Abstract
Living systems can be envisioned as a beautiful fabric with weaves of different biological networks such as genetic, protein, transcription factors and metabolic interactions. The weave pattern or the network architecture is a precise predictor of healthy or diseased state of the organism. Network-based approach gives insight into pathogenesis pathway which leads to drug discovery process. It helps researchers and clinicians in grouping together the proteins that interact in functional complexes and pathways thus exploring disease network nodes as potential target for drug discovery. Biological data are often structured in the form of complex interconnected networks such as protein interaction and metabolic networks. Visualization tools help in the visualization of various interactome data. Sheer size, complexity and dynamic nature of networks, and the algorithm responsible for visualization are the main challenges of biological network visualization. Visualization tools are based on certain graph theories and algorithm for visualization. In the present work, directed and undirected graphs are used in the visualization of protein interactions data. To visualize time course behavior of genes, there is need to combine these two graph theories which can interpret biological phenomenon in better way. Further, force-directed algorithm is used for the visualization of protein interaction data. It helps in integrating interactome data and provides better visualization. To overcome this aspect, a holistic approach is required.
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Rameshwari, R., Chapadgaonkar, S.S. & Prasad, T.V. A Robust Algorithm for Visualization of Protein Interaction Network. Iran J Sci Technol Trans Sci 43, 1411–1416 (2019). https://doi.org/10.1007/s40995-018-0632-7
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DOI: https://doi.org/10.1007/s40995-018-0632-7