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Prediction of donor splice sites using random forest with a new sequence encoding approach

Overview of attention for article published in BioData Mining, January 2016
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Title
Prediction of donor splice sites using random forest with a new sequence encoding approach
Published in
BioData Mining, January 2016
DOI 10.1186/s13040-016-0086-4
Pubmed ID
Authors

Prabina Kumar Meher, Tanmaya Kumar Sahu, Atmakuri Ramakrishna Rao

Abstract

Detection of splice sites plays a key role for predicting the gene structure and thus development of efficient analytical methods for splice site prediction is vital. This paper presents a novel sequence encoding approach based on the adjacent di-nucleotide dependencies in which the donor splice site motifs are encoded into numeric vectors. The encoded vectors are then used as input in Random Forest (RF), Support Vector Machines (SVM) and Artificial Neural Network (ANN), Bagging, Boosting, Logistic regression, kNN and Naïve Bayes classifiers for prediction of donor splice sites. The performance of the proposed approach is evaluated on the donor splice site sequence data of Homo sapiens, collected from Homo Sapiens Splice Sites Dataset (HS3D). The results showed that RF outperformed all the considered classifiers. Besides, RF achieved higher prediction accuracy than the existing methods viz., MEM, MDD, WMM, MM1, NNSplice and SpliceView, while compared using an independent test dataset. Based on the proposed approach, we have developed an online prediction server (MaLDoSS) to help the biological community in predicting the donor splice sites. The server is made freely available at http://cabgrid.res.in:8080/maldoss. Due to computational feasibility and high prediction accuracy, the proposed approach is believed to help in predicting the eukaryotic gene structure.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 36 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 36 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 17%
Researcher 6 17%
Student > Master 6 17%
Student > Bachelor 5 14%
Student > Doctoral Student 1 3%
Other 4 11%
Unknown 8 22%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 8 22%
Engineering 6 17%
Agricultural and Biological Sciences 3 8%
Computer Science 3 8%
Chemistry 2 6%
Other 4 11%
Unknown 10 28%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 23 January 2016.
All research outputs
#14,209,975
of 17,800,904 outputs
Outputs from BioData Mining
#235
of 277 outputs
Outputs of similar age
#242,338
of 350,082 outputs
Outputs of similar age from BioData Mining
#1
of 1 outputs
Altmetric has tracked 17,800,904 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 277 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.3. This one is in the 6th percentile – i.e., 6% of its peers scored the same or lower than it.
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