Recognition of Protein Network for Bioinformatics Knowledge Analysis Using Support Vector Machine.
Protein is the material foundation of living things, and it directly takes part in and runs the process of living things itself. Predicting protein complexes helps us understand the structure and function of complexes, and it is an important foundation for studying how cells work. Genome-wide protei...
| Published in: | BioMed Research International pp. 1 - 12 |
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| Main Authors: | , , , , , |
| Format: | pictorial research tables/charts Journal Article |
| Published: |
Wiley-Blackwell
4/23/2022
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=156465533&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 156465533 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 4/23/2022 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 156465533 156465533 156465533 10.1155/2022/2273648 156465533 ppf: 1 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Recognition of Protein Network for Bioinformatics Knowledge Analysis Using Support Vector Machine. aug: au: Kaur, Arshpreet Chitre, Abhijit Wanjale, Kirti Kumar, Pankaj Miah, Shahajan Alguno, Arnold C. affil: GNA University, Village Hargobindgarh, Phagwara, Punjab, India sug: subj: Bioinformatics Biological Markers Metabolic Networks and Pathways Proteins Metabolism Support Vector Machine Knowledge Evaluation Signal Processing, Computer Assisted Algorithms Genome Matrix Metalloproteinases Genetics ab: Protein is the material foundation of living things, and it directly takes part in and runs the process of living things itself. Predicting protein complexes helps us understand the structure and function of complexes, and it is an important foundation for studying how cells work. Genome-wide protein interaction (PPI) data is growing as high-throughput experiments become more common. The aim of this research is that it provides a dual-tree complex wavelet transform which is used to find out about the structure of proteins. It also identifies the secondary structure of protein network. Many computer-based methods for predicting protein complexes have also been developed in the field. Identifying the secondary structure of a protein is very important when you are studying protein characteristics and properties. This is how the protein sequence is added to the distance matrix. The scope of this research is that it can confidently predict certain protein complexes rapidly, which compensates for shortcomings in biological research. The three-dimensional coordinates of C atom are used to do this. According to the texture information in the distance matrix, the matrix is broken down into four levels by the double-tree complex wavelet transform because it has four levels. The subband energy and standard deviation in different directions are taken, and then, the two-dimensional feature vector is used to show the secondary structure features of the protein in a way that is easy to understand. Then, the KNN and SVM classifiers are used to classify the features that were found. Experiments show that a new feature called a dual-tree complex wavelet can improve the texture granularity and directionality of the traditional feature extraction method, which is called secondary structure. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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