Abstract
early diagnosis of cancer is crucial to improving the survival rate and to prolong the lives of patients. With the large amounts of medical data available in the medical field, applying data mining tools and an efficient prediction methodology to diagnose diseases can lead to useful knowledge to support medical professionals in saving lives. This paper explores genomic interactions networks, investigating protein-protein interaction networks to predict cancer related proteins using sequential minimal Optimization (SMO) for training Support Vector Machine (SVM). The WEKA software was utilized as the data mining tool, which is an open source collection of machine learning algorithms. The provided data set was studied and analyzed in order to build a useful and reliable model to predict cancer and non-cancer related proteins. Keywordsmining, Support Vector Machine (SVM), Protein-Protein Interaction (PPI), Sequential Minimal Optimization (SMO)
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