{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T22:39:02Z","timestamp":1779403142220,"version":"3.53.1"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686615","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T00:00:00Z","timestamp":1779321600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,5,21]]},"abstract":"<jats:p>The ABL1 gene encodes a non-receptor tyrosine kinase implicated in leukemia and other genetic disorders. This study presents a deep learning\u2013based approach for predicting the pathogenicity of ABL1 single amino acid variants (SAVs) using amino acid sequence representations. Variant data collected from UniProt, ClinVar, dbSNP, and EVS databases were encoded through embedding and transformer-based methods. Multiple architectures\u2014Feedforward, Conv1D, BiLSTM, Transformer, and Prot-BERT\u2014were evaluated under six data partitioning scenarios. The Convolutional Neural Network achieved the highest performance (AUC = 0.86; Accuracy = 93%; Specificity = 0.93) in distinguishing benign from pathogenic variants. The findings demonstrate the potential of sequence-centered deep learning frameworks for accurate variant effect prediction and support the integration of AI-assisted tools in computational genomics.<\/jats:p>","DOI":"10.3233\/shti260210","type":"book-chapter","created":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T21:58:16Z","timestamp":1779400696000},"source":"Crossref","is-referenced-by-count":0,"title":["Deep Learning-Based Prediction of Pathogenicity for ABL1 Protein Variants Using Sequence Representation"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0321-7259","authenticated-orcid":false,"given":"G\u00fclbahar Merve","family":"\u015eilbir","sequence":"first","affiliation":[{"name":"Department of Electricity and Automation\/Robotics and Artificial Intelligence, \u00c7ar\u015f\u0131ba\u015f\u0131 Vocational School, Trabzon University, Trabzon, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5781-2382","authenticated-orcid":false,"given":"Bur\u00e7in","family":"Kurt","sequence":"additional","affiliation":[{"name":"Department of Biostatistics and Medical Informatics, Karadeniz Technical University, Trabzon, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","Opening the Personal Gate between Technology and Health Care"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI260210","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T21:58:16Z","timestamp":1779400696000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI260210"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,21]]},"ISBN":["9781643686615"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti260210","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,21]]}}}