scholarly journals Correction: Nikitin, D., et al. Retroelement—Linked Transcription Factor Binding Patterns Point to Quickly Developing Molecular Pathways in Human Evolution. Cells 2019, 8, 130

Cells ◽  
2019 ◽  
Vol 8 (8) ◽  
pp. 832
Author(s):  
Daniil Nikitin ◽  
Andrew Garazha ◽  
Maxim Sorokin ◽  
Dmitry Penzar ◽  
Victor Tkachev ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Human Evolution ◽  
Binding Sites ◽  
Transcription Factor Binding Sites ◽  
Transcription Factor Binding ◽  
Endogenous Retroviruses ◽  
Molecular Pathways ◽  
Ltr Retrotransposons ◽  
Factor Binding

In the article ‘Retroelement—Linked Transcription Factor Binding Patterns Point to Quickly Developing Molecular Pathways in Human Evolution,’ a number of transcription factor binding sites (TFBS) mapped on all retroelement classes were incorrectly calculated as sum of TFBS numbers separately mapped on LINEs, SINEs and LTR retrotransposons/endogenous retroviruses (LR/ERVs) [...]

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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