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Paper   IPM / Biological / 13239
School of Biological Sciences
  Title:   Protein secondary structure prediction using three neural networks and a segmental semi Markov model
1.  S. A. Malekpour.
2.  S. Naghizadeh .
3.  H. Pezeshk.
4.  M. Sadeghi.
5.  C. Eslahchi.
  Status:   Published
  Journal: Mathematical Biosciences
  No.:  2
  Vol.:  217
  Year:  2009
  Pages:   145-150
  Supported by:  IPM
Prediction of protein secondary structure is an important step towards elucidating its three dimensional structure and its function. This is a challenging problem in bioinformatics. Segmental semi Markov models (SSMMs) are one of the best studied methods in this field. However, incorporating evolutionary information to these methods is somewhat difficult. On the other hand, the systems of multiple neural networks (NNs) are powerful tools for multi-class pattern classification which can easily be applied to take these sorts of information into account.
To overcome the weakness of SSMMs in prediction, in this work we consider a SSMM as a decision function on outputs of three NNs that uses multiple sequence alignment profiles. We consider four types of observations for outputs of a neural network. Then profile table related to each sequence is reduced to a sequence of four observations. In order to predict secondary structure of each amino acid we need to consider a decision function. We use an SSMM on outputs of three neural networks. The proposed SSMM has discriminative power and weights over different dependency models for outputs of neural networks. The results show that the accuracy of our model in predictions, particularly for strands, is considerably increased.

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