The definition of journal acceptance rate is the percentage of all articles submitted to Network Biology that was accepted for publication. The acceptance rate of Network Biology is still under calculation. Have you ever submitted your manuscript to Network Biology? Share with us!
The acceptance rate for an academic journal is dependent upon the relative demand for publishing in a particular journal, the peer review processes in place, the mix of invited and unsolicited submissions, and time to publication, among others. As such, it may be a proxy for perceived prestige and demand as compared to availability. However, locating acceptance rates for individual journals or for specific disciplines can be difficult, yet is necessary information for promotion and tenure activities. Journals with lower article acceptance rates are frequently considered to be more prestigious and more “meritorious”. As an internal benchmark, most journals will not publish their acceptance rates on their website. From their perspective, a consistently low acceptance rate may prove to be a deterrent to future submissions. Moreover, the method of calculating acceptance rates varies among journals. Some journals use all manuscripts received as a base for computing this rate. Other journals allow the editor to choose which papers are sent to reviewers and calculate the acceptance rate on those that are reviewed that is less than the total manuscripts received. Also, many editors do not maintain accurate records on this data and provide only a rough estimate.
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Network biology is a science that deals with the structure, function, regulation (control), design, and application, etc., of various biological networks. It is an interdisciplinary science based on life sciences (biology, ecology, medicine, etc.), mathematics and systems science (graph theory, network science, complexity theory, etc.), computational science (computation methods, programming), statistics, etc. The goal of this journal is to keep a record of the state-of-the-art research and promote the research work in these fast moving areas. The topics to be covered by Network Biology include, but are not limited to: Theories, algorithms and programs of network analysisEvolution, dynamics, optimization and control of biological networksNetwork construction, link predictionNetwork topology, topological analysis, relationship between topological structure and network functions, sensitivity analysis, network robustness and stabilityNetwork flow analysisDesign and formulation of biological networksEcological networks, food webs and natural equilibrium, co-evolution, co-extinction, biodiversity conservationMetabolic networks, protein-protein interaction networks, biochemical reaction networks, gene networks, transcriptional regulatory networks, cell cycle networks, phylogenetic networks, network motifs and modulesPhysiological networks, social networks, epidemiological networksNetwork regulation of metabolic processes, human diseases and ecological systemsSystem complexity, self-organization, emergence of biological systems, agent-based modeling, neural network modeling, and other network-based modeling, etc.Big data analytics of biological networks