Cellular Automata in Splice Site Prediction
نویسندگان
چکیده
منابع مشابه
Feature subset selection for splice site prediction
MOTIVATION The large amount of available annotated Arabidopsis thaliana sequences allows the induction of splice site prediction models with supervised learning algorithms (see Haussler (1998) for a review and references). These algorithms need information sources or features from which the models can be computed. For splice site prediction, the features we consider in this study are the presen...
متن کاملSplice Site Prediction Using Artificial Neural Networks
A system for utilizing an artificial neural network to predict splice sites in genes has been studied. The neural network uses a sliding window of nucleotides over a gene and predicts possible splice sites. Based on the neural network output, the exact location of the splice site is found using a curve fitting of a parabolic function. The splice site location is predicted without prior knowledg...
متن کاملSplice site prediction using stochastic regular grammars.
This paper presents a novel approach to the problem of splice site prediction, by applying stochastic grammar inference. We used four grammar inference algorithms to infer 1465 grammars, and used 10-fold cross-validation to select the best grammar for each algorithm. The corresponding grammars were embedded into a classifier and used to run splice site prediction and compare the results with th...
متن کاملPre-mRNA Secondary Structure Prediction Aids Splice Site Prediction
Accurate splice site prediction is a critical component of any computational approach to gene prediction in higher organisms. Existing approaches generally use sequence-based models that capture local dependencies among nucleotides in a small window around the splice site. We present evidence that computationally predicted secondary structure of moderate length pre-mRNA subsequencies contains i...
متن کاملDNA Encoding for Splice Site Prediction in Large DNA Sequence
Splice site prediction in the pre-mRNA is a very important task for understanding gene structure and its function. To predict splice sites, SVM (support vector machine) based classification technique is frequently used because of its classification accuracy. High classification accuracy of SVM largely depends on DNA encoding method for feature extraction of DNA sequences. However, existing enco...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: MOJ Proteomics & Bioinformatics
سال: 2014
ISSN: 2374-6920
DOI: 10.15406/mojpb.2014.01.00013