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