Algorithmic Methods for Computational Molecular Biology
A special issue of Algorithms (ISSN 1999-4893).
Deadline for manuscript submissions: closed (15 December 2017) | Viewed by 6889
Special Issue Editor
Interests: structural bioinformatics; protein–protein interaction prediction and network analysis; executable modeling of signaling and regulatory networks
Special Issue Information
Dear Colleagues,
Algorithmic approaches in molecular biology have a long history, best indicated perhaps by the 25th anniversary, this year, of the ISMB—Intelligent Systems for Molecular Biology—and the ECCB—European Conference for Computational Biology—in its 16th issue, which have, internationally, become the main annual conference(s) for bioinformatics. Bioinformatics as the study of information processes in living systems has been around since 1970, but started thriving with the advent of genome-wide sequencing technology, at the start of this century. Throughout, algorithmic approaches have allowed us to process the data, test hypothesis, and drive our improved understanding of molecular biology.
Current challenges are abound. We need better algorithms to bridge the gap from structure to function or phenotype. Since many biological systems have no single or well-defined structure, we really also need algorithms that bridge the gap from sequence via dynamics to function. Applications lie, for example, in the dynamic modeling of protein–protein interactions, by time, or by cellular context, or by mutational background; in better prediction of the impact of SNVs, in regulatory regions, in protein coding regions, either in structured or disordered regions of the protein, or in regions where the structure is unknown; in tracking of molecules in super-resolution microscopy data, across time, and across cellular compartments; in integrating data from various sources, platforms, labs; in generating and/or processing meta-data to make valuable data FAIR—Findable, Applicable, Interoperable, and Re-usable.
Topics may include (but are not limited to):
- application of distributed learning approaches, e.g., in personalized health using the ‘health train’ concepts
- algorithms that enable the application of FAIR data principles to biological data
- application of graph theory to biological molecular networks, with an aim to integrate heterogeneous and dynamic data.
- executable models to study signalling and regulatory biological cellular processes
- machine learning approaches to predict molecular properties of proteins, such as structure, dynamics, interaction, or function.
- algorithms for sequence alignment and analysis
- algorithms for protein structure analysis
- analysis of streaming data in biology
- image analysis, e.g., in microscopy, particularly with a focus on dynamics and molecular aspect.
Dr. K. Anton Feenstra
Guest Editor
Manuscript Submission Information
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Keywords
- distributed learning
- personalized health
- FAIR data
- executable and graph models for biological processes
- algorithms for sequence and structure analysis
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