{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T06:14:33Z","timestamp":1788329673020,"version":"build-2803163510"},"reference-count":15,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T00:00:00Z","timestamp":1782950400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T00:00:00Z","timestamp":1782950400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Journal of Forensic Sciences"],"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Forensic DNA phenotyping (FDP) enables the prediction of externally visible characteristics from genetic data, providing valuable investigative leads. While current approaches achieve high accuracy for blue and brown eye color, the prediction of intermediate phenotypes such as green and hazel remains challenging, particularly in Southern European and Mediterranean populations. In this study, we developed GenoEye, an interpretable machine learning framework for three\u2010category eye color prediction based on an expanded SNP panel, from which a final 37\u2010SNP predictive signature was derived. The model was developed and evaluated on a cohort of 363 Italian individuals using a gradient boosting approach, with independent data used for performance assessment. GenoEye achieved high predictive performance for blue and brown eye color (AUC up to 0.97), while improving the classification of intermediate phenotypes (AUC\u2009=\u20090.79), a category where existing tools show limited sensitivity. Comparative analyses under multiple decision strategies demonstrated that GenoEye provides consistently higher sensitivity for intermediate eye color while maintaining high specificity. To support practical application, GenoEye is implemented as a web\u2010based tool with integrated SHAP\u2010based interpretability. These results indicate that GenoEye improves the resolution of intermediate eye color prediction and provides a robust and interpretable framework for forensic applications.<\/jats:p>","DOI":"10.1111\/1556-4029.70403","type":"journal-article","created":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T05:34:31Z","timestamp":1783056871000},"page":"2145-2156","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["<scp>GenoEye<\/scp>\n                    : A machine learning\u2010based framework for the prediction of intermediate eye color phenotypes"],"prefix":"10.1111","volume":"71","author":[{"given":"Davide","family":"Dalfovo","sequence":"first","affiliation":[{"name":"Department of Cellular, Computational and Integrative Biology (CIBIO) University of Trento  Trento Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giorgia","family":"Pallafacchina","sequence":"additional","affiliation":[{"name":"Neuroscience Institute Italian National Research Council CNR  Padova Italy"},{"name":"Department of Biomedical Sciences University of Padova  Padova Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gianluca","family":"Occhi","sequence":"additional","affiliation":[{"name":"Department of Biology University of Padova  Padova Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alberto","family":"Marino","sequence":"additional","affiliation":[{"name":"Reparto Carabinieri Investigazioni Scientifiche di Parma, Sezione Biologia  Parma Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giovanni","family":"Vazza","sequence":"additional","affiliation":[{"name":"Department of Biology University of Padova  Padova Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christian","family":"Faccinetto","sequence":"additional","affiliation":[{"name":"Reparto Carabinieri Investigazioni Scientifiche di Parma, Sezione Biologia  Parma Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alessandro","family":"Romanel","sequence":"additional","affiliation":[{"name":"Department of Cellular, Computational and Integrative Biology (CIBIO) University of Trento  Trento Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,7,2]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1093\/fsr\/owae013"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.3390\/genes14081604"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.fsigen.2010.02.004"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.fsigen.2018.04.004"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.fsigen.2012.05.009"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.fsigen.2013.03.005"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1126\/sciadv.abd1239"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00414\u2010021\u201002645\u20105"},{"key":"e_1_2_9_10_1","unstructured":"KeG MengQ FinleyT WangT ChenW MaW et\u00a0al.LightGBM: a highly efficient gradient boosting decision tree.2017In: Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS'17); 2017 Dec 4\u20139; Long Beach CA. Red Hook NY: Curran Associates Inc. p. 3149\u20133157."},{"key":"e_1_2_9_11_1","volume-title":"Advances in Neural Information Processing Systems","author":"Lundberg SM","year":"2017"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.fsigen.2019.03.008"},{"key":"e_1_2_9_13_1","unstructured":"ChangW ChengJ AllaireJJ SievertC SchloerkeB Aden\u2010BuieG et\u00a0al.Shiny: Web application framework for R.2025https:\/\/github.com\/rstudio\/shiny"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1101\/gr.128652.111"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2025.110404"},{"key":"e_1_2_9_16_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41433\u2010021\u201001749\u2010x"}],"container-title":["Journal of Forensic Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/1556-4029.70403","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1111\/1556-4029.70403","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/1556-4029.70403","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T05:39:10Z","timestamp":1788327550000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1111\/1556-4029.70403"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,2]]},"references-count":15,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2026,9]]}},"alternative-id":["10.1111\/1556-4029.70403"],"URL":"https:\/\/doi.org\/10.1111\/1556-4029.70403","archive":["Portico"],"relation":{},"ISSN":["0022-1198","1556-4029"],"issn-type":[{"value":"0022-1198","type":"print"},{"value":"1556-4029","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,2]]},"assertion":[{"value":"2026-02-03","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-06-17","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-07-02","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}