The goal of the Bioinformatics core is to provide expert computational analysis of molecularprofiling (expression and NMR) data in order to determine the molecular signatures predictive of diagnosisand outcome in Soft Tissue Sarcoma (STS). The core will not only provide computational/statistical analysisbut will build and maintain the data infrastructure needed by the various projects, whose work will lead to thedefinition of new marker sets, mechanistic hypotheses and possible identification of new drug targets. Thecore will also facilitate integration of research in the projects by enabling the sharing of the various datasetscollected. Specifically, it will perform the following tasks. 1) Statistical analysis of microarray expression dataincluding: error analysis, normalization, unsupervised clustering analysis, differential gene analysis andmultivariate class prediction. These methods will be applied in the following cases: a. Cluster and differentialgene expression analysis of sarcoma subtypes to classify sarcoma tissue samples based on their similarityin gene expression, to identify potential diagnostic/prognostic markers and to determine the relevant genesfor subsequent pathway analysis; b. Expression analysis of SYT-SSX regulated genes along with theanalysis of the respective promoters and expression based survival prediction of Synovial Sarcomas; c.Supervised learning analysis of clinical variables such as distant recurrence and survival, the object being togenerate expression based predictors. 2) Statistical analysis of NMR data obtained from Liposarcomasamples, including prediction of Liposarcoma subtypes and sample clinical variables (outcome/survival)using supervised machine learning techniques. Development of integrated (microarray/NMR) molecularprofiling analysis to develop prognostic marker sets. 3) Pathway analysis of molecular profiling data.Integrating data from (1) and (2) with pathway data to: a. Elucidate the biological basis of tumor subtypes; b.Find new potential drug targets. 4) To develop an online repository of microarray expression data along witha database of annotation information and clinical data. Integrate and make available the large collection ofdatasets to be collected. 5) To develop a patient data tracking system for multi-institutional clinical trials.
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