{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T20:33:45Z","timestamp":1772138025368,"version":"3.50.1"},"reference-count":45,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"vor","delay-in-days":31,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["#62372165"],"award-info":[{"award-number":["#62372165"]}],"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":["#62032007"],"award-info":[{"award-number":["#62032007"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000038","name":"NSERC","doi-asserted-by":"publisher","award":["RGPIN-2019-0621"],"award-info":[{"award-number":["RGPIN-2019-0621"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,7,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Systematically investigating interactions among molecules of the same type across different contexts is crucial for unraveling disease mechanisms and developing potential therapeutic strategies. The \u201cA\u2013A\u2013B\u201d triplet paradigm provides a principled approach to model such context-specific interactions, and leveraging third-order tensor to capture such type ternary relationships is an efficient strategy. However, effectively modeling both multilinear and nonlinear characteristics to accurately identify such triplets using tensor-based methods remains a challenge. In this paper, we propose a novel Convolutional Neural Tensor Completion (ConvNTC) framework that collaboratively learns the multilinear and nonlinear representations to model triplet-based network interactions. ConvNTC consists of a multilinear module and a nonlinear module. The former is a tensor decomposition approach that integrates multiple constraints to learn the tensor factor embeddings. The latter contains three components: an embedding generator to produce position-specific index embeddings for each tensor entry in addition to the factor embeddings, a convolutional encoder to perform nonlinear feature mapping while preserving the tensor\u2019s rank-one property, and a Kolmogorov\u2013Arnold Network (KAN) based predictor to effectively capture high-dimensional relationships aligned with the intrinsic structure of real-world data. We evaluate ConvNTC on two types triplet datasets of the \u201cA\u2013A\u2013B\u201d type: miRNA\u2013miRNA\u2013disease and drug\u2013drug\u2013cell. Comprehensive experiments against 11 state-of-the-art methods demonstrate the superiority of ConvNTC in terms of triplet prediction. ConvNTC reveals promising prognostic values of the miRNA\u2013miRNA interactions on breast cancer and detects synergistic drug combinations in cancer cell lines.<\/jats:p>","DOI":"10.1093\/bib\/bbaf372","type":"journal-article","created":{"date-parts":[[2025,7,14]],"date-time":"2025-07-14T14:11:10Z","timestamp":1752502270000},"source":"Crossref","is-referenced-by-count":1,"title":["ConvNTC: convolutional neural tensor completion for detecting \u201cA\u2013A\u2013B\u201d type biological triplets"],"prefix":"10.1093","volume":"26","author":[{"given":"Pei","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Computer Science, College of Computer Science and Electronic Engineering , 116 Lu Shan South Road, Hunan University, Changsha 410082, Hunan ,","place":["China"]},{"name":"School of Computer Science, McGill University, Lorne M. Trottier Building , 3630 University Street, Montr\u00e9al, QC H3A 0C6 ,","place":["Canada"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiao","family":"Liang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Computer Science and Electronic Engineering , 116 Lu Shan South Road, Hunan University, Changsha 410082, Hunan ,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yue","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science, McGill University, Lorne M. 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