Extracellular matrix (ECM) proteins play an important role in a series of biological processes of cells. The study of ECM proteins is helpful to further comprehend their biological functions. We propose ECMP-RF (extracellular matrix proteins prediction by random forest) to predict ECM proteins. Firstly, the features of the protein sequence are extracted by combining encoding based on grouped weight, pseudo amino-acid composition, pseudo position-specific scoring matrix, a local descriptor, and an autocorrelation descriptor. Secondly, the synthetic minority oversampling technique (SMOTE) algorithm is employed to process the class imbalance data, and the elastic net (EN) is used to reduce the dimension of the feature vectors. Finally, the random forest (RF) classifier is used to predict the ECM proteins. Leave-one-out cross-validation shows that the balanced accuracy of the training and testing datasets is 97.3% and 97.9%, respectively. Compared with other state-of-the-art methods, ECMP-RF is significantly better than other predictors.
In this paper, we construct the real representation matrix of canonical hyperbolic quaternion matrices and give some properties in detail. Then, by means of the real representation, we study linear equations, the inverse and the generalized inverse of the canonical hyperbolic quaternion matrix and get some interesting results.
The identification of fertility-related proteins plays an essential part in understanding the embryogenesis of germ cell development. Since the traditional experimental methods are expensive and time-consuming to identify fertility-related proteins, the purposes of predicting protein functions from amino acid sequences appeared. In this paper, we propose a fertility-related protein prediction model. Firstly, the model combines protein physicochemical property information, evolutionary information and sequence information to construct the initial feature space ‘ALL’. Then, the least absolute shrinkage and selection operator (LASSO) is used to remove redundant features. Finally, light gradient boosting machine (LightGBM) is used as a classifier to predict. The 5-fold cross-validation accuracy of the training dataset is 88.5%, and the independent accuracy of the training dataset is 91.5%. The results show that our model is more competitive for the prediction of fertility-related proteins, which is helpful for the study of fertility diseases and related drug targets.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.