2022年10月21日 · Here we report such a method called EP-pred, an ensemble binary classifier, that combines three machine learning algorithms: SVM, KNN, and a Linear model. EP-pred has been evaluated against the Lipase Engineering Database together with a hidden Markov approach leading to a final set of ten sequences predicted to encode promiscuous esterases.

A machine learning program to predict promiscuity of esterases It will produce the following files: The different sequences are ranked based on the applicability domain or how similar is to the training set, since if they are too different you would be predicting on something that the classifier has not been trained on.

2022年10月21日 · Here we report such a method called EP-pred, an ensemble binary classifier, that combines three machine learning algorithms: SVM, KNN, and a Linear model. EP-pred has been evaluated against the Lipase Engineering Database together with a hidden Markov approach leading to a final set of ten sequences predicted to encode promiscuous esterases.

EP-pred is an ensemble binary classifier that combines three machine learning algorithms: SVM, KNN, and a Linear model that predicts promiscuity from sequence alone and has been evaluated against the Lipase Engineering Database together with a hidden Markov approach.

2022年10月21日 · Here we report such a method called EP-pred, an ensemble binary classifier, that combines three machine learning algorithms: SVM, KNN, and a Linear model. EP-pred has been evaluated against the...

2022年9月7日 · Our classifier, named EP-pred, combines three types of classification algorithms: SVM (support vector machines), KNN (k-nearest neighbors) and RidgeClassifier, one of the lin-ear models implemented in Scikit-Learn. EP-pred was then evaluated against LED and from those predicted to be positives, a final set of ten sequences were isolated and tested

• It will extract features from input esterase sequences in FASTA format. • It will filter those sequences that are too different from the training dataset because of the applicability domain phenomenon, since using a model to predict on proteins that the system has not seen and trained on before will likely produce errors.

Here we report such a method called EP-pred, an ensemble binary classifier, that combines three machine learning algorithms: SVM, KNN, and a Linear model. EP-pred has been evaluated against the Lipase Engineering Database together with a hidden Markov approach leading to a final set of ten sequences predicted to encode promiscuous esterases.

Here we report such a method called EP-pred, an ensemble binary classifier, that combines three machine learning algorithms: SVM, KNN, and a Linear model. EP-pred has been evaluated against the Lipase Engineering Database together with a hidden Markov approach leading to a final set of ten sequences predicted to encode promiscuous esterases.

2022年10月21日 · Xiang R, Fernandez-Lopez L, Robles-Martín A, Ferrer M, Guallar V. EP-Pred: A Machine Learning Tool for Bioprospecting Promiscuous Ester Hydrolases. Biomolecules. 2022; 12(10):1529. https://doi.org/10.3390/biom12101529