A Graph Theoretic Approach for the Feature Extraction of Transcription Factor Binding Sites

2015 ◽  
pp. 445-455
Author(s):  
Yinglei Song ◽  
Albert Y. Chi ◽  
Junfeng Qu
Keyword(s):  
Transcription Factor ◽  
Feature Extraction ◽  
Binding Sites ◽  
Transcription Factor Binding Sites ◽  
Transcription Factor Binding ◽  
Theoretic Approach ◽  
Factor Binding ◽  
Graph Theoretic ◽  
Graph Theoretic Approach

scholarly journals Efficient Identification of Transcription Factor Binding Sites with a Graph Theoretic Approach

2013 ◽  
Vol 2013 ◽  
pp. 1-6
Author(s):  
Jia Song ◽  
Li Xu ◽  
Hong Sun
Keyword(s):  
Transcription Factor ◽  
Binding Sites ◽  
Prediction Accuracy ◽  
Transcription Factor Binding Sites ◽  
Transcription Factor Binding ◽  
Theoretic Model ◽  
Theoretic Approach ◽  
Promoter Regions ◽  
Factor Binding ◽  
Graph Theoretic

Identifying transcription factor binding sites with experimental methods is often expensive and time consuming. Although many computational approaches and tools have been developed for this problem, the prediction accuracy is not satisfactory. In this paper, we develop a new computational approach that can model the relationships among all short sequence segments in the promoter regions with a graph theoretic model. Based on this model, finding the locations of transcription factor binding site is reduced to computing maximum weighted cliques in a graph with weighted edges. We have implemented this approach and used it to predict the binding sites in two organisms,Caenorhabditis elegansandmus musculus. We compared the prediction accuracy with that of the Gibbs Motif Sampler. We found that the accuracy of our approach is higher than or comparable with that of the Gibbs Motif Sampler for most of tested data and can accurately identify binding sites in cases where the Gibbs Motif Sampler has difficulty to predict their locations.

scholarly journals Prediction of Transcription Factor Binding Sites of SP1 on Human Chromosome1

2021 ◽  
Vol 11 (11) ◽  
pp. 5123
Author(s):  
Maiada M. Mahmoud ◽  
Nahla A. Belal ◽  
Aliaa Youssif
Keyword(s):  
Transcription Factor ◽  
Binding Sites ◽  
Messenger Rna ◽  
Area Under The Curve ◽  
Noisy Data ◽  
Transcription Factor Binding Sites ◽  
Classification Problem ◽  
Transcription Factor Binding ◽  
K Nearest Neighbors ◽  
Factor Binding

Transcription factors (TFs) are proteins that control the transcription of a gene from DNA to messenger RNA (mRNA). TFs bind to a specific DNA sequence called a binding site. Transcription factor binding sites have not yet been completely identified, and this is considered to be a challenge that could be approached computationally. This challenge is considered to be a classification problem in machine learning. In this paper, the prediction of transcription factor binding sites of SP1 on human chromosome1 is presented using different classification techniques, and a model using voting is proposed. The highest Area Under the Curve (AUC) achieved is 0.97 using K-Nearest Neighbors (KNN), and 0.95 using the proposed voting technique. However, the proposed voting technique is more efficient with noisy data. This study highlights the applicability of the voting technique for the prediction of binding sites, and highlights the outperformance of KNN on this type of data. The study also highlights the significance of using voting.

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