E-Bitter: Bitterant Prediction by the Consensus Voting From the Machine-Learning Methods.

ABSTRACT: In-silico bitterant prediction received the considerable attention due to the expensive and laborious experimental-screening of the bitterant. In this work, we collect the fully experimental dataset containing 707 bitterants and 592 non-bitterants, which is distinct from the fully or partially hypothetical non-bitterant dataset used in the previous works. Based on this experimental dataset, we harness the consensus votes from the multiple machine-learning methods (e.g., deep learning etc.) combined with the molecular fingerprint to build the bitter/bitterless classification models with five-fold cross-validation, which are further inspected by the Y-randomization test and applicability domain analysis. One of the best consensus models affords the accuracy, precision, specificity, sensitivi

SUBMITTER: Zheng S 

PROVIDER: S-EPMC5885771 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature