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Abstract(s)
Currently, the molecular diagnosis is based on the quantification of RNA, proteins and metabolites because they present changes in their quantity related to clinical situations. The same molecules are not generally suitable for early diagnosis or to follow clinical evolution, making necessary strategies to evaluate the complete molecular scenario. There are already experimental strategies that allow the determination of total protein profiles from saliva samples (the SalivaPrint). The goal of this work is to identify a profile of saliva proteins (similar to a fingerprint) and, using computational methods, identify how this profiles changes with age and gender. So far it has been possible to collect 79 samples as well as the metadata associated with each sample using an electronic questionnaire developed by us. A total protein profile was obtained and their association with gender was verified using statistical methods. Currently we are developing the Python scripts for automatic data acquiring and normalization. Total protein profiles annotation on a database (SalivaPrintDB) and their integration with the factors that affects them using machine learning strategies can empower the use of the approach proposed on this work as a tool for monitoring the individual's health status.
Description
Keywords
Health diagnostic Machine learning strategies Saliva diagnostic Saliva protein profile