DNA binding protein prediction method based on local evolution information

Abstract

The invention discloses a DNA binding protein prediction method based on local evolution information. The method comprises the specific steps: extracting the evolution information of a protein, segmenting the evolution information into local evolution information, and obtaining a feature vector for prediction; sorting the feature vectors according to contribution degrees of the feature vectors tothe model by using an SVMRFE + CBR feature extraction method, and removing irrelevant features; dividing the feature vectors without the irrelevant features into five parts by adopting a five-fold cross validation method, and inputting the four parts as a training set into an SVM model to train the SVM model; and after processing the protein, inputting the feature vector of the protein into an SVMmodel to obtain a prediction result. In the invention, the characteristics of multiple protein sequences are combined, wherein the local evolution information, original evolution information, amino acid composition and dipeptide information of the protein are combined, so that local and overall information of the protein is all contained, and the precision of a DNA binding protein prediction calculation model is improved.

Description

technical field [0001] The invention belongs to the field of DNA binding protein prediction of bioinformatics, and specifically relates to a DNA binding protein prediction method based on local evolution information. Background technique [0002] Identification of DNA-binding proteins based on sequence information is one of the most challenging problems in the field of genome annotation. DNA-binding proteins play crucial roles in various cellular biological processes, such as gene expression and transcription. However, identification using experimental methods is time-consuming and expensive. In the face of the increasingly large amount of data in the post-genome era, it is extremely important to find a method for quickly and accurately predicting whether a protein is DBP. [0003] In recent years, many prediction methods for DBP have emerged, which can be roughly divided into two categories, structure-based methods and sequence-based methods. Structure-based methods main...

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Problems solved by technology

[0005] However, most prediction models extract a large number of parameters for local information, and the input parameters are huge, thus weakening the influence of global information on the contribution of the model

Although algorithms are used in some models to balance the weight of global and local information, a large amount of useless information makes the model scale too large and redundant, resulting in insufficient prediction efficiency of the model

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