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- W2896811437 abstract "Background: Protein–peptide recognition plays an essential role in the orchestration and regulation of cell signaling networks, which is estimated to be responsible for up to 40% of biological interaction events in the human interactome and has recently been recognized as a new and attractive druggable target for drug development and disease intervention. Methods: We present a systematic review on the application of machine learning techniques in the quantitative modeling and prediction of protein–peptide binding affinity, particularly focusing on its implications for therapeutic peptide design. We also briefly introduce the physical quantities used to characterize protein–peptide affinity and attempt to extend the content of generalized machine learning methods. Results: Existing issues and future perspective on the statistical modeling and regression prediction of protein– peptide binding affinity are discussed. Conclusion: There is still a long way to go before establishment of general, reliable and efficient machine leaningbased protein–peptide affinity predictors." @default.
- W2896811437 created "2018-10-26" @default.
- W2896811437 creator A5036733870 @default.
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- W2896811437 creator A5074703170 @default.
- W2896811437 creator A5078088913 @default.
- W2896811437 date "2019-05-22" @default.
- W2896811437 modified "2023-10-17" @default.
- W2896811437 title "Machine Learning in Quantitative Protein–peptide Affinity Prediction: Implications for Therapeutic Peptide Design" @default.
- W2896811437 cites W1515235085 @default.
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- W2896811437 cites W1759855929 @default.
- W2896811437 cites W1828281654 @default.
- W2896811437 cites W1953008475 @default.
- W2896811437 cites W1969046853 @default.
- W2896811437 cites W1971769921 @default.
- W2896811437 cites W1977450595 @default.
- W2896811437 cites W1979715740 @default.
- W2896811437 cites W1980497258 @default.
- W2896811437 cites W1980955860 @default.
- W2896811437 cites W1983142511 @default.
- W2896811437 cites W1987539722 @default.
- W2896811437 cites W1990516841 @default.
- W2896811437 cites W1994048581 @default.
- W2896811437 cites W1997627388 @default.
- W2896811437 cites W2005162839 @default.
- W2896811437 cites W2010560388 @default.
- W2896811437 cites W2012757823 @default.
- W2896811437 cites W2016834015 @default.
- W2896811437 cites W2019347114 @default.
- W2896811437 cites W2020510681 @default.
- W2896811437 cites W2023272942 @default.
- W2896811437 cites W2032856926 @default.
- W2896811437 cites W2033757486 @default.
- W2896811437 cites W2041449172 @default.
- W2896811437 cites W2045362218 @default.
- W2896811437 cites W2048839385 @default.
- W2896811437 cites W2051294170 @default.
- W2896811437 cites W2051566695 @default.
- W2896811437 cites W2053302020 @default.
- W2896811437 cites W2059942519 @default.
- W2896811437 cites W2061636592 @default.
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- W2896811437 cites W2073503722 @default.
- W2896811437 cites W2073623394 @default.
- W2896811437 cites W2075713133 @default.
- W2896811437 cites W2077550665 @default.
- W2896811437 cites W2078336808 @default.
- W2896811437 cites W2083747978 @default.
- W2896811437 cites W2083931326 @default.
- W2896811437 cites W2088784800 @default.
- W2896811437 cites W2094619873 @default.
- W2896811437 cites W2098312592 @default.
- W2896811437 cites W2101052640 @default.
- W2896811437 cites W2106070848 @default.
- W2896811437 cites W2111405658 @default.
- W2896811437 cites W2113697512 @default.
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- W2896811437 cites W2120216126 @default.
- W2896811437 cites W2127325055 @default.
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- W2896811437 cites W2130479394 @default.
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- W2896811437 cites W2146360384 @default.
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- W2896811437 cites W2153594105 @default.
- W2896811437 cites W2156669671 @default.
- W2896811437 cites W2159454255 @default.
- W2896811437 cites W2164431990 @default.
- W2896811437 cites W2175637972 @default.
- W2896811437 cites W2208025102 @default.
- W2896811437 cites W2292990827 @default.
- W2896811437 cites W2322193123 @default.
- W2896811437 cites W2330730783 @default.
- W2896811437 cites W2395771819 @default.
- W2896811437 cites W2398467399 @default.
- W2896811437 cites W2400844889 @default.
- W2896811437 cites W2563096801 @default.
- W2896811437 cites W267236089 @default.
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- W2896811437 doi "https://doi.org/10.2174/1389200219666181012151944" @default.
- W2896811437 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30317994" @default.
- W2896811437 hasPublicationYear "2019" @default.
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