{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T12:59:03Z","timestamp":1770469143013,"version":"3.49.0"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1012400","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2024,9,12]],"date-time":"2024-09-12T00:00:00Z","timestamp":1726099200000}}],"reference-count":47,"publisher":"Public Library of Science (PLoS)","issue":"8","license":[{"start":{"date-parts":[[2024,8,30]],"date-time":"2024-08-30T00:00:00Z","timestamp":1724976000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62302339"],"award-info":[{"award-number":["62302339"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62172158"],"award-info":[{"award-number":["62172158"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>The identification of cancer driver genes (CDGs) poses challenges due to the intricate interdependencies among genes and the influence of measurement errors and noise. We propose a novel energy-constrained diffusion (ECD)-based model for identifying CDGs, termed ECD-CDGI. This model is the first to design an ECD-Attention encoder by combining the ECD technique with an attention mechanism. ECD-Attention encoder excels at generating robust gene representations that reveal the complex interdependencies among genes while reducing the impact of data noise. We concatenate topological embedding extracted from gene-gene networks through graph transformers to these gene representations. We conduct extensive experiments across three testing scenarios. Extensive experiments show that the ECD-CDGI model possesses the ability to not only be proficient in identifying known CDGs but also efficiently uncover unknown potential CDGs. Furthermore, compared to the GNN-based approach, the ECD-CDGI model exhibits fewer constraints by existing gene-gene networks, thereby enhancing its capability to identify CDGs. Additionally, ECD-CDGI is open-source and freely available. We have also launched the model as a complimentary online tool specifically crafted to expedite research efforts focused on CDGs identification.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1012400","type":"journal-article","created":{"date-parts":[[2024,8,30]],"date-time":"2024-08-30T19:36:37Z","timestamp":1725046597000},"page":"e1012400","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":12,"title":["ECD-CDGI: An efficient energy-constrained diffusion model for cancer driver gene identification"],"prefix":"10.1371","volume":"20","author":[{"given":"Tao","family":"Wang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6586-0533","authenticated-orcid":true,"given":"Linlin","family":"Zhuo","sequence":"additional","affiliation":[]},{"given":"Yifan","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Xiangzheng","family":"Fu","sequence":"additional","affiliation":[]},{"given":"Xiangxiang","family":"Zeng","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6406-1142","authenticated-orcid":true,"given":"Quan","family":"Zou","sequence":"additional","affiliation":[]}],"member":"340","published-online":{"date-parts":[[2024,8,30]]},"reference":[{"issue":"6127","key":"pcbi.1012400.ref001","doi-asserted-by":"crossref","first-page":"1546","DOI":"10.1126\/science.1235122","article-title":"Cancer genome landscapes","volume":"339","author":"B. 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