scholarly journals Protein Sequences Features Extraction for Predicting Beta- Turns and their Types: A Review

2019 ◽  
Vol 15 (9) ◽  
pp. 1331-1340
Author(s):  
Murtada Khalafallah Elbashir Elfaki
Keyword(s):  
Protein Sequences ◽  
Features Extraction ◽  
Beta Turns

An Investigation on Gesture Analysis and Geometric Features Extraction

2015 ◽  
Vol 03 (01) ◽  
pp. 387-392
Author(s):  
NOOR ADNAN IBRAHEEM ◽  
MOKHTAR MOHAMMED HASAN ◽  
RAFIQULZAMAN KHAN
Keyword(s):  
Features Extraction ◽  
Geometric Features ◽  
Gesture Analysis

DBP-PSSM: Combination of evolutionary profiles with the XGBoost algorithm to improve the identification of DNA-binding proteins

2020 ◽  
Vol 23 ◽  
Author(s):  
Yanping Zhang ◽  
Pengcheng Chen ◽  
Ya Gao ◽  
Jianwei Ni ◽  
Xiaosheng Wang
Keyword(s):  
Logistic Regression ◽  
Protein Structure ◽  
Dna Binding ◽  
Molecular Biology ◽  
Binding Proteins ◽  
Protein Sequences ◽  
Low Complexity ◽  
Dna Binding Proteins ◽  
Position Information ◽  
Position Representation

Aim and Objective:: Given the rapidly increasing number of molecular biology data available, computational methods of low complexity are necessary to infer protein structure, function, and evolution. Method:: In the work, we proposed a novel mthod, FermatS, which based on the global position information and local position representation from the curve and normalized moments of inertia, respectively, to extract features information of protein sequences. Furthermore, we use the generated features by FermatS method to analyze the similarity/dissimilarity of nine ND5 proteins and establish the prediction model of DNA-binding proteins based on logistic regression with 5-fold crossvalidation. Results:: In the similarity/dissimilarity analysis of nine ND5 proteins, the results are consistent with evolutionary theory. Moreover, this method can effectively predict the DNA-binding proteins in realistic situations. Conclusion:: The findings demonstrate that the proposed method is effective for comparing, recognizing and predicting protein sequences. The main code and datasets can download from https://github.com/GaoYa1122/FermatS.

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