{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T16:18:51Z","timestamp":1774023531042,"version":"3.50.1"},"reference-count":36,"publisher":"Oxford University Press (OUP)","issue":"10","license":[{"start":{"date-parts":[[2024,9,23]],"date-time":"2024-09-23T00:00:00Z","timestamp":1727049600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"crossref","award":["2022YFF1202101"],"award-info":[{"award-number":["2022YFF1202101"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62225109"],"award-info":[{"award-number":["62225109"]}],"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":["62072095"],"award-info":[{"award-number":["62072095"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,10,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>The prediction of drug\u2013target interaction is a vital task in the biomedical field, aiding in the discovery of potential molecular targets of drugs and the development of targeted therapy methods with higher efficacy and fewer side effects. Although there are various methods for drug\u2013target interaction (DTI) prediction based on heterogeneous information networks, these methods face challenges in capturing the fundamental interaction between drugs and targets and ensuring the interpretability of the model. Moreover, they need to construct meta-paths artificially or a lot of feature engineering (prior knowledge), and graph generation can fuse information more flexibly without meta-path selection.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We propose a causal enhanced method for drug\u2013target interaction (CE-DTI) prediction that integrates graph generation and multi-source information fusion. First, we represent drugs and targets by modeling the fusion of their multi-source information through automatic graph generation. Once drugs and targets are combined, a network of drug\u2013target pairs is constructed, transforming the prediction of drug\u2013target interactions into a node classification problem. Specifically, the influence of surrounding nodes on the central node is separated into two groups: causal and non-causal variable nodes. Causal variable nodes significantly impact the central node\u2019s classification, while non-causal variable nodes do not. Causal invariance is then used to enhance the contrastive learning of the drug\u2013target pairs network. Our method demonstrates excellent performance compared with other competitive benchmark methods across multiple datasets. At the same time, the experimental results also show that the causal enhancement strategy can explore the potential causal effects between DTPs, and discover new potential targets. Additionally, case studies demonstrate that this method can identify potential drug targets.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The source code of AdaDR is available at: https:\/\/github.com\/catly\/CE-DTI.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btae570","type":"journal-article","created":{"date-parts":[[2024,9,23]],"date-time":"2024-09-23T19:07:52Z","timestamp":1727118472000},"source":"Crossref","is-referenced-by-count":17,"title":["Causal enhanced drug\u2013target interaction prediction based on graph generation and multi-source information fusion"],"prefix":"10.1093","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-8128-0047","authenticated-orcid":false,"given":"Guanyu","family":"Qiao","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Harbin Institute of Technology , Harbin 150001,","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7381-2374","authenticated-orcid":false,"given":"Guohua","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Harbin Institute of Technology , Harbin 150001,","place":["China"]},{"name":"College of Computer and Control Engineering, Northeast Forestry University , Harbin 150040,","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0403-7287","authenticated-orcid":false,"given":"Yang","family":"Li","sequence":"additional","affiliation":[{"name":"College of Computer and Control Engineering, Northeast Forestry University , Harbin 150040,","place":["China"]}]}],"member":"286","published-online":{"date-parts":[[2024,9,23]]},"reference":[{"key":"2024101621404951300_btae570-B1","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1186\/s13321-023-00689-w","article-title":"How to approach machine learning-based prediction of drug\/compound\u2013target 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