Open AccessA Data Mining Method For Improving the Prediction Of Bioinformatics Data
Author(s) -
Tong Wang,
Wenjun Tan,
Jianxin Xue
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2137/1/012067
Subject(s) - jackknife resampling , eigenvalues and eigenvectors , feature (linguistics) , computer science , encoding (memory) , pattern recognition (psychology) , function (biology) , dimension (graph theory) , data mining , feature vector , algorithm , artificial intelligence , mathematics , biology , statistics , genetics , linguistics , physics , philosophy , quantum mechanics , estimator , pure mathematics
The composition of proteins nearly correlated with its function. Therefore, it is very ungently important to discuss a method that can automatically forecast protein structure. The fusion encoding method of PseAA and DC was adopted to describe the protein features. Using this encoding method to express protein sequences will produce higher dimensional feature vectors. This paper uses the algorithm of predigesting the characteristic dimension of proteins. By extracting significant feature vectors from the primitive feature vectors, eigenvectors with high dimensions are changed to eigenvectors with low dimensions. The experimental method of jackknife test is adopted. The consequences indicate that the arithmetic put forwarded here is appropriate for identifying whether the given protein is a homo-oligomer or a hetero-oligomer.