scholarly journals A machine learning information retrieval approach to protein fold recognition

2006 ◽  
Vol 22 (12) ◽  
pp. 1456-1463 ◽  
Author(s):  
J. Cheng ◽  
P. Baldi
Keyword(s):  
Machine Learning ◽  
Information Retrieval ◽  
Fold Recognition ◽  
Protein Fold ◽  
Protein Fold Recognition

scholarly journals Recent Trends in Machine Learning-based Protein Fold Recognition Methods

2020 ◽  
Vol 11 (4) ◽  
pp. 11233-11243
Keyword(s):  
Machine Learning ◽  
Protein Sequence ◽  
Protein Function ◽  
Three Dimensional ◽  
Fold Recognition ◽  
Machine Learning Algorithms ◽  
Dimensional Structure ◽  
Protein Fold ◽  
Three Dimensional Structure ◽  
Protein Fold Recognition

Proteins are macromolecules that enable life. Protein function is due to its three-dimensional structure and shape. It is challenging to understand how a linear sequence of amino acid residues folds into a three-dimensional structure. Machine learning-based methods may help significantly in reducing the gap present between known protein sequence and structure. Identifying protein folds from a sequence can help predict protein tertiary structure, determine protein function, and give insights into protein-protein interactions. This work focuses on the following aspects. The kind of features such as sequential, structural, functional, and evolutionary extracted for representing protein sequence and different methods of extracting these features. This work also includes details of machine learning algorithms used with respective settings and protein fold recognition structures. Detailed performance comparison of well-known works is also given.

scholarly journals Protein fold recognition using geometric kernel data fusion

2014 ◽  
Vol 30 (13) ◽  
pp. 1850-1857 ◽  
Author(s):  
Pooya Zakeri ◽  
Ben Jeuris ◽  
Raf Vandebril ◽  
Yves Moreau
Keyword(s):  
Data Fusion ◽  
Fold Recognition ◽  
Protein Fold ◽  
Protein Fold Recognition
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