scholarly article | Q13442814 |
P50 | author | Ulf Hedin | Q37374860 |
Asa M Wheelock | Q56426903 | ||
Goran Hansson | Q98467791 | ||
P2093 | author name string | Susumu Goto | |
Jesper Z Haeggström | |||
Craig E Wheelock | |||
Anders Gabrielsen | |||
Gabrielle Paulsson-Berne | |||
Diego Diez | |||
P2860 | cites work | A dynamic network approach for the study of human phenotypes | Q21145365 |
An automated method for finding molecular complexes in large protein interaction networks | Q21284295 | ||
Statistical mechanics of complex networks | Q21563680 | ||
The Spread of Obesity in a Large Social Network over 32 Years | Q22248107 | ||
Gene ontology: tool for the unification of biology | Q23781406 | ||
Severe hypertriglyceridemia in human APOC1 transgenic mice is caused by apoC-I-induced inhibition of LPL | Q24319030 | ||
KEGG: kyoto encyclopedia of genes and genomes | Q24515297 | ||
Cytoscape: a software environment for integrated models of biomolecular interaction networks | Q24515682 | ||
Community structure in social and biological networks | Q24534298 | ||
Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls | Q24550675 | ||
ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks | Q24570122 | ||
Common variants at 30 loci contribute to polygenic dyslipidemia | Q24598765 | ||
The architecture of complex weighted networks | Q24631525 | ||
A core gut microbiome in obese and lean twins | Q24649648 | ||
In the absence of the low density lipoprotein receptor, human apolipoprotein C1 overexpression in transgenic mice inhibits the hepatic uptake of very low density lipoproteins via a receptor-associated protein-sensitive pathway | Q37361432 | ||
Systems biology approaches and pathway tools for investigating cardiovascular disease | Q37492364 | ||
Network analysis of human in-stent restenosis | Q38401409 | ||
GOlorize: a Cytoscape plug-in for network visualization with Gene Ontology-based layout and coloring | Q38517296 | ||
Pathway analysis of coronary atherosclerosis | Q38520286 | ||
Signature patterns of gene expression in mouse atherosclerosis and their correlation to human coronary disease | Q38520485 | ||
S1P(5) is required for sphingosine 1-phosphate-induced autophagy in human prostate cancer PC-3 cells. | Q39846364 | ||
Systems biology and new technologies enable predictive and preventative medicine. | Q40473946 | ||
Flux coupling analysis of genome-scale metabolic network reconstructions. | Q40497938 | ||
Bayesian inference of protein-protein interactions from biological literature | Q42536511 | ||
Understanding modularity in molecular networks requires dynamics | Q43030828 | ||
A Genomic-Systems Biology Map for Cardiovascular Function | Q43807713 | ||
Apolipoprotein C-I is crucially involved in lipopolysaccharide-induced atherosclerosis development in apolipoprotein E-knockout mice. | Q43976830 | ||
Postprandial enrichment of remnant lipoproteins with apoC-I in healthy normolipidemic men with early asymptomatic atherosclerosis | Q44136772 | ||
Apolipoprotein C-I induces apoptosis in human aortic smooth muscle cells via recruiting neutral sphingomyelinase | Q44690061 | ||
Revisiting the foundations of network analysis | Q46086879 | ||
The apolipoprotein CI content of triglyceride-rich lipoproteins independently predicts early atherosclerosis in healthy middle-aged men. | Q46420643 | ||
The ApoC-I content of VLDL particles is associated with plaque size in persons with carotid atherosclerosis | Q46569874 | ||
Endogenous apoC-I increases hyperlipidemia in apoE-knockout mice by stimulating VLDL production and inhibiting LPL. | Q46990753 | ||
An algorithm for modularity analysis of directed and weighted biological networks based on edge-betweenness centrality | Q48426812 | ||
An architecture for biological information extraction and representation | Q48507596 | ||
MySpace and Facebook: applying the uses and gratifications theory to exploring friend-networking sites | Q51478221 | ||
Computing topological parameters of biological networks | Q51900389 | ||
Systems biology. Less is more in modeling large genetic networks | Q51961810 | ||
Self-similarity of complex networks | Q56041826 | ||
Evolution of Networks | Q56069103 | ||
KEGG for linking genomes to life and the environment | Q24650347 | ||
Systems biology and the virtual physiological human | Q24653792 | ||
Genomewide association analysis of coronary artery disease | Q24658344 | ||
The human disease network | Q24678240 | ||
Tough mining: the challenges of searching the scientific literature | Q24806856 | ||
Are scale-free networks robust to measurement errors? | Q24810672 | ||
Mining literature for a comprehensive pathway analysis: a case study for retrieval of homocysteine related genes for genetic and epigenetic studies | Q25257578 | ||
Emergence of Scaling in Random Networks | Q27037290 | ||
Resilience of the Internet to Random Breakdowns | Q27347883 | ||
Scale-Free Networks Are Ultrasmall | Q27450885 | ||
Network biology: understanding the cell's functional organization | Q27861027 | ||
Collective dynamics of 'small-world' networks | Q27861064 | ||
Targeting RAS signalling pathways in cancer therapy | Q28201363 | ||
Mammalian MAP kinase signalling cascades | Q28204140 | ||
Exploring complex networks | Q28205472 | ||
Visualizing networks | Q28260784 | ||
Integration of biological networks and gene expression data using Cytoscape | Q28681323 | ||
Network integration and graph analysis in mammalian molecular systems biology | Q28751567 | ||
Pathway databases and tools for their exploitation: benefits, current limitations and challenges | Q28751984 | ||
Text mining for biology - the way forward: opinions from leading scientists | Q28756933 | ||
Inflammation, atherosclerosis, and coronary artery disease | Q29547232 | ||
Lethality and centrality in protein networks | Q29547267 | ||
Error and attack tolerance of complex networks | Q29547268 | ||
Network motifs: simple building blocks of complex networks | Q29547340 | ||
Network motifs in the transcriptional regulation network of Escherichia coli | Q29547342 | ||
affy--analysis of Affymetrix GeneChip data at the probe level | Q29547353 | ||
BiNGO: a Cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks | Q29547427 | ||
The large-scale organization of metabolic networks | Q29547498 | ||
Systems biology: a brief overview | Q29547514 | ||
Network motifs: theory and experimental approaches | Q29615325 | ||
Computational systems biology | Q29616655 | ||
A network-based analysis of systemic inflammation in humans | Q29617040 | ||
Hierarchical organization of modularity in metabolic networks | Q29618451 | ||
Hierarchical structure and the prediction of missing links in networks | Q30050098 | ||
Bioconductor: an open source framework for bioinformatics and computational biology | Q36580879 | ||
Thematic review series: systems biology approaches to metabolic and cardiovascular disorders. Reverse engineering gene networks to identify key drivers of complex disease phenotypes | Q36609005 | ||
Thematic review series: systems biology approaches to metabolic and cardiovascular disorders. Multi-organ whole-genome measurements and reverse engineering to uncover gene networks underlying complex traits | Q36672069 | ||
Network-based global inference of human disease genes | Q36713985 | ||
Graph theory and networks in Biology. | Q36794657 | ||
Biological impacts and context of network theory | Q36798117 | ||
Recent progress on the analysis of power-law features in complex cellular networks | Q36943324 | ||
Understanding biological functions through molecular networks | Q37070273 | ||
WiFi networks and malware epidemiology | Q37083645 | ||
Proteins as networks: usefulness of graph theory in protein science | Q37108560 | ||
Protein networks in disease | Q37125547 | ||
Why study gene-environment interactions? | Q37128490 | ||
Gene-environment interactions and susceptibility to metabolic syndrome and other chronic diseases | Q37173088 | ||
Modelling and analysis of gene regulatory networks | Q37271452 | ||
Modularized learning of genetic interaction networks from biological annotations and mRNA expression data | Q30986009 | ||
The model organism as a system: integrating 'omics' data sets | Q31032864 | ||
Resources for integrative systems biology: from data through databases to networks and dynamic system models | Q31066861 | ||
Prediction of cardiac transcription networks based on molecular data and complex clinical phenotypes | Q31156132 | ||
The limits of reductionism in medicine: could systems biology offer an alternative? | Q33242695 | ||
The clinical applications of a systems approach | Q33242858 | ||
Integrating genetic and network analysis to characterize genes related to mouse weight | Q33255339 | ||
Cerebral: a Cytoscape plugin for layout of and interaction with biological networks using subcellular localization annotation. | Q33274604 | ||
Graphs in molecular biology | Q33300808 | ||
Towards ontology-driven navigation of the lipid bibliosphere | Q33322217 | ||
Uncovering a macrophage transcriptional program by integrating evidence from motif scanning and expression dynamics | Q33325887 | ||
Transcriptional profiling uncovers a network of cholesterol-responsive atherosclerosis target genes | Q33325906 | ||
OmicsViz: Cytoscape plug-in for visualizing omics data across species | Q33367888 | ||
Reconstruction and functional analysis of altered molecular pathways in human atherosclerotic arteries | Q33398803 | ||
Layered functional network analysis of gene expression in human heart failure | Q33486544 | ||
Validation of candidate causal genes for obesity that affect shared metabolic pathways and networks | Q33725093 | ||
Regulation of the L-arabinose operon of Escherichia coli | Q33927032 | ||
Elucidation and structural analysis of conserved pools for genome-scale metabolic reconstructions | Q34188505 | ||
Apolipoproteins C-I and C-III as important modulators of lipoprotein metabolism | Q34251160 | ||
Systems biology, proteomics, and the future of health care: toward predictive, preventative, and personalized medicine | Q34316883 | ||
Review of uses of network and graph theory concepts within proteomics | Q34427728 | ||
The challenges of modeling mammalian biocomplexity | Q34551720 | ||
Multiple high-throughput analyses monitor the response of E. coli to perturbations | Q34578020 | ||
Genetics. Getting closer to the whole picture. | Q34623402 | ||
Network analysis of FDA approved drugs and their targets | Q34630429 | ||
KEGGgraph: a graph approach to KEGG PATHWAY in R and bioconductor | Q34967352 | ||
Systems biology: Untangling the protein web. | Q34992536 | ||
Predicting the behavior of techno-social systems | Q34993803 | ||
Identification of inflammatory gene modules based on variations of human endothelial cell responses to oxidized lipids | Q35036420 | ||
Combinatorial transcriptional control of the lactose operon of Escherichia coli | Q35749641 | ||
Transcription factor modularity in a gene-centered C. elegans core neuronal protein-DNA interaction network | Q35854821 | ||
Thematic review series: The pathogenesis of atherosclerosis. Toward a biological network for atherosclerosis | Q35855112 | ||
Scale-freeness and biological networks | Q36208392 | ||
Glucose transporter 4: cycling, compartments and controversies | Q36327829 | ||
P433 | issue | 2 | |
P921 | main subject | cardiovascular disease | Q389735 |
P304 | page(s) | 289-304 | |
P577 | publication date | 2009-10-16 | |
P13046 | publication type of scholarly work | review article | Q7318358 |
P1433 | published in | Molecular BioSystems | Q3319467 |
P1476 | title | The use of network analyses for elucidating mechanisms in cardiovascular disease | |
P478 | volume | 6 |
Q89686770 | A comparative pharmacogenomic analysis of three classic TCM prescriptions for coronary heart disease based on molecular network modeling |
Q34051850 | A computable cellular stress network model for non-diseased pulmonary and cardiovascular tissue |
Q28538646 | A network pharmacology study of Chinese medicine QiShenYiQi to reveal its underlying multi-compound, multi-target, multi-pathway mode of action |
Q84073956 | A novel integrative network approach to understand the interplay between cardiovascular disease and other complex disorders |
Q45397586 | Atherosclerosis Drug Development in Jeopardy: The Need for Predictive Biomarkers of Treatment Response |
Q34507146 | Biomarkers of coronary artery disease: the promise of the transcriptome |
Q37784556 | Cause-effect relationships in medicine: a protein network perspective |
Q42018263 | Construction of competitive endogenous RNA network reveals regulatory role of long non-coding RNAs in type 2 diabetes mellitus |
Q34098195 | Correlation network analysis applied to complex biofilm communities |
Q37989807 | Deciphering the molecular basis of human cardiovascular disease through network biology |
Q54750996 | Effect of supplementation with an 80:20cis9,trans11 conjugated linoleic acid blend on the human platelet proteome |
Q38148743 | High-throughput methods for combinatorial drug discovery |
Q35177141 | How do high glycemic load diets influence coronary heart disease? |
Q54977757 | Identification of candidate biomarkers and pathways associated with SCLC by bioinformatics analysis. |
Q44795690 | Inferring differences in the distribution of reaction rates across conditions |
Q34037720 | Integrating biological pathways and genomic profiles with ChiBE 2. |
Q41217019 | Integrative Bioinformatics Analysis of Proteins Associated with the Cardiorenal Syndrome |
Q50068180 | Integrative gene ontology and network analysis of coronary artery disease associated genes suggests potential role of ErbB pathway gene EGFR. |
Q112293258 | Integrative multiomics analysis of human atherosclerosis reveals a serum response factor-driven network associated with intraplaque hemorrhage |
Q35817496 | Integrative network analysis for survival-associated gene-gene interactions across multiple genomic profiles in ovarian cancer. |
Q35092859 | Investigating the utility of clinical outcome-guided mutual information network in network-based Cox regression. |
Q28602218 | Linking Genes to Cardiovascular Diseases: Gene Action and Gene-Environment Interactions |
Q34309219 | Network analysis identifies a putative role for the PPAR and type 1 interferon pathways in glucocorticoid actions in asthmatics |
Q51695430 | Network analysis in the investigation of chronic respiratory diseases. From basics to application |
Q35097279 | Network biology in medicine and beyond |
Q35868607 | Network-based gene expression biomarkers for cold and heat patterns of rheumatoid arthritis in traditional chinese medicine |
Q58615269 | OnPLS-Based Multi-Block Data Integration: A Multivariate Approach to Interrogating Biological Interactions in Asthma |
Q35684873 | Protein alterations in women with chronic widespread pain--An explorative proteomic study of the trapezius muscle. |
Q34112124 | Proteome expression and carbonylation changes during Trypanosoma cruzi infection and Chagas disease in rats. |
Q37989491 | Regulation of Gene Expression in Atherosclerosis: Insights from Microarray Studies in Monocytes/Macrophages |
Q30433543 | Sudden cardiac death prediction and prevention: report from a National Heart, Lung, and Blood Institute and Heart Rhythm Society Workshop |
Q37687978 | Survey of network-based approaches to research of cardiovascular diseases |
Q35091855 | Systematic approaches towards the development of host-directed antiviral therapeutics |
Q38474337 | Systems genetics of obesity in an F2 pig model by genome-wide association, genetic network, and pathway analyses |
Q27026041 | The Emerging Paradigm of Network Medicine in the Study of Human Disease |
Q35315854 | The mechanism by which moderate alcohol consumption influences coronary heart disease |
Q37928825 | The utility of collaborative biobanks for cardiovascular research. |
Q42774938 | Transcriptome profiling reveals that the SM22α-regulated molecular pathways contribute to vascular pathology |
Q30664694 | Trials and tribulations of 'omics data analysis: assessing quality of SIMCA-based multivariate models using examples from pulmonary medicine |
Q27334820 | Using regulatory and epistatic networks to extend the findings of a genome scan: identifying the gene drivers of pigmentation in merino sheep |
Q30836881 | Weighted Interaction SNP Hub (WISH) network method for building genetic networks for complex diseases and traits using whole genome genotype data |
Q56888192 | Whole Genome Network Analysis of Ion Channels and Connexins in Myocardial Infarction |
Search more.