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. 2021 Sep 22;13:71. doi: 10.1186/s13321-021-00552-w

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© The Author(s) 2021

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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Fig. 2 — DTi2Vec Method Flowchart, (1) Filter the TTsim and DDsim graphs, (2) Construct a full DTI network by augmenting the three graphs, (3) Apply the three-step node2vec framework on the full DTI network, (4) Generate edge representation for each drug-target pair, (5) Feed the feature vector (FV) into ensemble boosting classifier to output the class labels