1,932 results match your criteria BMC Systems Biology [Journal]


Correction to: miRNAs and target genes in the blood as biomarkers for the early diagnosis of Parkinson's disease.

BMC Syst Biol 2019 Feb 12;13(1):20. Epub 2019 Feb 12.

Department of Physiology, College of Life Science, Hebei Normal University, Shijiazhuang, China.

AbstractIt was highlighted that the original article [1] contained some typesetting mistakes in the first paragraph of the Background section. Read More

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http://dx.doi.org/10.1186/s12918-019-0701-3DOI Listing
February 2019

In-silico comparison of two induction regimens (7 + 3 vs 7 + 3 plus additional bone marrow evaluation) in acute myeloid leukemia treatment.

BMC Syst Biol 2019 Jan 31;13(1):18. Epub 2019 Jan 31.

Institute of Biostatistics and Clinical Research, Westfälische Wilhelms-Universität Münster, Münster, Germany.

Background: Clinical integration of systems biology approaches is gaining in importance in the course of digital revolution in modern medicine. We present our results of the analysis of an extended mathematical model describing abnormal human hematopoiesis. The model is able to describe the course of an acute myeloid leukemia including its treatment. Read More

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http://dx.doi.org/10.1186/s12918-019-0684-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6357450PMC
January 2019
1 Read

How to prevent viremia rebound? Evidence from a PRRSv data-supported model of immune response.

BMC Syst Biol 2019 Jan 29;13(1):15. Epub 2019 Jan 29.

Division of Genetics and Genomics, The Roslin Institute, Easter Bush, Midlothian, UK.

Background: Understanding what determines the between-host variability in infection dynamics is a key issue to better control the infection spread. In particular, pathogen clearance is desirable over rebounds for the health of the infected individual and its contact group. In this context, the Porcine Respiratory and Reproductive Syndrome virus (PRRSv) is of particular interest. Read More

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https://bmcsystbiol.biomedcentral.com/articles/10.1186/s1291
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http://dx.doi.org/10.1186/s12918-018-0666-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6352383PMC
January 2019
1 Read

Correction to: circular RNA expression profiles during the differentiation of mouse neural stem cells.

BMC Syst Biol 2019 Jan 24;13(1):14. Epub 2019 Jan 24.

The First Affiliated Hospital with Nanjing Medical University, Nanjing, 210019, Jiangsu, China.

It was highlighted that the original article [1] contained a mistake in the grant number in the Funding section of the Declarations, and in the legend of Fig. 6. This Correction article shows the incorrect and correct version of the Funding and the legend of Fig. Read More

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http://dx.doi.org/10.1186/s12918-019-0682-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6345063PMC
January 2019
2.435 Impact Factor

Correction to: MISC: missing imputation for single-cell RNA sequencing data.

BMC Syst Biol 2019 Jan 22;13(1):13. Epub 2019 Jan 22.

Joint Bioinformatics Program, University of Arkansas Little Rock George Washington Donaghey College of Engineering & IT and University of Arkansas for Medical Sciences, Little Rock, AR, 72204, USA.

It was highlighted that the original article [1] contained a typesetting error in the last name of Allon Canaan. This was incorrectly captured as Allon Canaann in the original article which has since been updated. Read More

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http://dx.doi.org/10.1186/s12918-019-0681-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6343234PMC
January 2019

A mathematical model to estimate cholesterylester transfer protein (CETP) triglycerides flux in human plasma.

BMC Syst Biol 2019 Jan 22;13(1):12. Epub 2019 Jan 22.

Institute of Clinical Chemistry and Laboratory Medicine, Medical Centre - University of Freiburg, Freiburg im Breisgau, Germany.

Background: Cholesterylester transfer protein (CETP) modulates the composition of various lipoproteins associated with cardiovascular disease. Despite its central role in lipoprotein metabolism, its mode of action is still not fully understood. Here we present a simple way to estimate CETP-mediated lipid fluxes between different lipoprotein fractions. Read More

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http://dx.doi.org/10.1186/s12918-019-0679-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6341636PMC
January 2019

miRNAs and target genes in the blood as biomarkers for the early diagnosis of Parkinson's disease.

BMC Syst Biol 2019 Jan 21;13(1):10. Epub 2019 Jan 21.

Department of Physiology, College of Life Sciences, Hebei Normal University, Shijiazhuang, China.

Background: Parkinson's disease (PD) is the second most common neurodegenerative disease, and it is a multifactorial disease with no definite diagnostic index. The aim of this study is to construct a molecular network to find molecules that play important roles in the progression of PD with the goal of using them diagnostically and for early intervention.

Results: We downloaded two gene expression profiles (GSE54536 and GSE100054) from the Expression Omnibus (GEO) database to analyze possible markers. Read More

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http://dx.doi.org/10.1186/s12918-019-0680-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6341689PMC
January 2019
2 Reads

Use of genome-scale models to get new insights into the marine actinomycete genus Salinispora.

BMC Syst Biol 2019 Jan 21;13(1):11. Epub 2019 Jan 21.

Centre for Biotechnology and Bioengineering (CeBiB), Department of Chemical Engineering, Biotechnology and Materials, University of Chile, Beauchef 851, Santiago, Chile.

Background: There is little published regarding metabolism of Salinispora species. In continuation with efforts performed towards this goal, this study is focused on new insights into the metabolism of the three-identified species of Salinispora using constraints-based modeling. At present, only one manually curated genome-scale metabolic model (GSM) for Salinispora tropica strain CNB-440 has been built despite the role of Salinispora strains in drug discovery. Read More

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http://dx.doi.org/10.1186/s12918-019-0683-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6341766PMC
January 2019

Model-based virtual patient analysis of human liver regeneration predicts critical perioperative factors controlling the dynamic mode of response to resection.

BMC Syst Biol 2019 Jan 16;13(1). Epub 2019 Jan 16.

Daniel Baugh Institute for Functional Genomics/Computational Biology, Department of Pathology, Anatomy, and Cell Biology, Thomas Jefferson University, Philadelphia, PA, USA.

Background: Liver has the unique ability to regenerate following injury, with a wide range of variability of the regenerative response across individuals. Existing computational models of the liver regeneration are largely tuned based on rodent data and hence it is not clear how well these models capture the dynamics of human liver regeneration. Recent availability of human liver volumetry time series data has enabled new opportunities to tune the computational models for human-relevant time scales, and to predict factors that can significantly alter the dynamics of liver regeneration following a resection. Read More

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https://bmcsystbiol.biomedcentral.com/articles/10.1186/s1291
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http://dx.doi.org/10.1186/s12918-019-0678-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6335689PMC
January 2019
4 Reads

An agent-based model of the Notch signaling pathway elucidates three levels of complexity in the determination of developmental patterning.

BMC Syst Biol 2019 Jan 14;13(1). Epub 2019 Jan 14.

Computer Science Department, Lafayette College, Easton, PA, 18042, USA.

Background: The Notch signaling pathway is involved in cell fate decision and developmental patterning in diverse organisms. A receptor molecule, Notch (N), and a ligand molecule (in this case Delta or Dl) are the central molecules in this pathway. In early Drosophila embryos, these molecules determine neural vs. Read More

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http://dx.doi.org/10.1186/s12918-018-0672-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6332573PMC
January 2019
1 Read

Multi-omics integration reveals molecular networks and regulators of psoriasis.

BMC Syst Biol 2019 Jan 14;13(1). Epub 2019 Jan 14.

Department of Integrative Biology and Physiology, University of California, Los Angeles, 610 Charles E. Young Dr. East, Los Angeles, CA, 90095, USA.

Background: Psoriasis is a complex multi-factorial disease, involving both genetic susceptibilities and environmental triggers. Genome-wide association studies (GWAS) and epigenome-wide association studies (EWAS) have been carried out to identify genetic and epigenetic variants that are associated with psoriasis. However, these loci cannot fully explain the disease pathogenesis. Read More

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https://bmcsystbiol.biomedcentral.com/articles/10.1186/s1291
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http://dx.doi.org/10.1186/s12918-018-0671-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6332659PMC
January 2019
4 Reads

Plasmodium vivax readiness to transmit: implication for malaria eradication.

BMC Syst Biol 2019 Jan 11;13(1). Epub 2019 Jan 11.

Department of Global Health (GH) & Center for Drug Discovery and Innovation (CDDI), College of Public Health, University of South Florida, Tampa, FL, 33612, USA.

Background: The lack of a continuous long-term in vitro culture system for Plasmodium vivax severely limits our knowledge of pathophysiology of the most widespread malaria parasite. To gain direct understanding of P. vivax human infections, we used Next Generation Sequencing data mining to unravel parasite in vivo expression profiles for P. Read More

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http://dx.doi.org/10.1186/s12918-018-0669-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6330404PMC
January 2019
1 Read

Constructing network topologies for multiple signal-encoding functions.

BMC Syst Biol 2019 Jan 11;13(1). Epub 2019 Jan 11.

The State Key Laboratory for Artificial Microstructures and Mesoscopic Physics, School of Physics, Peking University, Beijing, 100871, China.

Background: Cells use signaling protein networks to sense their environment and mediate specific responses. Information about environmental stress is usually encoded in the dynamics of the signaling molecules, and qualitatively distinct dynamics of the same signaling molecule can lead to dramatically different cell fates. Exploring the design principles of networks with multiple signal-encoding functions is important for understanding how distinct dynamic patterns are shaped and integrated by real cellular networks, and for building cells with targeted sensing-response functions via synthetic biology. Read More

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https://bmcsystbiol.biomedcentral.com/articles/10.1186/s1291
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http://dx.doi.org/10.1186/s12918-018-0676-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6330498PMC
January 2019
3 Reads

SLIMEr: probing flexibility of lipid metabolism in yeast with an improved constraint-based modeling framework.

BMC Syst Biol 2019 Jan 11;13(1). Epub 2019 Jan 11.

Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden.

Background: A recurrent problem in genome-scale metabolic models (GEMs) is to correctly represent lipids as biomass requirements, due to the numerous of possible combinations of individual lipid species and the corresponding lack of fully detailed data. In this study we present SLIMEr, a formalism for correctly representing lipid requirements in GEMs using commonly available experimental data.

Results: SLIMEr enhances a GEM with mathematical constructs where we Split Lipids Into Measurable Entities (SLIME reactions), in addition to constraints on both the lipid classes and the acyl chain distribution. Read More

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http://dx.doi.org/10.1186/s12918-018-0673-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6330394PMC
January 2019
2 Reads

Modelling overflow metabolism in Escherichia coli with flux balance analysis incorporating differential proteomic efficiencies of energy pathways.

BMC Syst Biol 2019 Jan 10;13(1). Epub 2019 Jan 10.

Department of Engineering Science, University of Oxford, Parks Road, Oxford, OX1 3PJ, UK.

Background: The formation of acetate by fast-growing Escherichia coli (E. coli) is a commonly observed phenomenon, often referred to as overflow metabolism. Among various studies that have been carried over decades, a recent work (Basan, M. Read More

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http://dx.doi.org/10.1186/s12918-018-0677-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6329140PMC
January 2019
1 Read

DynamicME: dynamic simulation and refinement of integrated models of metabolism and protein expression.

BMC Syst Biol 2019 Jan 9;13(1). Epub 2019 Jan 9.

Department of Bioengineering, University of California at San Diego, 9500 Gilman Drive, La Jolla, 92093, CA, USA.

Background: Genome-scale models of metabolism and macromolecular expression (ME models) enable systems-level computation of proteome allocation coupled to metabolic phenotype.

Results: We develop DynamicME, an algorithm enabling time-course simulation of cell metabolism and protein expression. DynamicME correctly predicted the substrate utilization hierarchy on a mixed carbon substrate medium. Read More

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http://dx.doi.org/10.1186/s12918-018-0675-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6327497PMC
January 2019
1 Read

Pathway crosstalk perturbation network modeling for identification of connectivity changes induced by diabetic neuropathy and pioglitazone.

BMC Syst Biol 2019 Jan 7;13(1). Epub 2019 Jan 7.

Department of Biomedical Sciences, University of North Dakota School of Medicine and Health Sciences, Grand Forks, North Dakota, 58202, USA.

Background: Aggregation of high-throughput biological data using pathway-based approaches is useful to associate molecular results to functional features related to the studied phenomenon. Biological pathways communicate with one another through the crosstalk phenomenon, forming large networks of interacting processes.

Results: In this work, we present the pathway crosstalk perturbation network (PXPN) model, a novel model used to analyze and integrate pathway perturbation data based on graph theory. Read More

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http://dx.doi.org/10.1186/s12918-018-0674-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6322225PMC
January 2019
2 Reads
2.435 Impact Factor

Integrating node embeddings and biological annotations for genes to predict disease-gene associations.

BMC Syst Biol 2018 Dec 31;12(Suppl 9):138. Epub 2018 Dec 31.

Data Analytics Department, Institute for Infocomm Research, Singapore, Singapore.

Background: Predicting disease causative genes (or simply, disease genes) has played critical roles in understanding the genetic basis of human diseases and further providing disease treatment guidelines. While various computational methods have been proposed for disease gene prediction, with the recent increasing availability of biological information for genes, it is highly motivated to leverage these valuable data sources and extract useful information for accurately predicting disease genes.

Results: We present an integrative framework called N2VKO to predict disease genes. Read More

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http://dx.doi.org/10.1186/s12918-018-0662-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6311944PMC
December 2018
1 Read

Analysis of significant protein abundance from multiple reaction-monitoring data.

BMC Syst Biol 2018 Dec 31;12(Suppl 9):123. Epub 2018 Dec 31.

Department of Statistics, Seoul National University, Seoul, South Korea.

Background: Discovering reliable protein biomarkers is one of the most important issues in biomedical research. The ELISA is a traditional technique for accurate quantitation of well-known proteins. Recently, the multiple reaction-monitoring (MRM) mass spectrometry has been proposed for quantifying newly discovered protein and has become a popular alternative to ELISA. Read More

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http://dx.doi.org/10.1186/s12918-018-0656-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6311902PMC
December 2018
2 Reads

A unified solution for different scenarios of predicting drug-target interactions via triple matrix factorization.

BMC Syst Biol 2018 Dec 31;12(Suppl 9):136. Epub 2018 Dec 31.

Department of Computer Science, The University of Hong Kong, Hong Kong, China.

Background: During the identification of potential candidates, computational prediction of drug-target interactions (DTIs) is important to subsequent expensive validation in wet-lab. DTI screening considers four scenarios, depending on whether the drug is an existing or a new drug and whether the target is an existing or a new target. However, existing approaches have the following limitations. Read More

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http://dx.doi.org/10.1186/s12918-018-0663-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6311903PMC
December 2018
1 Read

Optimizing gene set annotations combining GO structure and gene expression data.

BMC Syst Biol 2018 Dec 31;12(Suppl 9):133. Epub 2018 Dec 31.

School of Computer Science and Technology, Harbin Institute of Technology, West Da-Zhi Street, Harbin, China.

Background: With the rapid accumulation of genomic data, it has become a challenge issue to annotate and interpret these data. As a representative, Gene set enrichment analysis has been widely used to interpret large molecular datasets generated by biological experiments. The result of gene set enrichment analysis heavily relies on the quality and integrity of gene set annotations. Read More

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http://dx.doi.org/10.1186/s12918-018-0659-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6311910PMC
December 2018
1 Read
2.435 Impact Factor

Hot spot prediction in protein-protein interactions by an ensemble system.

BMC Syst Biol 2018 Dec 31;12(Suppl 9):132. Epub 2018 Dec 31.

Advanced Analytics Institute and Centre for Health Technologies, University of Technology, Sydney, Sydney, Broadway, NSW, 2007, Australia.

Background: Hot spot residues are functional sites in protein interaction interfaces. The identification of hot spot residues is time-consuming and laborious using experimental methods. In order to address the issue, many computational methods have been developed to predict hot spot residues. Read More

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http://dx.doi.org/10.1186/s12918-018-0665-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6311905PMC
December 2018
1 Read

FMSM: a novel computational model for predicting potential miRNA biomarkers for various human diseases.

BMC Syst Biol 2018 Dec 31;12(Suppl 9):121. Epub 2018 Dec 31.

Department of Computing, Hong Kong Polytechnic University, Hong Kong, 999077, China.

Background: MicroRNA (miRNA) plays a key role in regulation mechanism of human biological processes, including the development of disease and disorder. It is necessary to identify potential miRNA biomarkers for various human diseases. Computational prediction model is expected to accelerate the process of identification. Read More

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http://dx.doi.org/10.1186/s12918-018-0664-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6311922PMC
December 2018
1 Read

Laplacian normalization and bi-random walks on heterogeneous networks for predicting lncRNA-disease associations.

BMC Syst Biol 2018 Dec 31;12(Suppl 9):122. Epub 2018 Dec 31.

School of Mathematics and Computational Science, Xiangtan University, Hunan, 411105, China.

Background: Evidences have increasingly indicated that lncRNAs (long non-coding RNAs) are deeply involved in important biological regulation processes leading to various human complex diseases. Experimental investigations of these disease associated lncRNAs are slow with high costs. Computational methods to infer potential associations between lncRNAs and diseases have become an effective prior-pinpointing approach to the experimental verification. Read More

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http://dx.doi.org/10.1186/s12918-018-0660-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6311918PMC
December 2018
1 Read

Multi-CSAR: a multiple reference-based contig scaffolder using algebraic rearrangements.

BMC Syst Biol 2018 Dec 31;12(Suppl 9):139. Epub 2018 Dec 31.

Department of Computer Science, National Tsing Hua University, Hsinchu, 30013, Taiwan.

Background: One of the important steps in the process of assembling a genome sequence from short reads is scaffolding, in which the contigs in a draft genome are ordered and oriented into scaffolds. Currently, several scaffolding tools based on a single reference genome have been developed. However, a single reference genome may not be sufficient alone for a scaffolder to generate correct scaffolds of a target draft genome, especially when the evolutionary relationship between the target and reference genomes is distant or some rearrangements occur between them. Read More

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http://dx.doi.org/10.1186/s12918-018-0654-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6311912PMC
December 2018
1 Read

rPCMP: robust p-value combination by multiple partitions with applications to ATAC-seq data.

BMC Syst Biol 2018 Dec 31;12(Suppl 9):141. Epub 2018 Dec 31.

School of Mathematics and Statistics, Xi'an Jiaotong University, Xianning West 28, Xi'an, China.

Background: Evaluating the significance for a group of genes or proteins in a pathway or biological process for a disease could help researchers understand the mechanism of the disease. For example, identifying related pathways or gene functions for chromatin states of tumor-specific T cells will help determine whether T cells could reprogram or not, and further help design the cancer treatment strategy. Some existing p-value combination methods can be used in this scenario. Read More

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http://dx.doi.org/10.1186/s12918-018-0661-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6311921PMC
December 2018
1 Read

Network-based logistic regression integration method for biomarker identification.

BMC Syst Biol 2018 Dec 31;12(Suppl 9):135. Epub 2018 Dec 31.

Center for Computational Systems Biology, Shanghai Key Laboratory for Contemporary Applied Mathematics, School of Mathematical Sciences, Fudan University, No.220 Handan Road, Shanghai, 200433, China.

Background: Many mathematical and statistical models and algorithms have been proposed to do biomarker identification in recent years. However, the biomarkers inferred from different datasets suffer a lack of reproducibilities due to the heterogeneity of the data generated from different platforms or laboratories. This motivates us to develop robust biomarker identification methods by integrating multiple datasets. Read More

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http://dx.doi.org/10.1186/s12918-018-0657-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6311907PMC
December 2018
1 Read

Computational drug repositioning using meta-path-based semantic network analysis.

BMC Syst Biol 2018 Dec 31;12(Suppl 9):134. Epub 2018 Dec 31.

School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing, 100044, People's Republic of China.

Background: Drug repositioning is a promising and efficient way to discover new indications for existing drugs, which holds the great potential for precision medicine in the post-genomic era. Many network-based approaches have been proposed for drug repositioning based on similarity networks, which integrate multiple sources of drugs and diseases. However, these methods may simply view nodes as the same-typed and neglect the semantic meanings of different meta-paths in the heterogeneous network. Read More

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http://dx.doi.org/10.1186/s12918-018-0658-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6311940PMC
December 2018
1 Read

Identification of active signaling pathways by integrating gene expression and protein interaction data.

BMC Syst Biol 2018 Dec 31;12(Suppl 9):120. Epub 2018 Dec 31.

School of Medicine, Western Sydney University, Campbelltown, NSW, Australia.

Background: Signaling pathways are the key biological mechanisms that transduce extracellular signals to affect transcription factor mediated gene regulation within cells. A number of computational methods have been developed to identify the topological structure of a specific signaling pathway using protein-protein interaction data, but they are not designed for identifying active signaling pathways in an unbiased manner. On the other hand, there are statistical methods based on gene sets or pathway data that can prioritize likely active signaling pathways, but they do not make full use of active pathway structure that link receptor, kinases and downstream transcription factors. Read More

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http://dx.doi.org/10.1186/s12918-018-0655-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6311899PMC
December 2018
2 Reads
2.435 Impact Factor

Parameter estimation of qualitative biological regulatory networks on high performance computing hardware.

BMC Syst Biol 2018 Dec 29;12(1):146. Epub 2018 Dec 29.

Atta-ur-Rahman School of Applied Bio sciences (ASAB), NUST, Islamabad, 44000, Pakistan.

Background: Biological Regulatory Networks (BRNs) are responsible for developmental and maintenance related functions in organisms. These functions are implemented by the dynamics of BRNs and are sensitive to regulations enforced by specific activators and inhibitors. The logical modeling formalism by René Thomas incorporates this sensitivity with a set of logical parameters modulated by available regulators, varying with time. Read More

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http://dx.doi.org/10.1186/s12918-018-0670-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6311083PMC
December 2018
4 Reads

Mechanistic insight into activation of MAPK signaling by pro-angiogenic factors.

BMC Syst Biol 2018 Dec 27;12(1):145. Epub 2018 Dec 27.

Department of Biomedical Engineering, University of Southern California, Los Angeles, California, USA.

Background: Angiogenesis is important in physiological and pathological conditions, as blood vessels provide nutrients and oxygen needed for tissue growth and survival. Therefore, targeting angiogenesis is a prominent strategy in both tissue engineering and cancer treatment. However, not all of the approaches to promote or inhibit angiogenesis lead to successful outcomes. Read More

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http://dx.doi.org/10.1186/s12918-018-0668-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6307205PMC
December 2018
1 Read

Multiple transcription factors contribute to inter-chromosomal interaction in yeast.

BMC Syst Biol 2018 Dec 21;12(Suppl 8):140. Epub 2018 Dec 21.

Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, 7000 Fannin St. Suite 820, Houston, TX, 77030, USA.

Background: Chromatin interactions medicated by genomic elements located throughout the genome play important roles in gene regulation and can be identified with the technologies such as high-throughput chromosome conformation capture (Hi-C), followed by next-generation sequencing. These techniques were wildly used to reveal the relative spatial disposition of chromatins in human, mouse and yeast. Unlike metazoan where CTCF plays major roles in mediating chromatin interactions, in yeast, the transcription factors (TFs) involved in this biological process are poorly known. Read More

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http://dx.doi.org/10.1186/s12918-018-0643-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6302461PMC
December 2018
1 Read

Metabolomics of mammalian brain reveals regional differences.

BMC Syst Biol 2018 Dec 21;12(Suppl 8):127. Epub 2018 Dec 21.

Program in Developmental Biology, Baylor College of Medicine, Houston, TX, USA.

Background: The mammalian brain is organized into regions with specific biological functions and properties. These regions have distinct transcriptomes, but little is known whether they may also differ in their metabolome. The metabolome, a collection of small molecules or metabolites, is at the intersection of the genetic background of a given cell or tissue and the environmental influences that affect it. Read More

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http://dx.doi.org/10.1186/s12918-018-0644-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6302375PMC
December 2018
1 Read

Identification of gene signatures from RNA-seq data using Pareto-optimal cluster algorithm.

BMC Syst Biol 2018 Dec 21;12(Suppl 8):126. Epub 2018 Dec 21.

Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, 77030, TX, USA.

Background: Gene signatures are important to represent the molecular changes in the disease genomes or the cells in specific conditions, and have been often used to separate samples into different groups for better research or clinical treatment. While many methods and applications have been available in literature, there still lack powerful ones that can take account of the complex data and detect the most informative signatures.

Methods: In this article, we present a new framework for identifying gene signatures using Pareto-optimal cluster size identification for RNA-seq data. Read More

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http://dx.doi.org/10.1186/s12918-018-0650-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6302366PMC
December 2018
2 Reads

Circular RNA expression profiles during the differentiation of mouse neural stem cells.

BMC Syst Biol 2018 Dec 21;12(Suppl 8):128. Epub 2018 Dec 21.

The First Affiliated Hospital with Nanjing Medical University, Nanjing, 210019, Jiangsu, China.

Background: Circular RNAs (circRNAs) have recently been found to be expressed in human brain tissue, and many lines ofevidence indicate that circRNAs play regulatory roles in neurodevelopment. Proliferation and differentiation of neural stem cells (NSCs) are critical parts during development of central nervous system (CNS).To date, there have been no reports ofcircRNA expression profiles during the differentiation of mouse NSCs. Read More

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https://bmcsystbiol.biomedcentral.com/articles/10.1186/s1291
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http://dx.doi.org/10.1186/s12918-018-0651-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6302452PMC
December 2018
9 Reads
2.435 Impact Factor

scdNet: a computational tool for single-cell differential network analysis.

BMC Syst Biol 2018 Dec 21;12(Suppl 8):124. Epub 2018 Dec 21.

Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, 10675, Taiwan.

Background: Single-cell RNA sequencing (scRNA-Seq) is an emerging technology that has revolutionized the research of the tumor heterogeneity. However, the highly sparse data matrices generated by the technology have posed an obstacle to the analysis of differential gene regulatory networks.

Results: Addressing the challenges, this study presents, as far as we know, the first bioinformatics tool for scRNA-Seq-based differential network analysis (scdNet). Read More

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http://dx.doi.org/10.1186/s12918-018-0652-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6302455PMC
December 2018
2 Reads

GSAE: an autoencoder with embedded gene-set nodes for genomics functional characterization.

BMC Syst Biol 2018 Dec 21;12(Suppl 8):142. Epub 2018 Dec 21.

Greehey Children's Cancer Research Institute, The University of Texas Health Science Center at San Antonio, San Antonio, TX, 78229, USA.

Background: Bioinformatics tools have been developed to interpret gene expression data at the gene set level, and these gene set based analyses improve the biologists' capability to discover functional relevance of their experiment design. While elucidating gene set individually, inter-gene sets association is rarely taken into consideration. Deep learning, an emerging machine learning technique in computational biology, can be used to generate an unbiased combination of gene set, and to determine the biological relevance and analysis consistency of these combining gene sets by leveraging large genomic data sets. Read More

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http://dx.doi.org/10.1186/s12918-018-0642-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6302374PMC
December 2018
1 Read

Prediction of protein self-interactions using stacked long short-term memory from protein sequences information.

BMC Syst Biol 2018 Dec 21;12(Suppl 8):129. Epub 2018 Dec 21.

Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Science, Urumqi, 830011, China.

Background: Self-interacting Proteins (SIPs) plays a critical role in a series of life function in most living cells. Researches on SIPs are important part of molecular biology. Although numerous SIPs data be provided, traditional experimental methods are labor-intensive, time-consuming and costly and can only yield limited results in real-world needs. Read More

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https://bmcsystbiol.biomedcentral.com/articles/10.1186/s1291
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http://dx.doi.org/10.1186/s12918-018-0647-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6302371PMC
December 2018
2 Reads

Integrating proteomic and phosphoproteomic data for pathway analysis in breast cancer.

BMC Syst Biol 2018 Dec 21;12(Suppl 8):130. Epub 2018 Dec 21.

Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China.

Background: As protein is the basic unit of cell function and biological pathway, shotgun proteomics, the large-scale analysis of proteins, is contributing greatly to our understanding of disease mechanisms. Proteomics study could detect the changes of both protein expression and modification. With the releases of large-scale cancer proteome studies, how to integrate acquired proteomic and phosphoproteomic data in more comprehensive pathway analysis becomes implemented, but remains challenging. Read More

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http://dx.doi.org/10.1186/s12918-018-0646-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6302460PMC
December 2018
3 Reads

Classifying mild traumatic brain injuries with functional network analysis.

BMC Syst Biol 2018 Dec 21;12(Suppl 8):131. Epub 2018 Dec 21.

Department of Neurobiology and Anatomy, University of Texas Health Science Center at Houston, 6431 Fannin Street, Houston, TX, USA.

Background: Traumatic brain injury (TBI) represents a critical health problem of which timely diagnosis and treatment remain challenging. TBI is a result of an external force damaging brain tissue, accompanied by delayed pathogenic events which aggravate the injury. Molecular responses to different mild TBI subtypes have not been well characterized. Read More

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http://dx.doi.org/10.1186/s12918-018-0645-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6302365PMC
December 2018
1 Read

An experimental design framework for Markovian gene regulatory networks under stationary control policy.

BMC Syst Biol 2018 Dec 21;12(Suppl 8):137. Epub 2018 Dec 21.

Department of Electrical and Computer Engineering, Texas A&M University, College Station, 77843, TX, USA.

Background: A fundamental problem for translational genomics is to find optimal therapies based on gene regulatory intervention. Dynamic intervention involves a control policy that optimally reduces a cost function based on phenotype by externally altering the state of the network over time. When a gene regulatory network (GRN) model is fully known, the problem is addressed using classical dynamic programming based on the Markov chain associated with the network. Read More

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http://dx.doi.org/10.1186/s12918-018-0649-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6302376PMC
December 2018
1 Read

The International Conference on Intelligent Biology and Medicine (ICIBM) 2018: systems biology on diverse data types.

BMC Syst Biol 2018 Dec 21;12(Suppl 8):125. Epub 2018 Dec 21.

Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA.

Between June 10-12, 2018, the International Conference on Intelligent Biology and Medicine (ICIBM 2018) was held in Los Angeles, California, USA. The conference included 11 scientific sessions, four tutorials, one poster session, four keynote talks and four eminent scholar talks that covered a wide range of topics in 3D genome structure analysis and visualization, next generation sequencing analysis, computational drug discovery, medical informatics, cancer genomics and systems biology. Systems biology has been a main theme in ICIBM 2018, with exciting advances presented in many areas of systems biology, covering various different data types such as gene regulation, circular RNAs expression, single-cell RNA-Seq, inter-chromosomal interactions, metabolomics, proteomics and phosphoproteomics. Read More

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http://dx.doi.org/10.1186/s12918-018-0648-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6302362PMC
December 2018
1 Read

Qualitative modelling of the interplay of inflammatory status and butyrate in the human gut: a hypotheses about robust bi-stability.

BMC Syst Biol 2018 Dec 17;12(1):144. Epub 2018 Dec 17.

School of Medical Health (MV), Örebro University, Örebro, 70182, Sweden.

Background: Gut microbiota interacts with the human gut in multiple ways. Microbiota composition is altered in inflamed gut conditions. Likewise, certain microbial fermentation products as well as the lipopolysaccharides of the outer membrane are examples of microbial products with opposing influences on gut epithelium inflammation status. Read More

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http://dx.doi.org/10.1186/s12918-018-0667-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6296070PMC
December 2018
1 Read

Reframing gene essentiality in terms of adaptive flexibility.

BMC Syst Biol 2018 Dec 17;12(1):143. Epub 2018 Dec 17.

Department of Bioengineering, University of California, San Diego, La Jolla, 92093, CA, USA.

Background: Essentiality assays are important tools commonly utilized for the discovery of gene functions. Growth/no growth screens of single gene knockout strain collections are also often utilized to test the predictive power of genome-scale models. False positive predictions occur when computational analysis predicts a gene to be non-essential, however experimental screens deem the gene to be essential. Read More

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http://dx.doi.org/10.1186/s12918-018-0653-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6296033PMC
December 2018
2 Reads

Co-expression of long non-coding RNAs and autism risk genes in the developing human brain.

BMC Syst Biol 2018 Dec 14;12(Suppl 7):91. Epub 2018 Dec 14.

Department of Genetics and Biochemistry, Clemson University, Clemson, SC, 29646, USA.

Background: Autism Spectrum Disorder (ASD) is the umbrella term for a group of neurodevelopmental disorders convergent on behavioral phenotypes. While many genes have been implicated in the disorder, the predominant focus of previous research has been on protein coding genes. This leaves a vast number of long non-coding RNAs (lncRNAs) not characterized for their role in the disorder although lncRNAs have been shown to play important roles in development and are highly represented in the brain. Read More

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http://dx.doi.org/10.1186/s12918-018-0639-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6293492PMC
December 2018
2 Reads

Systems biology analysis reveals new insights into invasive lung cancer.

BMC Syst Biol 2018 Dec 14;12(Suppl 7):117. Epub 2018 Dec 14.

MidSouth Bioinformatics Center and Joint Bioinformatics Ph.D. Program, University of Arkansas at Little Rock and University of Arkansas for Medical Sciences, 2801 S. University Avenue, Little Rock, AR, 72204, USA.

Background: Adenocarcinoma in situ (AIS) is a pre-invasive lesion in the lung and a subtype of lung adenocarcinoma. The patients with AIS can be cured by resecting the lesion completely. In contrast, the patients with invasive lung adenocarcinoma have very poor 5-year survival rate. Read More

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http://dx.doi.org/10.1186/s12918-018-0637-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6293490PMC
December 2018
2 Reads
2.435 Impact Factor

A hotspots analysis-relation discovery representation model for revealing diabetes mellitus and obesity.

BMC Syst Biol 2018 Dec 14;12(Suppl 7):116. Epub 2018 Dec 14.

Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, 130012, China.

Background: Nowadays, because of the huge economic burden on society causing by obesity and diabetes, they turn into the most serious public health challenges in the world. To reveal the close and complex relationships between diabetes, obesity and other diseases, search the effective treatment for them, a novel model named as representative latent Dirichlet allocation (RLDA) topic model is presented.

Results: RLDA was applied to a corpus of more than 337,000 literatures of diabetes and obesity which were published from 2007 to 2016. Read More

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http://dx.doi.org/10.1186/s12918-018-0640-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6293500PMC
December 2018
2 Reads
2.435 Impact Factor

Comparative toxicogenomics of three insensitive munitions constituents 2,4-dinitroanisole, nitroguanidine and nitrotriazolone in the soil nematode Caenorhabditis elegans.

BMC Syst Biol 2018 Dec 14;12(Suppl 7):92. Epub 2018 Dec 14.

Environmental Laboratory, U.S. Army Engineer Research and Development Center, 3909 Halls Ferry Road, Vicksburg, MS, 39180, USA.

Background: Ecotoxicological studies on the insensitive munitions formulation IMX-101 and its components 2,4-dinitroanisole (DNAN), nitroguanidine (NQ) and nitrotriazolone (NTO) in various organisms showed that DNAN was the main contributor to the overall toxicity of IMX-101 and suggested that the three compounds acted independently. These results motivated this toxicogenomics study to discern toxicological mechanisms for these compounds at the molecular level.

Methods: Here we used the soil nematode Caenorhabditis elegans, a well-characterized genomics model, as the test organism and a species-specific, transcriptome-wide 44 K-oligo probe microarray for gene expression analysis. Read More

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http://dx.doi.org/10.1186/s12918-018-0636-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6293504PMC
December 2018
3 Reads

MISC: missing imputation for single-cell RNA sequencing data.

BMC Syst Biol 2018 Dec 14;12(Suppl 7):114. Epub 2018 Dec 14.

Joint Bioinformatics Program, University of Arkansas Little Rock George Washington Donaghey College of Engineering & IT and University of Arkansas for Medical Sciences, Little Rock, AR, 72204, USA.

Background: Single-cell RNA sequencing (scRNA-seq) technology provides an effective way to study cell heterogeneity. However, due to the low capture efficiency and stochastic gene expression, scRNA-seq data often contains a high percentage of missing values. It has been showed that the missing rate can reach approximately 30% even after noise reduction. Read More

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http://dx.doi.org/10.1186/s12918-018-0638-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6293493PMC
December 2018
2 Reads

MICRAT: a novel algorithm for inferring gene regulatory networks using time series gene expression data.

BMC Syst Biol 2018 Dec 14;12(Suppl 7):115. Epub 2018 Dec 14.

School of Computing, University of Southern Mississippi, Hattiesburg, MS, 39406, USA.

Background: Reconstruction of gene regulatory networks (GRNs), also known as reverse engineering of GRNs, aims to infer the potential regulation relationships between genes. With the development of biotechnology, such as gene chip microarray and RNA-sequencing, the high-throughput data generated provide us with more opportunities to infer the gene-gene interaction relationships using gene expression data and hence understand the underlying mechanism of biological processes. Gene regulatory networks are known to exhibit a multiplicity of interaction mechanisms which include functional and non-functional, and linear and non-linear relationships. Read More

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https://bmcsystbiol.biomedcentral.com/articles/10.1186/s1291
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http://dx.doi.org/10.1186/s12918-018-0635-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6293491PMC
December 2018
13 Reads