Protein interaction prediction method

A prediction method, a protein technology, applied in the field of bioinformatics, can solve problems with selection bias, unable to cover NIPs, etc., and achieve the effects of good robustness, easy generalization, and good predictive performance

Pending Publication Date: 2021-01-22
HANGZHOU NORMAL UNIVERSITY
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Problems solved by technology

This sampling method reduces the false negative rate, and the obtained negative data is more reliable, but it cannot cover NIPs in the same subcellular location, resulting in selection bias in the model prediction, and it is difficult for the generated model to obtain better results in real work scenarios

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  • Protein interaction prediction method

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[0029] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments. It should be understood that these examples are only used to illustrate the present invention and are not intended to limit the scope of the present invention. The operating methods not indicated in the following examples are generally in accordance with conventional conditions, or in accordance with the conditions suggested by the manufacturer.

[0030] The protein interaction prediction method of the present invention based on the sampling strategy of non-interacting protein pairs fused with biological semantics is as follows: figure 1 Shown, specifically, include the steps:

[0031] (A) Yeast PPIs data were obtained from the S. cerevisiae core subset (“Scere20080708.txt”) in the DIP database (Lukasz, Salwinski et al., Nucleic Acids Research, suppl_1 (2004): suppl_1.). The original yeast PPIs data was first clustered and analyzed using the CD...

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Abstract

The invention discloses a protein interaction prediction method based on a sampling strategy of non-interaction protein pairs fused with biological semantics. Protein pairs in different molecular functions, biological processes and cell components are sampled and combined based on GO term semantic similarity to obtain an NIPs subset. With a negative set sampling strategy, a non-protein interactiondata set with higher quality and low selection deviation is obtained, so that a protein interaction prediction model with better robustness and better prediction performance is obtained through training.

Description

technical field [0001] The invention relates to the technical field of biological information, in particular to a protein interaction prediction method based on a sampling strategy of non-interacting protein pairs fused with biological semantics. Background technique [0002] Protein-protein interactions (PPIs) play an important role in the structure and function of cells, and the study and reconstruction of PPIs network will not only help to understand cellular processes and disease pathogenesis, but also help to develop therapeutic drugs . Existing experimental methods for PPIs have labor-intensive and time-consuming limitations, leading to the need for computational prediction of protein interactions. Although some advanced PPIs calculation and prediction models have been proposed, most of the calculation models require both positive and negative samples for model training, which requires high-quality PPIs and NIPs (non-interacting proteins) data. Currently, PPIs valida...

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Application Information

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IPC IPC(8): G16B5/00G16B15/30G16B25/10
CPCG16B5/00G16B15/30G16B25/10
Inventor 黄剑平李达
Owner HANGZHOU NORMAL UNIVERSITY
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