Structure-Based Ab Initio Prediction of Transcription Factor–Binding Sites

2009 ◽  
pp. 23-41 ◽  
Author(s):  
L. Angela Liu ◽  
Joel S. Bader
Keyword(s):  
Transcription Factor ◽  
Ab Initio ◽  
Binding Sites ◽  
Transcription Factor Binding Sites ◽  
Transcription Factor Binding ◽  
Factor Binding ◽  
Ab Initio Prediction

Gene Variants and Haplotypes Modifying Transcription Factor Binding Sites in the Human Cyclooxygenase 1 and 2 (PTGS1 and PTGS2) Genes

2014 ◽  
Vol 15 (2) ◽  
pp. 182-195 ◽  
Author(s):  
Jose Agundez ◽  
David Gonzalez-Alvarez ◽  
Miguel Vega-Rodriguez ◽  
Emilia Botello ◽  
Elena Garcia-Martin
Keyword(s):  
Transcription Factor ◽  
Binding Sites ◽  
Transcription Factor Binding Sites ◽  
Transcription Factor Binding ◽  
Gene Variants ◽  
Factor Binding ◽  
Cyclooxygenase 1

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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