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AutoCoV: tracking the early spread of COVID-19 in terms of the spatial and temporal patterns from embedding space by K-mer based deep learning
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Inyoung Sung | - |
dc.contributor.author | Sangseon Lee | - |
dc.contributor.author | Minwoo Pak | - |
dc.contributor.author | Yunyol Shin | - |
dc.contributor.author | Sun Kim | - |
dc.date.accessioned | 2022-05-17T04:44:38Z | - |
dc.date.available | 2022-05-17T04:44:38Z | - |
dc.date.issued | 2022-04-25 | - |
dc.identifier.citation | BMC Bioinformatics. Vol 23(Suppl 3):149 | ko_KR |
dc.identifier.issn | 1471-2105 | - |
dc.identifier.uri | https://hdl.handle.net/10371/179833 | - |
dc.description.abstract | The widely spreading coronavirus disease (COVID-19) has three major spreading properties: pathogenic mutations, spatial, and temporal propagation patterns. We know the spread of the virus geographically and temporally in terms of statistics, i.e., the number of patients. However, we are yet to understand the spread at the level of individual patients. As of March 2021, COVID-19 is wide-spread all over the world with new genetic variants. One important question is to track the early spreading patterns of COVID-19 until the virus has got spread all over the world.
In this work, we proposed AutoCoV, a deep learning method with multiple loss object, that can track the early spread of COVID-19 in terms of spatial and temporal patterns until the disease is fully spread over the world in July 2020. Performances in learning spatial or temporal patterns were measured with two clustering measures and one classification measure. For annotated SARS-CoV-2 sequences from the National Center for Biotechnology Information (NCBI), AutoCoV outperformed seven baseline methods in our experiments for learning either spatial or temporal patterns. For spatial patterns, AutoCoV had at least 1.7-fold higher clustering performances and an F1 score of 88.1%. For temporal patterns, AutoCoV had at least 1.6-fold higher clustering performances and an F1 score of 76.1%. Furthermore, AutoCoV demonstrated the robustness of the embedding space with an independent dataset, Global Initiative for Sharing All Influenza Data (GISAID). In summary, AutoCoV learns geographic and temporal spreading patterns successfully in experiments on NCBI and GISAID datasets and is the first of its kind that learns virus spreading patterns from the genome sequences, to the best of our knowledge. We expect that this type of embedding method will be helpful in characterizing fast-evolving pandemics. | ko_KR |
dc.description.sponsorship | This research was supported by the Collaborative Genome Program for Fostering New Post-Genome Industry of the National Research Foundation (NRF) funded by the Ministry of Science and ICT (MSIT) [No.NRF2014M3C9A3063541];
Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) [No.2021-0-01343, Artifcial Intelligence Graduate School Program (Seoul National University)]; Basic Science Research Program through the NRF funded by the Ministry of Education [No.NRF-2021R1A6A3A01086898], Republic of Korea. The funding bodies played no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript. Publication costs are funded by NRF funded by MSIT | ko_KR |
dc.language.iso | en | ko_KR |
dc.subject | COVID-19 | - |
dc.subject | SARS-CoV-2 | - |
dc.subject | Deep learning | - |
dc.subject | Sequence embedding | - |
dc.subject | Early spreading pattern | - |
dc.title | AutoCoV: tracking the early spread of COVID-19 in terms of the spatial and temporal patterns from embedding space by K-mer based deep learning | ko_KR |
dc.type | Article | ko_KR |
dc.identifier.doi | https://doi.org/10.1186/s12859-022-04679-x | ko_KR |
dc.citation.journaltitle | BMC Bioinformatics | ko_KR |
dc.language.rfc3066 | en | - |
dc.rights.holder | The Author(s) | - |
dc.date.updated | 2022-05-01T03:20:31Z | - |
dc.citation.number | Suppl 3 | ko_KR |
dc.citation.startpage | 149 | ko_KR |
dc.citation.volume | 23 | ko_KR |
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