Splice Site Recognition in DNA Sequences Using K-mer Frequency Based Mapping for Support Vector Machine with Power Series Kernel

2008 ◽  
Author(s):  
Robertas Damaševicius
Keyword(s):  
Support Vector Machine ◽  
Power Series ◽  
Splice Site ◽  
Dna Sequences ◽  
Support Vector ◽  
Site Recognition

Splicing-site recognition of rice (Oryza sativa L.) DNA sequences by support vector machines

2003 ◽  
Vol 4 (5) ◽  
pp. 573-577
Author(s):  
Peng Si-hua ◽  
Fan Long-jiang ◽  
Peng Xiao-ning ◽  
Zhuang Shu-lin ◽  
Du Wei ◽  
...  
Keyword(s):  
Oryza Sativa ◽  
Support Vector Machines ◽  
Dna Sequences ◽  
Oryza Sativa L ◽  
Support Vector ◽  
Vector Machines ◽  
Site Recognition ◽  
Splicing Site

scholarly journals Prediction of methylated CpGs in DNA sequences using a support vector machine

FEBS Letters ◽  
2005 ◽  
Vol 579 (20) ◽  
pp. 4302-4308 ◽  
Author(s):  
Manoj Bhasin ◽  
Hong Zhang ◽  
Ellis L. Reinherz ◽  
Pedro A. Reche
Keyword(s):  
Support Vector Machine ◽  
Dna Sequences ◽  
Support Vector

scholarly journals DHSpred: support-vector-machine-based human DNase I hypersensitive sites prediction using the optimal features selected by random forest

2017 ◽  
Author(s):  
Balachandran Manavalan ◽  
Tae Hwan Shin ◽  
Gwang Lee
Keyword(s):  
Support Vector Machine ◽  
Random Forest ◽  
Dna Sequences ◽  
Feature Selection Method ◽  
Regulatory Elements ◽  
Dnase I ◽  
Support Vector ◽  
Large Set ◽  
Dnase I Hypersensitive Sites ◽  
Hypersensitive Sites

AbstractDNase I hypersensitive sites (DHSs) are genomic regions that provide important information regarding the presence of transcriptional regulatory elements and the state of chromatin. Therefore, identifying DHSs in uncharacterized DNA sequences is crucial for understanding their biological functions and mechanisms. Although many experimental methods have been proposed to identify DHSs, they have proven to be expensive for genome-wide application. Therefore, it is necessary to develop computational methods for DHS prediction. In this study, we proposed a support vector machine (SVM)-based method for predicting DHSs, called DHSpred (DNase I Hypersensitive Site predictor in human DNA sequences), which was trained with 174 optimal features. The optimal combination of features was identified from a large set that included nucleotide composition and di- and trinucleotide physicochemical properties, using a random forest algorithm. DHSpred achieved a Matthews correlation coefficient and accuracy of 0.660 and 0.871, respectively, which were 3% higher than those of control SVM predictors trained with non-optimized features, indicating the efficiency of the feature selection method. Furthermore, the performance of DHSpred was superior to that of state-of-the-art predictors. An online prediction server has been developed to assist the scientific community, and is freely available at:http://www.thegleelab.org/DHSpred.html.

scholarly journals Accuracy test in identifying the splice site type of DNA sequences by using probabilistic neural networks and support vector machines

2014 ◽  
Vol 1 (1) ◽  
Author(s):  
Djati Kerami
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Support Vector Machines ◽  
Splice Site ◽  
Dna Sequences ◽  
Support Vector ◽  
Probabilistic Neural Networks ◽  
Application Problem ◽  
Vector Machines ◽  
Accuracy Level

It has been known that Probabilistic Neural Networks as machine learning is very fast in it’s computation time and give a better accuracy comparing to another type of neural networks, on solving a real-world application problem. In the recent years, Support Vector Machines has become a popular model over other machine learning. It can be analyzed theoretically and can achieve a good performance at same time. This paper will describe the use of those machines learning to solve pattern recognition problems with a preliminary case study in detecting the type of splice site on the DNA sequences, particularity on the accuracy level. The results obtained show that Support Vector Machines have a good accuracy level about 95 % comparing to Probabilistic Neural Networks with 92 % approximately.

Support vector machine? – not only for MRI-images

2020 ◽  
Author(s):  
V Vasilevska ◽  
K Schlaaf ◽  
H Dobrowolny ◽  
G Meyer-Lotz ◽  
HG Bernstein ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Support Vector
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