Deep in the bowel: highly interpretable neural encoder-decoder networks predict gut metabolites from gut microbiome Le, Vuong, Quinn, Thomas P., Tran, Truyen and Venkatesh, Svetha 2020, Deep in the bowel: highly interpretable neural encoder-decoder networks predict gut metabolites from gut microbiome, BMC Genomics, vol. 21, no. Supplement 4, Proceedings of the Joint International GIW & ABACBS-2019 Conference: genomics (part 2), doi: 10.1186/s12864-020-6652-7. Attached Files Name Description MIMEType Size Downloads Title Deep in the bowel: highly interpretable neural encoder-decoder networks predict gut metabolites from gut microbiome Author(s) Le, Vuong Quinn, Thomas P. Tran, Truyen Venkatesh, Svetha orcid.org/0000-0001-8675-6631 Journal name BMC Genomics Volume number 21 Issue number Supplement 4 Season Proceedings of the Joint International GIW & ABACBS-2019 Conference: genomics (part 2) Article ID 256 Total pages 15 Publisher BioMed Central Place of publication London, Eng. Publication date 2020-07-20 Keyword(s) Science & Technology Life Sciences & Biomedicine Biotechnology & Applied Microbiology Genetics & Heredity Metabolomics Multi-omics Machine learning Deep learning Interpretability Notes ISSN : 1471-2164 Language eng DOI 10.1186/s12864-020-6652-7 Indigenous content off Field of Research 06 Biological Sciences 08 Information and Computing Sciences 11 Medical and Health Sciences HERDC Research category C1 Refereed article in a scholarly journal Copyright notice ©2020, The Authors Free to Read? Yes Persistent URL http://hdl.handle.net/10536/DRO/DU:30142034 Document type: Journal Article Collections: Open Access Collection A2I2 (Applied Artificial Intelligence Institute) Related Links Link Description Link to full-text (open access)     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au. Versions Version Filter Type Wed, 16 Sep 2020, 15:04:39 EST Thu, 17 Sep 2020, 05:02:17 EST Thu, 17 Sep 2020, 07:31:25 EST Sat, 03 Oct 2020, 06:04:37 EST Tue, 06 Oct 2020, 07:58:15 EST Tue, 06 Oct 2020, 08:07:28 EST Tue, 06 Oct 2020, 08:08:20 EST Fri, 30 Oct 2020, 14:54:31 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 54 Abstract Views, 1 File Downloads  -  Detailed Statistics Created: Wed, 16 Sep 2020, 15:04:39 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.