Integrated learning method for recognizing ECM (extracellular matrix) protein

Abstract

The invention discloses an integrated learning method for recognizing ECM (extracellular matrix) protein. According to the method, data set building: a training sample set and an independent test sample set of an ECM protein sequence are built; the protein sequence in the training sample set is mapped into a numeric feature vector; a relatively effective feather subset is selected by an information gain ratio-incremental feature selection method, an integrated classifier model is built by an integrated learning method, and the problem of data set unbalance is solved; the independent test sample set is mapped into a numeric feature vector, the category of the test sample is obtained by a majority voting method on the basis of a predication result of the integrated learning method, and the performance of a prediction system is finally evaluated by utilizing the predication result of the test sample. The invention discloses a network server system for recognizing the ECM protein. Users do not need to understand the concrete executing process of ECM protein recognition, and the prediction result can be obtained only through inputting the protein sequence to be predicted.

Description

technical field [0001] The invention relates to the field of protein functional attribute recognition, in particular to an integrated learning method for recognizing extracellular matrix proteins. Background technique [0002] The extracellular matrix (ECM) is the microenvironment for the survival of cells and tissues, and plays an important role in the regulation of cell behavior and tissue properties. The powerful biological functions of ECM are attributed to the diversity of ECM proteins. The composition and dynamic changes of ECM proteins have an all-round impact on the proliferation, differentiation, migration of cells, morphogenesis and differentiation of tissues and other life phenomena. Meanwhile, dysfunction of ECM proteins can lead to numerous diseases. Proteoglycans and collagens are the main components of ECM proteins. Among them, proteoglycans regulate physiological activities such as tissue repair, tumor growth, cell adhesion, proliferation, and migration; c...

AI Technical Summary

Problems solved by technology

[0004] In order to solve the deficiencies in the prior art, the present invention discloses an integrated learning method for identifying extracellular matrix proteins, the purpose of which is to solve the problem of unbalanced data sets, and at the same time integrate a variety of sequence feature information to balance and improve the extracellular matrix. Prediction Accuracy of Protein Positive and Negative Samples

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