KH DiGiovanna JJ Tamura D . Xeroderma pigmentosum. In: Adam MP Bick S Mirzaa GM Pagon RA Wallace SE Amemiya A , editors. GeneReviews\u00ae. University of Washington; 2003:1993-2026. [Medline: 20301571]"}],"container-title":["JMIR Bioinformatics and Biotechnology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/bioinform.jmir.org\/2026\/1\/e88672","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,11,11]],"date-time":"2026-11-11T16:19:38Z","timestamp":1794413978000},"score":1,"resource":{"primary":{"URL":"https:\/\/bioinform.jmir.org\/2026\/1\/e88672"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,22]]},"references-count":16,"URL":"https:\/\/doi.org\/10.2196\/88672","relation":{},"ISSN":["2563-3570"],"issn-type":[{"value":"2563-3570","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,22]]},"article-number":"v7i1e88672"}}