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2016 | 63 | 3 | 483-491

Article title

Identification of antimicrobial peptides by using eigenvectors

Authors

Content

Title variants

Languages of publication

EN

Abstracts

EN
Antibacterial peptides are subject to broad research due to their potential application and the benefit they can provide for a wide range of diseases. In this work, a mathematical-computational method, called the Polarity Vector Method, is introduced that has a high discriminative level (>70%) to identify peptides associated with Gram (-) bacteria, Gram (+) bacteria, cancer cells, fungi, insects, mammalian cells, parasites, and viruses, taken from the Antimicrobial Peptides Database. This supervised method uses only eigenvectors from the incident polar matrix of the group studied. It was verified with a comparative study with another extensively verified method developed previously by our team, the Polarity Index Method. The number of positive hits of both methods was up to 98% in all the tests conducted.

Year

Volume

63

Issue

3

Pages

483-491

Physical description

Dates

published
2016
received
2015-02-13
revised
2016-02-10
accepted
2016-06-13
(unknown)
2016-06-23

Contributors

  • Department of Mathematics, Faculty of Sciences, Universidad Nacional Autónoma de México, México

References

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Document Type

Publication order reference

Identifiers

YADDA identifier

bwmeta1.element.bwnjournal-article-abpv63p483kz
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