Aims and Scope

The overall aim of “EURASIP Journal on Bioinformatics and Systems Biology” is to publish research results related to signal processing and bioinformatics theories and techniques relevant to a wide area of applications into the core new disciplines of genomics, proteomics, and systems biology.

The journal is intended to offer a common platform for scientists from several areas including signal processing, bioinformatics, statistics, biology and medicine, who are interested in the development of algorithmic, mathematical, statistical, modeling, simulation, data mining, and computational techniques, as demanded by various applications in genomics, proteomics, system biology, and more general in health and medicine.

Papers should emphasize original results related to the theoretical and algorithmic aspects of signal processing and bioinformatics, in close connection with the applications to genomics, proteomics, systems biology and medicine. Tutorial papers, especially those emphasizing strong components of signal processing or bioinformatics in multidisciplinary views of genomics, proteomics and systems biology are also welcome. The journal will embrace a wide range of topics, and will accommodate different exposition styles, to help scientists with various backgrounds, e.g., engineering, bioinformatics, or biology, to interact effortlessly and to facilitate the exchange of information across the multidisciplinary areas involved. EURASIP Journal on Bioinformatics and Systems Biology employs a paperless, electronic submission and evaluation system to promote a rapid turnaround in the peer review process.

Subject areas include (but are by no means limited to):

  • Reverse engineering of biological circuits
  • Data mining methods for genomics and proteomics
  • Signal Processing theory and techniques for systems biology
  • Modeling and simulation of biological networks
  • Nanotechnology in genomics and proteomics
  • Signal processing methods in sequence analysis
  • Information theoretic approaches to genomics and proteomics
  • Microarray image and data analysis
  • Noise models in high-throughput technologies
  • Integration of heterogeneous data