{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T23:35:44Z","timestamp":1761176144871,"version":"build-2065373602"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686318","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"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":[[2025,10,21]]},"abstract":"<jats:p>Deep learning-based AI models have been extensively applied in genomics, achieving remarkable success across diverse applications. As these models gain prominence, there exists an urgent need for interpretability methods to establish trustworthiness in model-driven decisions. For genetic researchers, interpretable insights derived from these models hold significant value in providing novel perspectives for understanding biological processes. Current interpretability analyses in genomics predominantly rely on intuition and experience rather than rigorous theoretical foundations. In this review, we categorize interpretability methods into input-based and model-based approaches, while critically evaluating their limitations through concrete biological application scenarios. Furthermore, we establish theoretical underpinnings to elucidate the origins of these constraints through formal mathematical demonstrations, aiming to assist genetic researchers in better understanding and designing models in the future. Finally, we provide feasible suggestions for future research on interpretability in the field of genetics.<\/jats:p>","DOI":"10.3233\/faia250903","type":"book-chapter","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:45:33Z","timestamp":1761126333000},"source":"Crossref","is-referenced-by-count":0,"title":["Deep Learning and Explainable AI: New Pathways to Genetic Insights"],"prefix":"10.3233","author":[{"given":"Chenyu","family":"Wang","sequence":"first","affiliation":[{"name":"Engineering Research Center of Intelligent Technology for Agriculture, Ministry of Education, Wuhan, 430070, Hubei, China"},{"name":"National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, 430070, Hubei, China"}]},{"given":"Chaoying","family":"Zuo","sequence":"additional","affiliation":[{"name":"Engineering Research Center of Intelligent Technology for Agriculture, Ministry of Education, Wuhan, 430070, Hubei, China"},{"name":"National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, 430070, Hubei, China"}]},{"given":"Zihan","family":"Su","sequence":"additional","affiliation":[{"name":"Engineering Research Center of Intelligent Technology for Agriculture, Ministry of Education, Wuhan, 430070, Hubei, China"},{"name":"National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, 430070, Hubei, China"}]},{"given":"Yuhang","family":"Xing","sequence":"additional","affiliation":[{"name":"Agricultural College, Shihezi University, 832002, Xinjiang, China"}]},{"given":"Lu","family":"Li","sequence":"additional","affiliation":[{"name":"Engineering Research Center of Intelligent Technology for Agriculture, Ministry of Education, Wuhan, 430070, Hubei, China"},{"name":"National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, 430070, Hubei, China"}]},{"given":"Maojun","family":"Wang","sequence":"additional","affiliation":[{"name":"Engineering Research Center of Intelligent Technology for Agriculture, Ministry of Education, Wuhan, 430070, Hubei, China"}]},{"given":"Zeyu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Engineering Research Center of Intelligent Technology for Agriculture, Ministry of Education, Wuhan, 430070, Hubei, China"},{"name":"National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, 430070, Hubei, China"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2025"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA250903","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:45:34Z","timestamp":1761126334000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA250903"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,21]]},"ISBN":["9781643686318"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia250903","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,21]]}}}