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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.


The different distribution of the amino acids compositions in anticancer peptides and non-anticancer peptides.
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f3: The different distribution of the amino acids compositions in anticancer peptides and non-anticancer peptides.

Mentions: where is the number of i-th amino acids of j-th protein in m-th group, denotes the total number of amino acids of j-th protein in m-th group, denotes the number of samples in the m-th group (here (k1 = 138, k2 = 206). We calculated the average amino acids compositions of anticancer peptides and non-anticancer peptides by using of Equation (9). The calculate results indicated that the amino acids compositions of anticancer peptides and non-anticancer peptides were different. Hence the amino acids compositions were suitable as features to distinguish anticancer peptides and non-anticancer peptides. The different distribution of the amino acids compositions in anticancer peptides and non-anticancer peptides were shown in Fig. 3.


Identifying anticancer peptides by using improved hybrid compositions
The different distribution of the amino acids compositions in anticancer peptides and non-anticancer peptides.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f3: The different distribution of the amino acids compositions in anticancer peptides and non-anticancer peptides.
Mentions: where is the number of i-th amino acids of j-th protein in m-th group, denotes the total number of amino acids of j-th protein in m-th group, denotes the number of samples in the m-th group (here (k1 = 138, k2 = 206). We calculated the average amino acids compositions of anticancer peptides and non-anticancer peptides by using of Equation (9). The calculate results indicated that the amino acids compositions of anticancer peptides and non-anticancer peptides were different. Hence the amino acids compositions were suitable as features to distinguish anticancer peptides and non-anticancer peptides. The different distribution of the amino acids compositions in anticancer peptides and non-anticancer peptides were shown in Fig. 3.

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.