iUmami-SCM utilized a newly developed scoring card method (SCM) in conjunction with the propensity scores of amino acids and dipeptide. Our predictor demonstrated excellent prediction performance ability for predicting umami peptides as well as outperforming other commonly used machine learning classifiers...
The iUmami-SCM tool predicts the umami/non-umami taste of peptides based on their primary amino acid sequence employing a scoring card method (SCM) in conjunction with the propensity scores of amino acids and dipeptides. For its design, this tool is limited to the prediction of only peptide...
Nine potential umami peptides were screened using the iUmami_SCM, UMPred-FRL, Umami_YYDS, Umami-MRNN, Innovagen, Expasy-ProtParam, and ToxinPred tools. Peptides AGVEDVY, LFEQHPEYRK, AFDVQ, GPTVEEVD, NVVAGSDLR, ATNGSR, and VEVVALND (1mg/mL) were confirmed to possess umami taste, and...
MichaelYuhe requested review from yuaanlin, pan93412 and ScmTble May 24, 2024 03:16 MichaelYuhe added the documentation label May 24, 2024 pan93412 approved these changes May 24, 2024 View reviewed changes View details MichaelYuhe merged commit cb840ca into main May 24, 2024 4 checks...
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Compared to Umami-SCM and UMPred-FRL, iUP-BERT has superior performance in both independent testing and cross-validation. Despite notable advancements in the field, particularly in terms of independent testing, machine learning (ML)-based umami peptide detection algorithms that rely exclusively on ...
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