Signal detection in genome sequences using complexity based features

Authors:
Mehdi Kargar;Aijun An;Nick Cercone;Kayvan Tirdad;Morteza Zihayat
Affiliations:
York University, Toronto, Canada;York University, Toronto, Canada;York University, Toronto, Canada;York University, Toronto, Canada;York University, Toronto, Canada
Venue:
Proceedings of the 12th International Workshop on Data Mining in Bioinformatics
Year:
2013

Citing 7
Cited 0

Probabilistic reasoning in intelligent systems: networks of plausible inference

Probabilistic reasoning in intelligent systems: networks of plausible inference
C4.5: programs for machine learning

C4.5: programs for machine learning
On Complexity Measures for Biological Sequences

CSB '04 Proceedings of the 2004 IEEE Computational Systems Bioinformatics Conference
Data Mining: Concepts and Techniques

Data Mining: Concepts and Techniques
Diverging patterns: discovering significant frequency change dissimilarities in large databases

Proceedings of the 18th ACM conference on Information and knowledge management
Evaluation of different complexity measures for signal detection in genome sequences

Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology
Distinguishing Endogenous Retroviral LTRs from SINE Elements Using Features Extracted from Evolved Side Effect Machines

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this work, we tackle the problem of evaluating complexity methods and measures for finding interesting signals in the whole genome of three prokaryotic organisms. In addition to previous complexity measures, new measures are introduced for representing Open Reading Frames (ORF). We apply different classification algorithms to determine which complexity measure results in better predictive performance in discriminating genes from pseudo-genes in ORFs. Also, we investigate whether positions and lengths of windows in ORFs have significant impact on distinguishing between genes and pseudo-genes. Different classification algorithms are applied for classifying ORFs into genes and pseudo-genes.