In this short review paper, the significant and profound impacts of the Chou’s “invariance theorem” have been briefly presented with crystal clear convincingness.
Invariance Theorem Mahalanobis Distance Significant Impacts Profound ImpactsCONFLICTS OF INTEREST
The author declares no conflicts of interest regarding the publication of this paper.
REFERENCESReferencesChou, K.C. (1995) A Novel Approach to Predicting Protein Structural Classes in a (20-1)-D Amino Acid Composition Space. Proteins: Structure, Function & Genetics, 21, 319-344. https://doi.org/10.1002/prot.340210406Mahalanobis, P.C. (1936) On the Generalized Distance in Statistics. Proceedings of the National Academy of Sciences, India, 2, 49-55.Pillai, K.C.S. and Mahalanobis, D. (1985) Encyclopedia of Statistical Sciences. In: Kotz, S. and Johnson, N.L., Eds., John Wiley & Sons. (This Reference Also Presents a Brief Biography of Mahalanobis Who Was a Man of Great Originality and Who Made Considerable Contributions to Statistics, New York, 176-181.)Liu, W. and Chou, K.C. (1998) Prediction of Protein Structural Classes by Modified Mahalanobis Discriminant Algorithm. Journal of Protein Chemistry, 17, 209-217. https://doi.org/10.1023/A:1022576400291Lin, H. and Li, Q.Z. (2007) Predicting Conotoxin Superfamily and Family by Using Pseudo Amino Acid Composition and Modified Mahalanobis Discriminant. Biochemical and Biophysical Research Communications, 354, 548-551. https://doi.org/10.1016/j.bbrc.2007.01.011Lin, H. (2008) The Modified Mahalanobis Discriminant for Predicting Outer Membrane Proteins by Using Chou’s Pseudo Amino Acid Composition. Journal of Theoretical Biology, 252, 350-356.
https://doi.org/10.1016/j.jtbi.2008.02.004Ding, H., Liu, L., Guo, F.B., Huang, J. and Lin, H. (2011) Identify Golgi Protein Types with Modified Mahalanobis Discriminant Algorithm and Pseudo Amino Acid Composition. Protein & Peptide Letters, 18, 58-63.
https://doi.org/10.2174/092986611794328708