About: Using Support Vector Machine and Evolutionary Profiles to Predict Antifreeze Protein Sequences

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Using Support Vector Machine and Evolutionary Profiles to Predict Antifreeze Protein Sequences
Creator	Ma, Zhiqiang Yin, Minghao Zhao, Xiaowei
Source	PMC
abstract	Antifreeze proteins (AFPs) are ice-binding proteins. Accurate identification of new AFPs is important in understanding ice-protein interactions and creating novel ice-binding domains in other proteins. In this paper, an accurate method, called AFP_PSSM, has been developed for predicting antifreeze proteins using a support vector machine (SVM) and position specific scoring matrix (PSSM) profiles. This is the first study in which evolutionary information in the form of PSSM profiles has been successfully used for predicting antifreeze proteins. Tested by 10-fold cross validation and independent test, the accuracy of the proposed method reaches 82.67% for the training dataset and 93.01% for the testing dataset, respectively. These results indicate that our predictor is a useful tool for predicting antifreeze proteins. A web server (AFP_PSSM) that implements the proposed predictor is freely available.
has issue date	2012-02-17(xsd:dateTime)
bibo:doi	10.3390/ijms13022196
bibo:pmid	22408447
has license	cc-by
sha1sum (hex)	058c0fc94ded8fa4e7a302081c3ea738d16a4b1a
schema:url	https://doi.org/10.3390/ijms13022196
resource representing a document's title	Using Support Vector Machine and Evolutionary Profiles to Predict Antifreeze Protein Sequences
has PubMed Central identifier	PMC3292016
has PubMed identifier	22408447
schema:publication	Int J Mol Sci
resource representing a document's body	covid:058c0fc94ded8fa4e7a302081c3ea738d16a4b1a#body_text
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