CRISPR/Cas9 is a promising tool for genome engineering in bacteria, but it's limited by inconsistent accuracy. Though some studies have been conducted to understand why this inconsistency occurs, many important biological features have not been explored. Moreover, computer based attempts to predict accuracy have suffered from these knowledge gaps. This is due mainly to the fact that the mathematical equations that these predictions are based on, do not take these biological features into account.
The explosion of data from personal phones, apps, and sensors have enabled powerful machine learning algorithms to help computers identify, categorize, and evaluate information without the help of humans. However, teaching computers how to identify, categorize, and evaluate information usually requires feeding the computers a lot of data pre-labelled by humans. The pre-labelling process is costly and time consuming. The goal of this project is to develop new algorithms to teach computers to identify, categorize, and evaluate information with less pre-labelled data.
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