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Figure 3. A protein interaction network of top 20 highly ranked breast cancer disease proteins (possible drug targets).

Figure 3 A protein interaction network of top 20 highly ranked breast cancer disease proteins (possible drug targets).

Related Figures (5)

Figure 1. A framework for visualizing systems biology data for drug discovery applications.
Figure 1. A framework for visualizing systems biology data for drug discovery applications.
Figure 2. A heatmap visualization of a breast cancer protein adjacency matrix, after ACO ranking/re-ordering of proteins in the protein interaction networks. A. Result for the protein interaction network constructed by including only protein interactions with a confidence score > 0.50 from the HAPPI database. B. Result for the protein interaction network constructed by including only protein interactions with a confidence score > 0.99 from the HAPPI database. ACO: Ant colony ontimization: HAPPI: Human annotated and predicted protein interaction.
Figure 2. A heatmap visualization of a breast cancer protein adjacency matrix, after ACO ranking/re-ordering of proteins in the protein interaction networks. A. Result for the protein interaction network constructed by including only protein interactions with a confidence score > 0.50 from the HAPPI database. B. Result for the protein interaction network constructed by including only protein interactions with a confidence score > 0.99 from the HAPPI database. ACO: Ant colony ontimization: HAPPI: Human annotated and predicted protein interaction.
Figure 4. A poly-pharmaocological network of breast cancer drugs, known drug targets and breast cancer-related genes. analysis to reveal weak yet significant patterns hidden in func- tional genomics results. Essential to the success of these tools is the layout of multi-scale biological networks and pathways, which integrate disparate measurements of different biological entities into a coherent qualitative model. Although such models are static and semi-quantitative, ongoing development of increasingly sophisticated visual analytics tools will enable full-fledged simulation of drug perturbation of a biological system in the future. quality of drug targets and lead compounds. Here, we offer five suggestions to our readers interested in choosing and applying these tools to their research.
Figure 4. A poly-pharmaocological network of breast cancer drugs, known drug targets and breast cancer-related genes. analysis to reveal weak yet significant patterns hidden in func- tional genomics results. Essential to the success of these tools is the layout of multi-scale biological networks and pathways, which integrate disparate measurements of different biological entities into a coherent qualitative model. Although such models are static and semi-quantitative, ongoing development of increasingly sophisticated visual analytics tools will enable full-fledged simulation of drug perturbation of a biological system in the future. quality of drug targets and lead compounds. Here, we offer five suggestions to our readers interested in choosing and applying these tools to their research.
Figure 5. Breast cancer pharmacological networks. A. A breast cancer protein-protein interaction network. The network is constructed by using first-neighbor expansion methods [65] against the HAPPI database [14]. Only protein interactions with a confidence score >0.75 from the HAPPI database are retained. Seed genes are constructed from the OMIM morbid map using the MeSH query term ‘breast cancer’. B. A breast cancer candidate drug-drug association network. All data in this network are based on a filtered subset of breast cancer connectivity map (‘C-Map’). The edge width is drawn in proportion to the number of common target proteins shared between two drugs and the node size is in proportion to the number of possible target proteins the specific drug affects. C. A breast cancer drug target protein-protein association network. The edges and nodes are colored as targets of different drug categories.
Figure 5. Breast cancer pharmacological networks. A. A breast cancer protein-protein interaction network. The network is constructed by using first-neighbor expansion methods [65] against the HAPPI database [14]. Only protein interactions with a confidence score >0.75 from the HAPPI database are retained. Seed genes are constructed from the OMIM morbid map using the MeSH query term ‘breast cancer’. B. A breast cancer candidate drug-drug association network. All data in this network are based on a filtered subset of breast cancer connectivity map (‘C-Map’). The edge width is drawn in proportion to the number of common target proteins shared between two drugs and the node size is in proportion to the number of possible target proteins the specific drug affects. C. A breast cancer drug target protein-protein association network. The edges and nodes are colored as targets of different drug categories.
Related topics:
BioinformaticsSystems BiologyDrug DiscoverySystem BiologyDrug Targeting
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