scholarly journals An Improved Method for Splice Site Prediction in DNA Sequences Using Support Vector Machines

2015 ◽  
Vol 57 ◽  
pp. 358-367 ◽  
Author(s):  
Neelam Goel ◽  
Shailendra Singh ◽  
Trilok Chand Aseri
Keyword(s):  
Support Vector Machines ◽  
Splice Site ◽  
Dna Sequences ◽  
Support Vector ◽  
Improved Method ◽  
Splice Site Prediction ◽  
Site Prediction ◽  
Vector Machines

scholarly journals Accurate splice site prediction using support vector machines

2007 ◽  
Vol 8 (Suppl 10) ◽  
pp. S7 ◽  
Author(s):  
Sören Sonnenburg ◽  
Gabriele Schweikert ◽  
Petra Philips ◽  
Jonas Behr ◽  
Gunnar Rätsch
Keyword(s):  
Support Vector Machines ◽  
Splice Site ◽  
Support Vector ◽  
Splice Site Prediction ◽  
Site Prediction ◽  
Vector Machines

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.

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 A novel artificial intelligence-based approach for identification of deoxynucleotide aptamers

2021 ◽  
Vol 17 (8) ◽  
pp. e1009247
Author(s):  
Frances L. Heredia ◽  
Abiel Roche-Lima ◽  
Elsie I. Parés-Matos
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Support Vector Machines ◽  
Dna Binding ◽  
Dna Sequences ◽  
Support Vector ◽  
Sequence Information ◽  
Dna Aptamers ◽  
Vector Machines ◽  
Selection Of

The selection of a DNA aptamer through the Systematic Evolution of Ligands by EXponential enrichment (SELEX) method involves multiple binding steps, in which a target and a library of randomized DNA sequences are mixed for selection of a single, nucleotide-specific molecule. Usually, 10 to 20 steps are required for SELEX to be completed. Throughout this process it is necessary to discriminate between true DNA aptamers and unspecified DNA-binding sequences. Thus, a novel machine learning-based approach was developed to support and simplify the early steps of the SELEX process, to help discriminate binding between DNA aptamers from those unspecified targets of DNA-binding sequences. An Artificial Intelligence (AI) approach to identify aptamers were implemented based on Natural Language Processing (NLP) and Machine Learning (ML). NLP method (CountVectorizer) was used to extract information from the nucleotide sequences. Four ML algorithms (Logistic Regression, Decision Tree, Gaussian Naïve Bayes, Support Vector Machines) were trained using data from the NLP method along with sequence information. The best performing model was Support Vector Machines because it had the best ability to discriminate between positive and negative classes. In our model, an Accuracy (A) of 0.995, the fraction of samples that the model correctly classified, and an Area Under the Receiving Operating Curve (AUROC) of 0.998, the degree by which a model is capable of distinguishing between classes, were observed. The developed AI approach is useful to identify potential DNA aptamers to reduce the amount of rounds in a SELEX selection. This new approach could be applied in the design of DNA libraries and result in a more efficient and faster process for DNA aptamers to be chosen during SELEX.

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