Protein structure class prediction method based on weighted composition position vectors and support vector machine

Abstract

The invention discloses a protein structure class prediction method based on weighted composition position vectors and a support vector machine. Firstly, protein structure class normal data sets are selected or established, weighting factors are introduced based on composition position vectors, and to-be-predicted protein sequences are represented through the weighted composition position vectors; the weighted composition position vectors are combined with the support vector machine, and the protein structure class prediction method is established through a direct multiclass classification strategy. According to the method, information of amino acid compositions is contained, position information of each amino acid residue in the protein sequences is also contained, the one-to-one correspondence function relation is formed between the position information and the protein sequences, the weighting factors are introduced to the composition position vector representation method, and prediction accuracy can be obviously improved by means of adjusting the weighting factors. The method is simple, rapid and sensitive, and is expected to be applied to other protein prediction fields.

Description

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AI Technical Summary

Benefits of technology

In summary, it provides technical benefits that include improving predictive performance based on specific compositions or positions within proteins compared to previous methods such as genetic algorithms (GAs). It suggests adding weights from both location vectors and aminophosphate residues into existing models to improve their predictions. Additionally, we suggest applying these techniques directly onto new datasets without building up too many variables. Overall, our technology helps identify important features related to diseases like cancer through identifying changes caused by certain mutations.

Problems solved by technology

This patents describes various techniques used during molecular analysis that aim at predicting how well certain structures or functions within an organism's gene may work properly. These include identifying specific parts from these structures called primary structures through their orientation relative to each others; understanding what they fit together correctly helps identify any defective ones. Additionally, there exist several ways to determine if two structures share similar chemical compositions such as alpha-, beta-, gamma-, etc.; while some existing models use more complicated mathematical relationships like partial hydrolysis, peeling off layers containing α -walls, and uncoupling groups involving multiple atoms.

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