ECMPride: prediction of human extracellular matrix proteins based on the ideal dataset using hybrid features with domain evidence.

ABSTRACT: Extracellular matrix (ECM) proteins play an essential role in various biological processes in multicellular organisms, and their abnormal regulation can lead to many diseases. For large-scale ECM protein identification, especially through proteomic-based techniques, a theoretical reference database of ECM proteins is required. In this study, based on the experimentally verified ECM datasets and by the integration of protein domain features and a machine learning model, we developed ECMPride, a flexible and scalable tool for predicting ECM proteins. ECMPride achieved excellent performance in predicting ECM proteins, with appropriate balanced accuracy and sensitivity, and the performance of ECMPride was shown to be superior to the previously developed tool. A new theoretical dataset of human

SUBMITTER: Liu B 

PROVIDER: S-EPMC7195829 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature