BMC Systems Biology is an open access journal publishing original peer-reviewed research articles in experimental and theoretical aspects of the function of biological systems at the molecular, cellular or organismal level, in particular those addressing the engineering of biological systems, network modelling, quantitative analyses, integration of different levels of information and synthetic biology. BMC Systems Biology is part of the BMC series which publishes subject-specific journals focused on the needs of individual research communities across all areas of biology and medicine. We do not make editorial decisions on the basis of the interest of a study or its likely impact. Studies must be scientifically valid; for research articles this includes a scientifically sound research question, the use of suitable methods and analysis, and following community-agreed standards relevant to the research field. Specific criteria for other article types can be found in the submission guidelines. BMC series - open, inclusive and trusted.
BMC Systems Biology
Description
Identifiers
e-ISSN | 1752-0509 |
Publisher
BioMed Central
Additional information
Data set: Springer
Articles
BMC Systems Biology > 2019 > 13 > 2 > 1-15
Background Characterization of drug-protein interaction networks with biological features has recently become challenging in recent pharmaceutical science toward a better understanding of polypharmacology. Results We present a novel method for systematic analyses of the underlying features characteristic of drug-protein interaction networks, which we call “drug-protein interaction signatures” from...
BMC Systems Biology > 2019 > 13 > 2 > 1-9
Background Anti-tumor necrosis factor alpha (TNF- α) therapy has made a significant impact on treating psoriasis. Despite these agents being designed to block TNF- α activity, their mechanism of action in the remission of psoriasis is still not fully understood at the molecular level. Results To better understand the molecular mechanisms of Anti-TNF- α therapy, we analysed the global gene expression...
BMC Systems Biology > 2019 > 13 > 2 > 1-16
Background Biological experiments have confirmed the association between miRNAs and various diseases. However, such experiments are costly and time consuming. Computational methods help select potential disease-related miRNAs to improve the efficiency of biological experiments. Methods In this work, we develop a novel method using multiple types of data to calculate miRNA and disease similarity...