LIPOPREDICT: Bacterial lipoprotein prediction server

Bacterial lipoproteins have many important functions owing to their essential nature and roles in pathogenesis and represent a class of possible vaccine candidates. The prediction of bacterial lipoproteins from sequence is thus an important task for computational vaccinology. A Support Vector Machin...

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Detalles Bibliográficos
Autores principales: Kumari, S Ramya, Kadam, Kiran, Badwaik, Ritesh, Jayaraman, Valadi K
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Biomedical Informatics 2012
Materias:
Server
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3346017/
https://www.ncbi.nlm.nih.gov/pubmed/22570522
http://dx.doi.org/10.6026/97320630008394
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author Kumari, S Ramya
Kadam, Kiran
Badwaik, Ritesh
Jayaraman, Valadi K
author_facet Kumari, S Ramya
Kadam, Kiran
Badwaik, Ritesh
Jayaraman, Valadi K
author_sort Kumari, S Ramya
collection PubMed
description Bacterial lipoproteins have many important functions owing to their essential nature and roles in pathogenesis and represent a class of possible vaccine candidates. The prediction of bacterial lipoproteins from sequence is thus an important task for computational vaccinology. A Support Vector Machines (SVM) based module for predicting bacterial lipoproteins, LIPOPREDICT, has been developed. The best performing sequence model were generated using selected dipeptide composition, which gave 97% accuracy of prediction. The results obtained were compared very well with those of previously developed methods. AVAILABILITY: The database is available for free at www.lipopredict.cdac.in
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spelling pubmed-33460172012-05-08 LIPOPREDICT: Bacterial lipoprotein prediction server Kumari, S Ramya Kadam, Kiran Badwaik, Ritesh Jayaraman, Valadi K Bioinformation Server Bacterial lipoproteins have many important functions owing to their essential nature and roles in pathogenesis and represent a class of possible vaccine candidates. The prediction of bacterial lipoproteins from sequence is thus an important task for computational vaccinology. A Support Vector Machines (SVM) based module for predicting bacterial lipoproteins, LIPOPREDICT, has been developed. The best performing sequence model were generated using selected dipeptide composition, which gave 97% accuracy of prediction. The results obtained were compared very well with those of previously developed methods. AVAILABILITY: The database is available for free at www.lipopredict.cdac.in Biomedical Informatics 2012-04-30 /pmc/articles/PMC3346017/ /pubmed/22570522 http://dx.doi.org/10.6026/97320630008394 Text en © 2012 Biomedical Informatics This is an open-access article, which permits unrestricted use, distribution, and reproduction in any medium, for non-commercial purposes, provided the original author and source are credited.
spellingShingle Server
Kumari, S Ramya
Kadam, Kiran
Badwaik, Ritesh
Jayaraman, Valadi K
LIPOPREDICT: Bacterial lipoprotein prediction server
title LIPOPREDICT: Bacterial lipoprotein prediction server
title_full LIPOPREDICT: Bacterial lipoprotein prediction server
title_fullStr LIPOPREDICT: Bacterial lipoprotein prediction server
title_full_unstemmed LIPOPREDICT: Bacterial lipoprotein prediction server
title_short LIPOPREDICT: Bacterial lipoprotein prediction server
title_sort lipopredict: bacterial lipoprotein prediction server
topic Server
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3346017/
https://www.ncbi.nlm.nih.gov/pubmed/22570522
http://dx.doi.org/10.6026/97320630008394
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AT kadamkiran lipopredictbacteriallipoproteinpredictionserver
AT badwaikritesh lipopredictbacteriallipoproteinpredictionserver
AT jayaramanvaladik lipopredictbacteriallipoproteinpredictionserver

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