BioSystems

BioSystems - ISSN 0303-2647
Source Normalized Impact per Paper (SNIP): 0.769 Source Normalized Impact per Paper (SNIP):
SNIP measures contextual citation impact by weighting citations based on the total number of citations in a subject field.
SCImago Journal Rank (SJR): 0.623 SCImago Journal Rank (SJR):
SJR is a prestige metric based on the idea that not all citations are the same. SJR uses a similar algorithm as the Google page rank; it provides a quantitative and a qualitative measure of the journal’s impact.
Impact Factor: 1.495 (2015) Impact Factor:
The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years.
© Thomson Reuters Journal Citation Reports 2015
5 Year Impact Factor: 1.62 (2015) Five-Year Impact Factor:
To calculate the five year Impact Factor, citations are counted in 2014 to the previous five years and divided by the source items published in the previous five years.
© Journal Citation Reports 2015, Published by Thomson Reuters
Volumes: Volumes 151-162
Issues: 12 issues
ISSN: 03032647

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Description

BioSystems encourages experimental, computational, and theoretical articles that link biology, evolutionary thinking, and the information processing sciences. The link areas form a circle that encompasses the fundamental nature of biological information processing, computational modeling of complex biological systems, evolutionary models of computation, the application of biological principles to the design of novel computing systems, and the use of biomolecular materials to synthesize artificial systems that capture essential principles of natural biological information processing.

The categories and topics listed below are examples, the editors will be happy to comment on the relevance of other topics:

Molecular Evolution

Self-organizing and self-replicating systems

Origins and evolution of the genetic mechanism

Biological Information Processing

Molecular recognition

Cellular control

Neuromolecular computing

Biological adaptability Molecular computing technologies

Evolutionary Systems

Stochastic evolutionary algorithms

Evolutionary optimization

Simulation of genetic and ecological systems

Applications (neural nets, machine learning, robotics)

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