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While artificial intelligence (AI) offers potential solutions, existing models often lack interpretability and fail to address uncertainty, limiting clinical utility. CLinNET, a multi\u2010modal deep neural network with a dual\u2010branch design integrating sequencing data, gene expression, biological pathways, and gene ontology (GO) is introduced to enhance gene curation and VUS interpretation. CLinNET employs a biologically informed architecture, confidence\u2010based uncertainty quantification, and layer\u2010wise SHapley Additive exPlanations (SHAP) for robust interpretability. Its sparse networks, enriched with pathway and GO data, prioritize tissue\u2010expressed genes to improve prediction accuracy and biological relevance. Trained on ND datasets, CLinNET outperformed existing methods with an F1\u2010score of 76.4%, accuracy of 77.2%, and area under the precision\u2010recall curve (AUC\u2010PR) of 84%. Incorporating uncertainty filtering further improved precision to 87% while retaining 73% of predictions as high\u2010confidence. CLinNET identified significantly more ND\u2010associated genes than random permutations, with minimal overlap with cardiovascular\u2010associated genes, confirming specificity. Among the top decile of ranked genes, 78\u00a0were\u00a0linked to NDs (\n                    <jats:italic>p<\/jats:italic>\n                    \u2010value = 1.2e\u201011), and 372 to rare diseases involving nervous system abnormalities, highlighting their diagnostic potential. CLinNET's validation in prostate cancer datasets underscores its adaptability, positioning it as a robust tool for individualized medicine.\n                  <\/jats:p>","DOI":"10.1002\/advs.202512842","type":"journal-article","created":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T19:57:03Z","timestamp":1769630223000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["CLinNET: An Interpretable and Uncertainty\u2010Aware Deep Learning Framework for Multi\u2010Modal Clinical Genomics"],"prefix":"10.1002","volume":"13","author":[{"given":"Ivan","family":"Bakhshayeshi","sequence":"first","affiliation":[{"name":"UNSW BioMedical Machine Learning Lab (BML) School of Biomedical Engineering, UNSW Sydney Sydney NSW 2052 Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammad Mahdi","family":"Hosseini","sequence":"additional","affiliation":[{"name":"Remote Internship in UNSW BioMedical Machine Learning Lab School of Biomedical Engineering, UNSW Sydney Sydney NSW 2052 Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ahmadreza","family":"Argha","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering UNSW Sydney Sydney NSW 2052 Australia"},{"name":"Tyree Institute of Health Engineering (IHealthE) UNSW Sydney Sydney NSW 2052 Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Roxana","family":"Zahedi","sequence":"additional","affiliation":[{"name":"UNSW BioMedical Machine Learning Lab (BML) School of Biomedical Engineering, UNSW Sydney Sydney NSW 2052 Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nigel H.","family":"Lovell","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering UNSW Sydney Sydney NSW 2052 Australia"},{"name":"Tyree Institute of Health Engineering (IHealthE) UNSW Sydney Sydney NSW 2052 Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hamid","family":"Alinejad\u2010Rokny","sequence":"additional","affiliation":[{"name":"UNSW BioMedical Machine Learning Lab (BML) School of Biomedical Engineering, UNSW Sydney Sydney NSW 2052 Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2026,1,28]]},"reference":[{"key":"e_1_2_8_1_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41398-022-02150-2"},{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.3390\/genes15091118"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41582-023-00896-x"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.tins.2020.05.004"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12868-021-00662-z"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41551-024-01302-7"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41576-021-00389-x"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajhg.2023.11.005"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13073-017-0433-1"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cell.2022.10.009"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1093\/hmg\/ddv253"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41431-021-00930-w"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-019-0191-0"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-74399-w"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-022-29268-7"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41551-024-01312-5"},{"key":"e_1_2_8_17_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41551-018-0324-9"},{"key":"e_1_2_8_18_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-021-03922-4"},{"key":"e_1_2_8_19_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-023-41146-4"},{"key":"e_1_2_8_20_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-50495-5"},{"key":"e_1_2_8_21_1","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkad1025"},{"key":"e_1_2_8_22_1","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkaa1113"},{"key":"e_1_2_8_23_1","author":"Magger O.","year":"2012","journal-title":"PLoS Comput. 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