Mal-Prec: computational prediction of protein Malonylation sites via machine learning based feature integration : Malonylation site prediction.

ABSTRACT:

Background

Malonylation is a recently discovered post-translational modification that is associated with a variety of diseases such as Type 2 Diabetes Mellitus and different types of cancers. Compared with experimental identification of malonylation sites, computational method is a time-effective process with comparatively low costs.

Results

In this study, we proposed a novel computational model called Mal-Prec (Malonylation Prediction) for malonylation site prediction through the combination of Principal Component Analysis and Support Vector Machine. One-hot encoding, physio-chemical properties, and composition of k-spaced acid pairs were initially performed to extract sequence features. PCA was then applied to select optimal feature subsets while SVM was adopted to predict

SUBMITTER: Liu X 

PROVIDER: S-EPMC7682087 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature