An alternative splicing predictor in C.Elegans based on time series analysis

Michele Ceccarelli, Antonio Maratea

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Prediction of Alternative Splicing has been traditionally based on expressed sequences' study, helped by homology considerations and the analysis of local discriminative features. More recently, Machine Learning algorithms have been developed that try avoid the use of a priori information, with partial success. Here we approach the prediction of Alternative Splicing as a time series analysis problem and we show that it is possible to obtain results similar or better than the state of the art without any explicit modeling of homology, positions in the splice site, nor any use of other local features. As a consequence, our method has a better generality and a broader and simpler applicability with respect to previous ones. Results on pre-mRNA sequences in C.Elegans are reported.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages588-595
Number of pages8
Volume4578 LNAI
Publication statusPublished - 24 Dec 2007
Externally publishedYes
Event7th International Workshop on Fuzzy Logic and Applications, WILF 2007 - Camogli, Italy
Duration: 7 Jul 200710 Jul 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4578 LNAI
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other7th International Workshop on Fuzzy Logic and Applications, WILF 2007
CountryItaly
CityCamogli
Period7/7/0710/7/07

Fingerprint

Alternative Splicing
Time series analysis
Time Series Analysis
Homology
Predictors
Prediction
Local Features
RNA Precursors
Messenger RNA
Learning algorithms
Learning systems
Learning Algorithm
Machine Learning
Partial
Modeling

Keywords

  • Alternative splicing
  • Autoregressive model
  • Support vector machine

ASJC Scopus subject areas

  • Computer Science(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Theoretical Computer Science

Cite this

Ceccarelli, M., & Maratea, A. (2007). An alternative splicing predictor in C.Elegans based on time series analysis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4578 LNAI, pp. 588-595). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4578 LNAI).

An alternative splicing predictor in C.Elegans based on time series analysis. / Ceccarelli, Michele; Maratea, Antonio.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 4578 LNAI 2007. p. 588-595 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4578 LNAI).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Ceccarelli, M & Maratea, A 2007, An alternative splicing predictor in C.Elegans based on time series analysis. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 4578 LNAI, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4578 LNAI, pp. 588-595, 7th International Workshop on Fuzzy Logic and Applications, WILF 2007, Camogli, Italy, 7/7/07.
Ceccarelli M, Maratea A. An alternative splicing predictor in C.Elegans based on time series analysis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 4578 LNAI. 2007. p. 588-595. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
Ceccarelli, Michele ; Maratea, Antonio. / An alternative splicing predictor in C.Elegans based on time series analysis. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 4578 LNAI 2007. pp. 588-595 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{d7ba529848a946b1bb9d034ac5c29b27,
title = "An alternative splicing predictor in C.Elegans based on time series analysis",
abstract = "Prediction of Alternative Splicing has been traditionally based on expressed sequences' study, helped by homology considerations and the analysis of local discriminative features. More recently, Machine Learning algorithms have been developed that try avoid the use of a priori information, with partial success. Here we approach the prediction of Alternative Splicing as a time series analysis problem and we show that it is possible to obtain results similar or better than the state of the art without any explicit modeling of homology, positions in the splice site, nor any use of other local features. As a consequence, our method has a better generality and a broader and simpler applicability with respect to previous ones. Results on pre-mRNA sequences in C.Elegans are reported.",
keywords = "Alternative splicing, Autoregressive model, Support vector machine",
author = "Michele Ceccarelli and Antonio Maratea",
year = "2007",
month = "12",
day = "24",
language = "English",
isbn = "9783540733997",
volume = "4578 LNAI",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "588--595",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

}

TY - GEN

T1 - An alternative splicing predictor in C.Elegans based on time series analysis

AU - Ceccarelli, Michele

AU - Maratea, Antonio

PY - 2007/12/24

Y1 - 2007/12/24

N2 - Prediction of Alternative Splicing has been traditionally based on expressed sequences' study, helped by homology considerations and the analysis of local discriminative features. More recently, Machine Learning algorithms have been developed that try avoid the use of a priori information, with partial success. Here we approach the prediction of Alternative Splicing as a time series analysis problem and we show that it is possible to obtain results similar or better than the state of the art without any explicit modeling of homology, positions in the splice site, nor any use of other local features. As a consequence, our method has a better generality and a broader and simpler applicability with respect to previous ones. Results on pre-mRNA sequences in C.Elegans are reported.

AB - Prediction of Alternative Splicing has been traditionally based on expressed sequences' study, helped by homology considerations and the analysis of local discriminative features. More recently, Machine Learning algorithms have been developed that try avoid the use of a priori information, with partial success. Here we approach the prediction of Alternative Splicing as a time series analysis problem and we show that it is possible to obtain results similar or better than the state of the art without any explicit modeling of homology, positions in the splice site, nor any use of other local features. As a consequence, our method has a better generality and a broader and simpler applicability with respect to previous ones. Results on pre-mRNA sequences in C.Elegans are reported.

KW - Alternative splicing

KW - Autoregressive model

KW - Support vector machine

UR - http://www.scopus.com/inward/record.url?scp=37249051302&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=37249051302&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:37249051302

SN - 9783540733997

VL - 4578 LNAI

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 588

EP - 595

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ER -