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epiACO - a method for identifying epistasis based on ant Colony optimization algorithm

BioData Mining201710:23

https://doi.org/10.1186/s13040-017-0143-7

Received: 27 October 2016

Accepted: 29 June 2017

Published: 6 July 2017

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Open Peer Review reports

Pre-publication versions of this article and author comments to reviewers are available by contacting info@biomedcentral.com.

Original Submission
27 Oct 2016 Submitted Original manuscript
24 Jan 2017 Reviewed Reviewer Report - Alfred Ultsch
30 Jan 2017 Reviewed Reviewer Report - Edward Keedwell
10 Mar 2017 Author responded Author comments - Yingxia Sun
Resubmission - Version 2
10 Mar 2017 Submitted Manuscript version 2
7 Apr 2017 Reviewed Reviewer Report - Edward Keedwell
25 May 2017 Author responded Author comments - Yingxia Sun
Resubmission - Version 3
25 May 2017 Submitted Manuscript version 3
12 Jun 2017 Reviewed Reviewer Report - Edward Keedwell
21 Jun 2017 Reviewed Reviewer Report - Alfred Ultsch
Resubmission - Version 4
Submitted Manuscript version 4
Publishing
29 Jun 2017 Editorially accepted
6 Jul 2017 Article published 10.1186/s13040-017-0143-7

How does Open Peer Review work?

Open peer review is a system where authors know who the reviewers are, and the reviewers know who the authors are. If the manuscript is accepted, the named reviewer reports are published alongside the article. Pre-publication versions of the article and author comments to reviewers are available by contacting info@biomedcentral.com. All previous versions of the manuscript and all author responses to the reviewers are also available.

You can find further information about the peer review system here.

Authors’ Affiliations

(1)
School of Information Science and Engineering, Qufu Normal University
(2)
Institute of Network Computing, Qufu Normal University
(3)
College of Electrical Engineering and Automation, Anhui University

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