Publication Name	World Journal of Clinical Oncology
Manuscript ID	39544
Country	United States

Received	2018-04-27 00:40
Peer-Review Started	2018-04-27 02:39
To Make the First Decision	2018-07-09 07:58
Return for Revision	2018-07-10 09:29
Revised	2018-07-24 18:25
Second Decision	2018-08-02 12:21
Accepted by Journal Editor-in-Chief
Accepted by Company Editor-in-Chief	2018-08-05 16:05
Articles in Press	2018-08-05 16:05
Publication Fee Transferred
Edit the Manuscript by Language Editor
Typeset the Manuscript	2018-09-10 08:57
Publish the Manuscript Online	2018-09-14 11:38

ISSN	2218-4333 (online)
Open Access	This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Copyright	© The Author(s) 2018. Published by Baishideng Publishing Group Inc. All rights reserved.
Article Reprints	For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
Permissions	For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
Publisher	Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
Website	http://www.wjgnet.com

Category	Mathematical & Computational Biology
Manuscript Type	Basic Study
Article Title	Tunable structure priors for Bayesian rule learning for knowledge integrated biomarker discovery
Manuscript Source	Invited Manuscript
All Author List	Jeya Balaji Balasubramanian and Vanathi Gopalakrishnan
ORCID	Author(s) ORCID Number Jeya Balaji Balasubramanian http://orcid.org/0000-0002-0025-8410 Vanathi Gopalakrishnan http://orcid.org/0000-0002-7813-4055
Funding Agency and Grant Number	Funding Agency Grant Number NIGMS, National Institutes of Health R01GM100387
Corresponding Author	Vanathi Gopalakrishnan, PhD, Associate Professor, Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Room 530, 5607 Baum Boulevard, Pittsburgh, PA 15206, United States. vanathi@pitt.edu
Key Words	Supervised machine learning; Rule-based models; Bayesian methods; Background knowledge; Informative priors; Biomarker discovery
Core Tip	Bayesian rule learning is a unique rule learning algorithm that infers rule models from searched Bayesian networks. We extended it to allow the incorporation of prior domain knowledge using a mathematically robust Bayesian framework with structure priors. The hyperparameter of the structure priors enables the user to control the influence of their specified prior knowledge. This opens up many possibilities including incorporating uncertain knowledge that can interact with data accordingly during inference.
Publish Date	2018-09-14 11:38
Citation	Balasubramanian JB, Gopalakrishnan V. Tunable structure priors for bayesian rule learning for knowledge integrated biomarker discovery. World J Clin Oncol 2018; 9(5): 98-109
URL	http://www.wjgnet.com/2218-4333/full/v9/i5/98.htm
DOI	http://dx.doi.org/10.5306/wjco.v9.i5.98

Full Article (PDF)	WJCO-9-98.pdf
Full Article (Word)	WJCO-9-98.doc
Manuscript File	39544-Review.docx
Answering Reviewers	39544-Answering reviewers.pdf
Audio Core Tip	39544-Audio core tip.mp3
Conflict-of-Interest Disclosure Form	39544-Conflict-of-interest statement.pdf
Copyright License Agreement	39544-Copyright assignment.pdf
Approved Grant Application Form(s) or Funding Agency Copy of any Approval Document(s)	39544-Grant application form(s).pdf
Peer-review Report	39544-Peer-review(s).pdf
Scientific Misconduct Check	39544-Scientific misconduct check.pdf
Scientific Editor Work List	39544-Scientific editor work list.pdf