Accu RMSD: A machine learning approach to predicting structure similarity of docked protein complexes

Bahar Akbal-Delibas; Marc Pomplun; Nurit Haspel

doi:10.1145/2649387.2649392

Accu RMSD: A machine learning approach to predicting structure similarity of docked protein complexes

Bahar Akbal-Delibas
, Marc Pomplun
, Nurit Haspel

Computer Science

University of Massachusetts Boston

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Original language	English
Title of host publication	ACM BCB 2014 - 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics
Publisher	Association for Computing Machinery
Pages	289-296
Number of pages	8
ISBN (Electronic)	9781450328944
DOIs	https://doi.org/10.1145/2649387.2649392
State	Published - Sep 20 2014
Event	5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, ACM BCB 2014 - Newport Beach, United States Duration: Sep 20 2014 → Sep 23 2014

Publication series

Name	ACM BCB 2014 - 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics

Conference

Conference	5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, ACM BCB 2014
Country/Territory	United States
City	Newport Beach
Period	9/20/14 → 9/23/14

ASJC Scopus Subject Areas

Health Informatics
Computer Science Applications
Software
Biomedical Engineering

Keywords

Machine learning
Neural networks
Protein docking and refinement
Rmsd prediction
Scoring functions

Access to Document

10.1145/2649387.2649392

Cite this

Akbal-Delibas, B., Pomplun, M., & Haspel, N. (2014). Accu RMSD: A machine learning approach to predicting structure similarity of docked protein complexes. In ACM BCB 2014 - 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (pp. 289-296). (ACM BCB 2014 - 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics). Association for Computing Machinery. https://doi.org/10.1145/2649387.2649392

Accu RMSD: A machine learning approach to predicting structure similarity of docked protein complexes. / Akbal-Delibas, Bahar; Pomplun, Marc; Haspel, Nurit.
ACM BCB 2014 - 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics. Association for Computing Machinery, 2014. p. 289-296 (ACM BCB 2014 - 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Akbal-Delibas, B, Pomplun, M & Haspel, N 2014, Accu RMSD: A machine learning approach to predicting structure similarity of docked protein complexes. in ACM BCB 2014 - 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics. ACM BCB 2014 - 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, Association for Computing Machinery, pp. 289-296, 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, ACM BCB 2014, Newport Beach, United States, 9/20/14. https://doi.org/10.1145/2649387.2649392

Akbal-Delibas B, Pomplun M, Haspel N. Accu RMSD: A machine learning approach to predicting structure similarity of docked protein complexes. In ACM BCB 2014 - 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics. Association for Computing Machinery. 2014. p. 289-296. (ACM BCB 2014 - 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics). doi: 10.1145/2649387.2649392

Akbal-Delibas, Bahar ; Pomplun, Marc ; Haspel, Nurit. / Accu RMSD : A machine learning approach to predicting structure similarity of docked protein complexes. ACM BCB 2014 - 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics. Association for Computing Machinery, 2014. pp. 289-296 (ACM BCB 2014 - 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics).

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ER -

Accu RMSD: A machine learning approach to predicting structure similarity of docked protein complexes

Publication series

Conference

ASJC Scopus Subject Areas

Keywords

Access to Document

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