SOMPNN is a novel model for prediction of TMH that features by minimal parameter assumptions requirement and high computational efficiency. In the SOMPNN model, a self-organizing map (SOM) is used to adaptively learn the helices distribution knowledge hidden in the training data, and then a probabilistic neural network (PNN) is adopted to predict TMH segments based on the knowledge learned by SOM.
Computational Systems Biology Group, Shanghai Jiao Tong University
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Amino Acids. 2012 Jun;42(6):2195-205. doi: 10.1007/s00726-011-0959-2. Epub 2011 Jun 22.
SOMPNN: an efficient non-parametric model for predicting transmembrane helices.
Yu DJ1, Shen HB, Yang JY.