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AMLM 2017 : CfP: Special Issue on Advances in Machine Learning Methods for Understanding Multiomics Data

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Link: http://www.hindawi.com/journals/BMRI/si/430361/cfp/
 
When N/A
Where N/A
Submission Deadline Sep 8, 2017
Notification Due Dec 1, 2017
Final Version Due Jan 26, 2018
 

Call For Papers

Call for Papers: Special Issue on Advances in Machine Learning Methods for Understanding Multiomics Data

http://www.hindawi.com/journals/BMRI/si/430361/cfp/

BioMed Research International

Important dates:

Manuscript Due - Friday, 8 September 2017
First Round of Reviews - Friday, 1 December 2017
Publication Date - Friday, 26 January 2018

Overview:

Nowadays, a vast amount of data is being collected as a result of rather increasingly large-scale experiments aimed at supporting development in integrating genomics, proteomics, metabolomics, and other omics sciences. The role of machine learning methods for analyzing such massive data to further our understanding for biology and medicine is undoubted. However, extracting meaningful information from data originated from different sources is challenging, and traditional methodological approaches may not be suitable in this context.

This special issue aims at promoting the use of new machine learning developments for understanding multiomics data. Although this call is open to any original research that uses machine learning for multiomics data integration analysis, we particularly encourage submissions that propose novel machine learning developments.

Topics:

Potential topics include but are not limited to the following:

* Original research using machine learning methods as the main approach to the integration of multiomics data or omics data with data from other sources
* Comparative assessment of a (preferably) large number of machine learning algorithms used for multiomics data analysis
* Novel machine learning algorithms aimed at modeling multiomics data integration
* Variants of existing machine learning algorithms aimed at modeling multiomics data integration which are acceptable if they are sufficiently novel to be considered original research
* Description of annotated and/or curated datasets large enough to be potentially used in new machine learning experiments for understanding multiomics data, provided that they are made publicly available to the scientific community
* New software tools specifically designed to analyze multiomics data using machine learning methods, provided that they are made publicly available to the scientific community

Submission:

Authors can submit their manuscripts through the Manuscript Tracking System at:
https://mts.hindawi.com/submit/journals/bmri/bioinformatics/amlm/

Lead Guest Editor:

Ivan Olier, Manchester Metropolitan University, Manchester, UK
https://www2.mmu.ac.uk/scmdt/staff/profile/index.php?id=2765

Guest Editors:

Sandra Ortega-Martorell, Liverpool John Moores University, Liverpool, UK
https://www.ljmu.ac.uk/about-us/staff-profiles/faculty-of-engineering-and-technology/department-of-applied-mathematics/sandra-ortega-martorell

Margarida Julia-Sape, Universitat Autònoma de Barcelona, Barcelona, Spain
http://gabrmn.uab.es/margarida.julia

Radka Stoyanova, University of Miami, Miami, USA
http://radonc.med.miami.edu/about-us/our-physicists/radka-stoyanova

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