Abstract
In this paper, we identify the mutated signal transduction pathways in a breast cancerous cell. A simulated model is developed for these pathways. Some of the pathways like PKB (protein kinase B), MAPK (mitogen-activated protein kinase), MTOR (mammalian target of rapamycin), Fas ligand (Type-II transmembrane protein), Notch (single-pass transmembrane receptor), SHH (Sonic Hedgehog), Tnf (tumor necrosis factor), Wnt (wingless/integrated) pathways are simulated. For computational modeling of signal transduction pathways, SBML (Systems Biology Markup Language) is used. Programming is done in SBML and executed in Cell Designer. These simulated models are in the form of XML files. We extracted the information in the XML files into tables, and we applied information processing techniques to it like information cleaning, information integration, information transformation, information reduction and information discretization. K-means clustering algorithm is applied on the extracted data set. Python code is written to implement K-means clustering algorithm. Two clusters are formed after running the code on the data set, one representing benign tumors and the other representing malignant tumors.
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Golagani, P.P., Beebi, S.K., Mahalakshmi, T.S. (2020). Using K-means Clustering Algorithm with Python Programming for Predicting Breast Cancer. In: Fiaidhi, J., Bhattacharyya, D., Rao, N. (eds) Smart Technologies in Data Science and Communication. Lecture Notes in Networks and Systems, vol 105. Springer, Singapore. https://doi.org/10.1007/978-981-15-2407-3_21
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DOI: https://doi.org/10.1007/978-981-15-2407-3_21
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