scholarly journals High-throughput chromatin information enables accurate tissue-specific prediction of transcription factor binding sites

2008 ◽  
Vol 37 (1) ◽  
pp. 14-25 ◽  
Author(s):  
Tom Whitington ◽  
Andrew C. Perkins ◽  
Timothy L. Bailey
Keyword(s):  
Transcription Factor ◽  
High Throughput ◽  
Binding Sites ◽  
Transcription Factor Binding Sites ◽  
Transcription Factor Binding ◽  
Tissue Specific ◽  
Factor Binding ◽  
Specific Prediction

High-throughput SELEX–SAGE method for quantitative modeling of transcription-factor binding sites

2002 ◽  
Vol 20 (8) ◽  
pp. 831-835 ◽  
Author(s):  
Emmanuelle Roulet ◽  
Stéphane Busso ◽  
Anamaria A. Camargo ◽  
Andrew J.G. Simpson ◽  
Nicolas Mermod ◽  
...  
Keyword(s):  
Transcription Factor ◽  
High Throughput ◽  
Binding Sites ◽  
Transcription Factor Binding Sites ◽  
Transcription Factor Binding ◽  
Quantitative Modeling ◽  
Factor Binding

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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