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Identifying anticancer peptides by using improved hybrid compositions

View Article: PubMed Central - PubMed

ABSTRACT

Cancer is one of the main causes of threats to human life. Identification of anticancer peptides is important for developing effective anticancer drugs. In this paper, we developed an improved predictor to identify the anticancer peptides. The amino acid composition (AAC), the average chemical shifts (acACS) and the reduced amino acid composition (RAAC) were selected to predict the anticancer peptides by using the support vector machine (SVM). The overall prediction accuracy reaches to 93.61% in jackknife test. The results indicated that the combined parameter was helpful to the prediction for anticancer peptides.

No MeSH data available.


prediction results with respect to the correlation factor λ of the acACS based on the jackknife test.The triangle indicates the best results obtained with λ = 5.
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f1: prediction results with respect to the correlation factor λ of the acACS based on the jackknife test.The triangle indicates the best results obtained with λ = 5.

Mentions: The acACS vectors were formed based on protein sequence, and then the best λ and i were selected. In order to obtain the best performance of predicting anticancer peptides, the combined scheme of chemically shifted atoms and the best λ were optimized with the maximum accuracy. Results in Fig. 1 showed that the accuracy was the best when λ = 5 and in Fig. 2 showed that the prediction result was the best when the combination mode of chemically shifted atoms was . Therefore, the combination mode chemically shifted was selected and the correlation factor λ was set to 5 for generating the acACS feature vectors.


Identifying anticancer peptides by using improved hybrid compositions
prediction results with respect to the correlation factor λ of the acACS based on the jackknife test.The triangle indicates the best results obtained with λ = 5.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5037382&req=5

f1: prediction results with respect to the correlation factor λ of the acACS based on the jackknife test.The triangle indicates the best results obtained with λ = 5.
Mentions: The acACS vectors were formed based on protein sequence, and then the best λ and i were selected. In order to obtain the best performance of predicting anticancer peptides, the combined scheme of chemically shifted atoms and the best λ were optimized with the maximum accuracy. Results in Fig. 1 showed that the accuracy was the best when λ = 5 and in Fig. 2 showed that the prediction result was the best when the combination mode of chemically shifted atoms was . Therefore, the combination mode chemically shifted was selected and the correlation factor λ was set to 5 for generating the acACS feature vectors.

View Article: PubMed Central - PubMed

ABSTRACT

Cancer is one of the main causes of threats to human life. Identification of anticancer peptides is important for developing effective anticancer drugs. In this paper, we developed an improved predictor to identify the anticancer peptides. The amino acid composition (AAC), the average chemical shifts (acACS) and the reduced amino acid composition (RAAC) were selected to predict the anticancer peptides by using the support vector machine (SVM). The overall prediction accuracy reaches to 93.61% in jackknife test. The results indicated that the combined parameter was helpful to the prediction for anticancer peptides.

No MeSH data available.