1. Cuff JA, Barton GJ. Application of multiple sequence alignment profiles to improve protein secondary structure prediction. Proteins 2000; 40:502-511. [Abstract]

2. Manesh HN, Sadeghi M, Arab S, Movahedi AM. Prediction of protein surface accessibility with information theory. Proteins 2001;42:452-459. [Abstract]

3. Ahmad S, Gromiha MM. NETASA: Neural network based prediction of solvent accessibility. Bioinformatics 2002; 18:819-824. [Abstract]

4. Kabsch W, Sander C. Dictionary of protein secondary structure: Pattern recognition of hydrogen bond and geometrical features. Biopolymer 1983; 22:2577-2637. [Abstract]

5. Rost B, Sander C. Conservation and prediction of solvent accessibility in protein families. Proteins 1994; 20:216-226. [Abstract]

6. Chan HS and Dill KA. Origins of structure in globular proteins. Proc Natl. Acad. Sci 1990; 87:6388-6392. [Abstract]

7. Li X, and Pan XM. New methods for accurate prediction of solvent accessibility from protein sequence. Proteins 2001; 42:1-5. [Abstract]

8. Thompson MJ, Goldstein RA. Predicting solvent accessibility: higher accuracy using Bayesian statistics and optimized residue substitution classes. Proteins 1996; 25:38-47. [Abstract]

9. Yuan Z, Burrage K, Mattick J. Prediction of protein solvent accessibility using support vector machines. Proteins 2002; 48:566-570. [Abstract]

10.Chou KC and Zhang CT. Prediction of protein structural classes. Crit.Rev.Biochem.Mol.Bio.1995;30:275-349. [Abstract]

11. Zell A and Mamier G. Stuttgart neural netwok simulator, version 4.2. University of Stuttgart, Stuttgart, Germany.

12. Romelhart DE, Hinton GE, and Williams RJ. Learning representations by back-propagation errors. Nature 1986;323:533-536.

13. Jones DT. Protein secondary structure prediction based on position- specific scoring matrices. J.Mol.Biol 1999; 292:195-202.[Abstract]

14.Giorgi MHM, Hazout S, and Tuffery P. PredAcc: prediction of solvent accessibility.Bioinformatics 1990;15:176-172. [Abstract]

15. Ahmad S, Gromiha MM and Sarai A. Real value prediction of solvent accessibility from amino acid sequence. Proteins 2003; 50:629-635. [Abstract]

16. Kaur H and Raghava GPS. Prediction of Beta-turns in proteins from multiple alignment using neural network.Protein Science 2003;12:627-634. [Abstract]

17. Kaur H and Raghava GPS. A neural network based method for the prediction of Gamma-turns in proteins using multiple alignment. Protein Science 2003;12:923. [Abstract]

18. Gromiha MM. Importance of native state topology for determining the folding rate of two state proteins. J.Chem.Inf.Comput.Sci. 2003; 43:1481-1485.[Abstract]

19. Rost B, Schneider R Sander C. Protein folding recognition by prediction based threading. J.Mol.Biol. 1997; 270:1-60. [Abstract]

20. Macdonald JR, and Johnson WC JR. Environmental features are important in determining protein secondary structure. Protein Science 2001;10:1172-1177. [Abstract]

21. Altschul Sp, Madden TL, Alejandro AS, Zhang J, Miller W, and Lipman DJ. Gapped blast and psi-blast:Anew generation of protein databases and search programs.Nucleic Acids Res. 1997;49:120-144. [Abstract]