PURPOSE This study aims to review and evaluate available informatics platforms for research and management purposes of Lynch syndrome (LS) to identify gaps and needs for future development. METHODS LS informatics tools were identified through literature search in four publication databases (1 and Scopus). First, the LS and functional elements of every informatics tools for LS were introduced. Then, current existing LS informatics tools were reviewed and explained. RESULTS A detailed review of implemented studies shows that many types of informatics platforms are available for LS management (ie, prediction model, clinical decision support system, database website, and other tools for research and management purposes of LS). Moreover, several dimensions of existing LS informatics tools were discussed and features and positive findings were reported. CONCLUSION Reviewing the literature reveals that several LS informatics tools were focused on gene-specific estimate, cancer risk prediction, identifying/screening patients, supporting personalized care of individuals with LS, and storing mismatch repair mutations information. Nevertheless, these platforms do not fully cover the care and research purposes. For instance, future developments of LS tools require more attention to dynamic knowledgebase, extra-colonic lynch–related cancers on the basis of precision medicine, variants of unknown significance, and support from diagnosis to surveillance for patient follow-up. Insights and recommendations provided in this study could help researchers and developers to meet the existing challenges in future developments.
Introduction: Growing evidence has shown that some overweight factors could be implicated in tumor genesis, higher recurrence and mortality. In addition, association of various overweight factors and breast cancer has not been extensively explored. The goal of this research was to explore and evaluate the association of various overweight/obesity factors and breast cancer, based on obesity breast cancer data set.Material and Methods: Several studies show that a significantly stronger association is obvious between overweight and higher breast cancer incidence, but the role of some overweight factors such as BMI, insulin-resistance, Homeostasis Model Assessment (HOMA), Leptin, adiponectin, glucose and MCP.1 is still debatable, So for experiment of research work several clinical and biochemical overweight factors, including age, Body Mass Index (BMI), Glucose, Insulin, Homeostatic Model Assessment (HOMA), Leptin, Adiponectin, Resistin and Monocyte chemo attractant protein-1(MCP-1) were analyzed. Data mining algorithms including k-means, Apriori, Hierarchical clustering algorithm (HCM) were applied using orange version 3.22 as an open source data mining tool.Results: The Apriori algorithm generated a list of frequent item sets and some strong rules from dataset and found that insulin, HOMA and leptin are two items often simultaneously were seen for BC patients that leads to cancer progression. K-means algorithm applied and it divided samples on three clusters and its results showed that the pair of andlt;Adiponectin, MCP.1andgt; has the highest effect on seperation of clusters. In addition HCM was carried out and classified BC patients into 1-32 clusters to So this research apply HCM algorithm. We carried out hierarchical clustering with average linkage without purning and classified BC patients into 1–32 clusters in order to identify BC patients with similar charestrictics.Conclusion: These finding provide the employed algorithms in this study can be helpful to our aim.
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