Amino Acid Principal Component Analysis (AAPCA) and its applications in protein structural class prediction.

@article{Du2006AminoAP,
  title={Amino Acid Principal Component Analysis (AAPCA) and its applications in protein structural class prediction.},
  author={Qishi Du and Zhi-qin Jiang and Wen-Zhang He and Dongjuan Li and Kou-Chen Chou},
  journal={Journal of biomolecular structure & dynamics},
  year={2006},
  volume={23 6},
  pages={
          635-40
        }
}
The extremely complicated nature of many biological problems makes them bear the features of fuzzy sets, such as with vague, imprecise, noisy, ambiguous, or input-missing information For instance, the current data in classifying protein structural classes are typically a fuzzy set To deal with this kind of problem, the AAPCA (Amino Acid Principal Component Analysis) approach was introduced. In the AAPCA approach the 20-dimensional amino acid composition space is reduced to an orthogonal space… CONTINUE READING

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