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PredIL13: stacking a variety of machine and deep learning methods with ESM-2 language model for identifying IL13-inducing peptides
http://hdl.handle.net/10228/0002001219
http://hdl.handle.net/10228/0002001219075953bd-9736-4b33-8496-7386697ec129
| 名前 / ファイル | ライセンス | アクション |
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10444524.pdf (5 MB)
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| アイテムタイプ | 共通アイテムタイプ(1) | |||||||||||||||||
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| 公開日 | 2025-02-04 | |||||||||||||||||
| タイトル | ||||||||||||||||||
| タイトル | PredIL13: stacking a variety of machine and deep learning methods with ESM-2 language model for identifying IL13-inducing peptides | |||||||||||||||||
| 言語 | en | |||||||||||||||||
| 著者 |
倉田, 博之
× 倉田, 博之
WEKO
2130
× Harun-Or-Roshid, Md.
× Tsukiyama, Sho
× 前田, 和勲
WEKO
16743
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| 著作権関連情報 | ||||||||||||||||||
| 権利情報Resource | http://creativecommons.org/licenses/by/4.0/ | |||||||||||||||||
| 権利情報 | Copyright (c) 2024 Kurata et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. | |||||||||||||||||
| 抄録 | ||||||||||||||||||
| 内容記述タイプ | Abstract | |||||||||||||||||
| 内容記述 | Interleukin (IL)-13 has emerged as one of the recently identified cytokine. Since IL-13 causes the severity of COVID-19 and alters crucial biological processes, it is urgent to explore novel molecules or peptides capable of including IL-13. Computational prediction has received attention as a complementary method to in-vivo and in-vitro experimental identification of IL-13 inducing peptides, because experimental identification is time-consuming, laborious, and expensive. A few computational tools have been presented, including the IL13Pred and iIL13Pred. To increase prediction capability, we have developed PredIL13, a cutting-edge ensemble learning method with the latest ESM-2 protein language model. This method stacked the probability scores outputted by 168 single-feature machine/deep learning models, and then trained a logistic regression-based meta-classifier with the stacked probability score vectors. The key technology was to implement ESM-2 and to select the optimal single-feature models according to their absolute weight coefficient for logistic regression (AWCLR), an indicator of the importance of each single-feature model. Especially, the sequential deletion of single-feature models based on the iterative AWCLR ranking (SDIWC) method constructed the meta-classifier consisting of the top 16 single-feature models, named PredIL13, while considering the model’s accuracy. The PredIL13 greatly outperformed the-state-of-the-art predictors, thus is an invaluable tool for accelerating the detection of IL13-inducing peptide within the human genome. | |||||||||||||||||
| 言語 | en | |||||||||||||||||
| 書誌情報 |
en : PLoS ONE 巻 19, 号 8, p. e0309078, 発行日 2024-08-22 |
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| 出版社 | ||||||||||||||||||
| 出版者 | Public Library of Science | |||||||||||||||||
| 言語 | en | |||||||||||||||||
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| 言語 | eng | |||||||||||||||||
| 資源タイプ | ||||||||||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||||||||||||||
| 資源タイプ | journal article | |||||||||||||||||
| 出版タイプ | ||||||||||||||||||
| 出版タイプ | VoR | |||||||||||||||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||||||||||||||
| DOI | ||||||||||||||||||
| 識別子タイプ | DOI | |||||||||||||||||
| 関連識別子 | https://doi.org/10.1371/journal.pone.0309078 | |||||||||||||||||
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| 収録物識別子タイプ | EISSN | |||||||||||||||||
| 収録物識別子 | 1932-6203 | |||||||||||||||||
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| 値 | yes | |||||||||||||||||
| 研究者情報 | ||||||||||||||||||
| URL | https://hyokadb02.jimu.kyutech.ac.jp/html/100000926_ja.html | |||||||||||||||||
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| 値 | 10444524 | |||||||||||||||||
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| 値 | 12585 | |||||||||||||||||
