Reply to: LncADeep performance on full-length transcripts
To address their suggestion, as mentioned previously, we have already updated our website (http://homepage.cs.latrobe.edu.au/ypchen/ncRNAanalysis/) to include the statement “Note: In our experiments, we have used the full-length model of LncADeep” and have also included the experiments of the partia...
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| Veröffentlicht in: | Nature machine intelligence 2021-03, Vol.3 (3), p.196-196 |
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| container_title | Nature machine intelligence |
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| creator | Amin, Noorul McGrath, Annette Chen, Yi-Ping Phoebe |
| description | To address their suggestion, as mentioned previously, we have already updated our website (http://homepage.cs.latrobe.edu.au/ypchen/ncRNAanalysis/) to include the statement “Note: In our experiments, we have used the full-length model of LncADeep” and have also included the experiments of the partial-length model of LncADeep. Data availability The dataset and source code are available at http://homepage.cs.latrobe.edu.au/ypchen/ncRNAanalysis/. LncFinder: an integrated platform for long non-coding RNA identification utilizing sequence intrinsic composition, structural information and physicochemical property. |
| doi_str_mv | 10.1038/s42256-019-0107-3 |
| format | Article |
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| ispartof | Nature machine intelligence, 2021-03, Vol.3 (3), p.196-196 |
| issn | 2522-5839 2522-5839 |
| language | eng |
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| source | SpringerLink Journals - AutoHoldings |
| subjects | 45/91 631/114/1305 631/114/1386 Bioinformatics Datasets Deep learning Engineering Experiments Matters Arising Source code Websites |
| title | Reply to: LncADeep performance on full-length transcripts |
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