Statistical reverse engineering methods for high-throughput molecular data
EURASIP Journal on Bioinformatics and Systems Biology welcomes submissions to the new special issue on Statistical reverse engineering methods for high-throughput molecular data.
With the recent advancement in the high-throughput technologies, molecular biology is rapidly evolving into a quantitative science. This achievement led to an increased role of reverse engineering methods in making sense of this high-dimensional data. The development and application of such methods will guide new experiments, shed new light on existing hypotheses and will eventually trigger new discoveries that have been difficult to achieve using the traditional biochemical approaches alone. This special issue will focus on statistical reverse engineering methods for high-throughput molecular data.
We invite all scientists working in the field of statistics, systems biology, bioinformatics and engineering to contribute original research articles and tutorial papers to this special issue. All submitted manuscripts will be peer-reviewed.
Developing reverse engineering methods for high-throughput molecular data presents paramount importance in bioinformatics and systems biology. Understanding how genes, proteins and metabolites interact opens the possibility to understand deeply the mechanisms of actions of diseases and to develop efficient drugs.
Potential topics include, but are not limited to:
- Inference for probabilistic graphical models
- Dynamic Bayesian networks
- Causal inference from observational data
- Methods dedicated to time-resolved data (e.g. Granger causality)
- Scalable algorithms and parallelization for inference
- Special methods for single-cell high-throughput data (single-cell DNA/RNA sequencing, mass cytometry)
- Special methods for reconstructing signaling pathways (e.g., from protein-arrays, mass-spectrometry)
Before submitting your manuscript, please ensure you have carefully read the Instructions for Authors for EURASIP Journal on Bioinformatics and Systems Biology. The complete manuscript should be submitted through the EURASIP Journal on Bioinformatics and Systems Biology submission system. To ensure that you submit to the correct special issue please select the appropriate section in the drop-down menu upon submission. In addition, indicate within your cover letter that you wish your manuscript to be considered as part of the special issue on Statistical reverse engineering methods for high-throughput molecular data. All submissions will undergo rigorous peer review and accepted articles will be published within the journal as a collection.
Deadline for submissions: September 15, 2016
Lead guest editor:
Heinz Koeppl, Technische Universitaet Darmstadt, Department of Electrical Engineering and Information Technology Darmstadt, Germany
Nurgazy Sulaimanov, Technische Universitaet Darmstadt, Department of Electrical Engineering and Information Technology Darmstadt, Germany
Maria Rodriguez Martinez, IBM Research - Zurich, Department of Cognitive Computing and Computational Sciences Rueschlikon, Switzerland
Submissions will also benefit from the usual benefits of open access publication:
- Rapid publication: Online submission, electronic peer review and production make the process of publishing your article simple and efficient
- High visibility and international readership in your field: Open access publication ensures high visibility and maximum exposure for your work - anyone with online access can read your article
- No space constraints: Publishing online means unlimited space for figures, extensive data and video footage
- Authors retain copyright, licensing the article under a Creative Commons license: articles can be freely redistributed and reused as long as the article is correctly attributed
If you would like to let your peers know about this open special issue, download this Call for Papers and share it with them.
For editorial enquiries please contact firstname.lastname@example.org
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