SOMPNN: An Efficient Non-Parametric Model for Predicting Transmembrane Helices
Accurately predicting the transmembrane helices (TMH) in a helical membrane protein is an
important but challenging task. Recent researches have demonstrated that statistics-based methods
are promising routes to improve the TMH prediction accuracy. However, most of existing TMH predictors
are parametric models and they have to make assumptions of several or even hundreds of adjustable parameters
based on the underlying probability distribution, which is difficult when no a-priori knowledge is available.
Besides the performances of these parametric predictors significantly depend on the estimated parameters, some
of them need to exploit the entire training dataset in the prediction stage, which will lead to low prediction
efficiency and this problem will become even worse when dealing with large-scale dataset. In this paper, we propose
a novel SOMPNN 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. Experimental results on two benchmark datasets show that the proposed
SOMPNN outperforms existing popular TMH predictors and is promising to be extended to deal with other complicated biological problems.
Code and Dataset
Click here to download the datasets and source codes of SOMPNN.
MemBrain: Improving the Accuracy of Predicting Transmembrane Helices.
Dong-Jun Yu, Hong-Bin Shen, Jing-Yu Yang, SOMPNN: An Efficient Non-Parametric Model for Predicting Transmembrane Helices, Amino Acids, 2012, 42: 2195-2205.