CyclinPred: a SVM-based method for predicting cyclin protein sequences.
Functional annotation of protein sequences with low similarity to well characterized protein sequences is a major challenge of computational biology in the post genomic era. The cyclin protein family is once such important family of proteins which consists of sequences with low sequence similarity m...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2008-07-01
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| Series: | PLoS ONE |
| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/18596929/?tool=EBI |
| _version_ | 1818434988226379776 |
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| author | Mridul K Kalita Umesh K Nandal Ansuman Pattnaik Anandhan Sivalingam Gowthaman Ramasamy Manish Kumar Gajendra P S Raghava Dinesh Gupta |
| author_facet | Mridul K Kalita Umesh K Nandal Ansuman Pattnaik Anandhan Sivalingam Gowthaman Ramasamy Manish Kumar Gajendra P S Raghava Dinesh Gupta |
| author_sort | Mridul K Kalita |
| collection | DOAJ |
| description | Functional annotation of protein sequences with low similarity to well characterized protein sequences is a major challenge of computational biology in the post genomic era. The cyclin protein family is once such important family of proteins which consists of sequences with low sequence similarity making discovery of novel cyclins and establishing orthologous relationships amongst the cyclins, a difficult task. The currently identified cyclin motifs and cyclin associated domains do not represent all of the identified and characterized cyclin sequences. We describe a Support Vector Machine (SVM) based classifier, CyclinPred, which can predict cyclin sequences with high efficiency. The SVM classifier was trained with features of selected cyclin and non cyclin protein sequences. The training features of the protein sequences include amino acid composition, dipeptide composition, secondary structure composition and PSI-BLAST generated Position Specific Scoring Matrix (PSSM) profiles. Results obtained from Leave-One-Out cross validation or jackknife test, self consistency and holdout tests prove that the SVM classifier trained with features of PSSM profile was more accurate than the classifiers based on either of the other features alone or hybrids of these features. A cyclin prediction server--CyclinPred has been setup based on SVM model trained with PSSM profiles. CyclinPred prediction results prove that the method may be used as a cyclin prediction tool, complementing conventional cyclin prediction methods. |
| first_indexed | 2024-12-14T16:45:44Z |
| format | Article |
| id | doaj.art-5a09ed92bf1f43c0a1fccdb63e09f612 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-14T16:45:44Z |
| publishDate | 2008-07-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-5a09ed92bf1f43c0a1fccdb63e09f6122022-12-21T22:54:11ZengPublic Library of Science (PLoS)PLoS ONE1932-62032008-07-0137e260510.1371/journal.pone.0002605CyclinPred: a SVM-based method for predicting cyclin protein sequences.Mridul K KalitaUmesh K NandalAnsuman PattnaikAnandhan SivalingamGowthaman RamasamyManish KumarGajendra P S RaghavaDinesh GuptaFunctional annotation of protein sequences with low similarity to well characterized protein sequences is a major challenge of computational biology in the post genomic era. The cyclin protein family is once such important family of proteins which consists of sequences with low sequence similarity making discovery of novel cyclins and establishing orthologous relationships amongst the cyclins, a difficult task. The currently identified cyclin motifs and cyclin associated domains do not represent all of the identified and characterized cyclin sequences. We describe a Support Vector Machine (SVM) based classifier, CyclinPred, which can predict cyclin sequences with high efficiency. The SVM classifier was trained with features of selected cyclin and non cyclin protein sequences. The training features of the protein sequences include amino acid composition, dipeptide composition, secondary structure composition and PSI-BLAST generated Position Specific Scoring Matrix (PSSM) profiles. Results obtained from Leave-One-Out cross validation or jackknife test, self consistency and holdout tests prove that the SVM classifier trained with features of PSSM profile was more accurate than the classifiers based on either of the other features alone or hybrids of these features. A cyclin prediction server--CyclinPred has been setup based on SVM model trained with PSSM profiles. CyclinPred prediction results prove that the method may be used as a cyclin prediction tool, complementing conventional cyclin prediction methods.https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/18596929/?tool=EBI |
| spellingShingle | Mridul K Kalita Umesh K Nandal Ansuman Pattnaik Anandhan Sivalingam Gowthaman Ramasamy Manish Kumar Gajendra P S Raghava Dinesh Gupta CyclinPred: a SVM-based method for predicting cyclin protein sequences. PLoS ONE |
| title | CyclinPred: a SVM-based method for predicting cyclin protein sequences. |
| title_full | CyclinPred: a SVM-based method for predicting cyclin protein sequences. |
| title_fullStr | CyclinPred: a SVM-based method for predicting cyclin protein sequences. |
| title_full_unstemmed | CyclinPred: a SVM-based method for predicting cyclin protein sequences. |
| title_short | CyclinPred: a SVM-based method for predicting cyclin protein sequences. |
| title_sort | cyclinpred a svm based method for predicting cyclin protein sequences |
| url | https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/18596929/?tool=EBI |
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