| Summary: | Shotgun Proteomics is the leading technique for protein identification in complexmixtures. However, it produces a large amount of data which results in aextremely high computational cost for identifying the protein. Retention time(RT) is an important factor to be used to enhance the efficiency of protein identification.By predicting the retention time successfully, we could decrease thecomputational cost dramatically. This thesis uses a machine learning method,Gaussian Processes, to predict the retention time of a set of peptide in hand.We also implement a feature extraction method called Bag-of-Words to generatethe features for training the model. In addition, we also investigate theeffect of different types of optimization methods to the model’s parameters.The results show comparable precision of the prediction and relatively lowtime cost when comparing with the state-of-art prediction model.
|
|---|
