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A Comparative Study on Filtering Protein Secondary Structure Prediction
May-June 2012 (vol. 9 no. 3)
pp. 731-739
G. Christodoulou, Dept. of Comput. Sci., Univ. of Cyprus, Nicosia, Cyprus
V. J. Promponas, Dept. of Biol. Sci., Univ. of Cyprus, Nicosia, Cyprus
M. Agathocleous, Dept. of Comput. Sci., Univ. of Cyprus, Nicosia, Cyprus
P. Kountouris, Dept. of Comput. Sci., Univ. of Cyprus, Nicosia, Cyprus
S. Hadjicostas, Dept. of Comput. Sci., Univ. of Cyprus, Nicosia, Cyprus
V. Vassiliades, Dept. of Comput. Sci., Univ. of Cyprus, Nicosia, Cyprus
C. Christodoulou, Dept. of Comput. Sci., Univ. of Cyprus, Nicosia, Cyprus
Filtering of Protein Secondary Structure Prediction (PSSP) aims to provide physicochemically realistic results, while it usually improves the predictive performance. We performed a comparative study on this challenging problem, utilizing both machine learning techniques and empirical rules and we found that combinations of the two lead to the highest improvement.

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Index Terms:
proteins,bioinformatics,filtering theory,learning (artificial intelligence),molecular biophysics,molecular configurations,empirical rule,protein secondary structure prediction filtering,machine learning technique,Accuracy,Proteins,Filtering,Machine learning algorithms,Logistics,Training,Machine learning,bidirectional recurrent neural networks.,Protein secondary structure prediction,filtering,machine learning,structural bioinformatics
G. Christodoulou, V. J. Promponas, M. Agathocleous, P. Kountouris, S. Hadjicostas, V. Vassiliades, C. Christodoulou, "A Comparative Study on Filtering Protein Secondary Structure Prediction," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 9, no. 3, pp. 731-739, May-June 2012, doi:10.1109/TCBB.2012.22
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