Machine Learning Techniques for High-Throughput Structure and Function Analys...

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Machine Learning Techniques for High-Throughput Structure and Function Analysis for Proteomics and Genomics

Call for Papers

With the development of high-throughput sequencing techniques, more and more sequencing data is available, such as genomics reads, transcriptomes data, and proteomics sequences. It is critical to use the data to uncover their structure and function. Genomics function can also be identified from the predicted results, such as motif identification, regulatory regions detection, and even epigenomics and disease relationship prediction.

Machine learning methods are important techniques for this task, especially for the ensemble learning, large scale data process, various kernel design, and imbalanced classification methods.

We invite authors to contribute original research manuscripts to this special issue, focusing on the advanced machine learning algorithms and their applications in proteomics or genomics sequences analysis.

Potential topics include, but are not limited to:

    Protein structure and function prediction with machine learning methods
    Special protein identification methods
    Epigenomics and disease relationship prediction
    Protein posttranslational modification (PTM or PTLM) sites prediction
    RNA posttranscriptional modification (PTCM) sites prediction
    Protein-protein binding site (PPBS) prediction
    Motif and regulatory elements identification from high-throughput data
    Advanced machine learning methods with the application to bioinformatics
    Cloud computing and parallel machine learning techniques for protein structure and genomics function analysis

Authors can submit their manuscripts via the Manuscript Tracking System at http://mts.hindawi.com/submit/jo ... onal.biology/mlth/.
Manuscript Due        Friday, 20 May 2016
First Round of Reviews        Friday, 12 August 2016
Publication Date        Friday, 7 October 2016
Lead Guest Editor

    Bin Liu, Harbin Institute of Technology Shenzhen, Shenzhen, Guangdong, China

Guest Editors

    Humberto González-Díaz, University of the Basque Country/Euskal Herriko Unibertsitatea (UPV/EHU), Leioa, Bizkaia, Spain
    Xun Lan, Stanford University, Stanford, USA