Incorporating prior knowledge of protein localization in a neural network for protein location prediction
I. Hoogenboom (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Marcel J T Reinders – Mentor (TU Delft - Pattern Recognition and Bioinformatics)
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Abstract
Determining protein subcellular location is important for understanding cellular
functions and biological processes of underlying diseases. High throughput fluorescence images can be used in combination with convolutional neural networks to predict this location. In this work we propose a hierarchical model which uses prior knowledge of proteins to divide the samples in general groups before predicting the subcellular location. Results show mixed results with significant improvements for some labels and a decline in results for others.