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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Gao, Junning
dc.contributor.author Yao, Shuwei
dc.contributor.author Mamitsuka, Hiroshi
dc.contributor.author Zhu, Shanfeng
dc.date.accessioned 2019-02-25T08:41:28Z
dc.date.available 2019-02-25T08:41:28Z
dc.date.issued 2018
dc.identifier.citation Gao , J , Yao , S , Mamitsuka , H & Zhu , S 2018 , AiProAnnotator : Low-rank Approximation with network side information for high-performance, large-scale human Protein abnormality Annotator . in Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 . , 8621517 , IEEE , pp. 13-20 , IEEE International Conference on Bioinformatics and Biomedicine , Madrid , Spain , 03/12/2018 . https://doi.org/10.1109/BIBM.2018.8621517 en
dc.identifier.isbn 9781538654897
dc.identifier.other PURE UUID: 1a7353ba-f61d-4824-a8e0-57e1b3767ad5
dc.identifier.other PURE ITEMURL: https://research.aalto.fi/en/publications/1a7353ba-f61d-4824-a8e0-57e1b3767ad5
dc.identifier.other PURE LINK: https://ieeexplore.ieee.org/document/8621517
dc.identifier.other PURE FILEURL: https://research.aalto.fi/files/31653724/BIBM_AiPA_final_B415.pdf
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/36660
dc.description Best student paper of the conference
dc.description.abstract Annotating genes/proteins is a vital issue in biology. Particularly we focus on human proteins and medical annotation, which both are important. The most proper data for our annotation is human phenotype ontology (HPO), which are sparse but reliable (well-curated). Existing approaches for this problem are feature-based or network-based. The feature-based approach can incorporate a variety of information, by which this approach is more appropriate for noisy data than reliable data, while the network-based approach is not necessarily useful for sparse data. Low-rank approximation is very powerful for both sparse and reliable data. We thus propose to use matrix factorization to approximate the input annotation matrix (proteins × HPO terms) by factorized low-rank matrices. We further incorporate network information, i.e. protein-protein network (PPN) and network from HPO (NHPO), into the framework of matrix factorization as graph regularization over the two low-rank matrices. That is, the input annotation matrix is factorized into two low-rank factor matrices so that they can be smooth over PPN and NHPO. We call our software of implementing the above method “AiProAnnotator”, which in this paper has been empirically examined using the latest HPO data extensively under various experimental settings, including performance comparison under cross-validation, computation time and case studies, etc. Experimental results showed the high predictive performance and time efficiency of AiProAnnotator clearly. en
dc.format.extent 8
dc.format.extent 13-20
dc.format.mimetype application/pdf
dc.language.iso en en
dc.relation.ispartof IEEE International Conference on Bioinformatics and Biomedicine en
dc.relation.ispartofseries Proceedings of the 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) en
dc.rights openAccess en
dc.title AiProAnnotator en
dc.type A4 Artikkeli konferenssijulkaisussa fi
dc.description.version Peer reviewed en
dc.contributor.department Fudan University
dc.contributor.department Professorship Kaski Samuel
dc.contributor.department Department of Computer Science en
dc.identifier.urn URN:NBN:fi:aalto-201902251817
dc.identifier.doi 10.1109/BIBM.2018.8621517
dc.type.version acceptedVersion

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