QAcon: single model quality assessment using protein structural and contact information with machine learning techniques | Zendy

Renzhi Cao | Zendy; Badri Adhikari | Zendy; Debswapna Bhattacharya | Zendy; Miao Sun | Zendy; Jie Hou | Zendy; Jianlin Cheng | Zendy

Open Access

QAcon: single model quality assessment using protein structural and contact information with machine learning techniques

Author(s) -

Renzhi Cao,

Badri Adhikari,

Debswapna Bhattacharya,

Miao Sun,

Jie Hou,

Jianlin Cheng

Publication year - 2016

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btw694

Subject(s) - computer science , source code , data mining , machine learning , artificial intelligence , protein structure prediction , artificial neural network , quality assessment , quality (philosophy) , feature (linguistics) , software , evaluation methods , protein structure , reliability engineering , philosophy , linguistics , physics , epistemology , nuclear magnetic resonance , engineering , programming language , operating system

Protein model quality assessment (QA) plays a very important role in protein structure prediction. It can be divided into two groups of methods: single model and consensus QA method. The consensus QA methods may fail when there is a large portion of low quality models in the model pool.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research