Phylogenetic Manifold Regularization: A semi-supervised approach to predict transcription factor binding sites

Ahsan, Faizy; Drouin, Alexandre; Laviolette, François; Precup, Doina; Blanchette, Mathieu

doi:10.1109/bibm49941.2020.9313437

Cited by 2 publications

(1 citation statement)

References 17 publications

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“…However, such approaches may suffer from binding sites turnover phenomenon, where the number of binding sites in a given region is maintained despite the sequence itself is not conserved in the orthologous regions ( Moses et al , 2006 ; Sinha and Siggia, 2005 ). Therefore, a more sophisticated operation is required to use the sequence function conservation property rather than the crude combination of sequence conservation score with the deep learning methods ( Ahsan et al , 2020 ).…”

Section: Introductionmentioning

confidence: 99%

PhyloPGM: boosting regulatory function prediction accuracy using evolutionary information

et al. 2022

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Motivation The computational prediction of regulatory function associated with a genomic sequence is of utter importance in -omics study, which facilitates our understanding of the underlying mechanisms underpinning the vast gene regulatory network. Prominent examples in this area include the binding prediction of transcription factors in DNA regulatory regions, and predicting RNA–protein interaction in the context of post-transcriptional gene expression. However, existing computational methods have suffered from high false-positive rates and have seldom used any evolutionary information, despite the vast amount of available orthologous data across multitudes of extant and ancestral genomes, which readily present an opportunity to improve the accuracy of existing computational methods. Results In this study, we present a novel probabilistic approach called PhyloPGM that leverages previously trained TFBS or RNA–RBP binding predictors by aggregating their predictions from various orthologous regions, in order to boost the overall prediction accuracy on human sequences. Throughout our experiments, PhyloPGM has shown significant improvement over baselines such as the sequence-based RNA–RBP binding predictor RNATracker and the sequence-based TFBS predictor that is known as FactorNet. PhyloPGM is simple in principle, easy to implement and yet, yields impressive results. Availability and implementation The PhyloPGM package is available at https://github.com/BlanchetteLab/PhyloPGM Supplementary information Supplementary data are available at Bioinformatics online.

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Section: Introductionmentioning

confidence: 99%

PhyloPGM: boosting regulatory function prediction accuracy using evolutionary information

et al. 2022

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Prediction of Transcription Factor Binding Sites of SP1 on Human Chromosome1

2021

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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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Phylogenetic Manifold Regularization: A semi-supervised approach to predict transcription factor binding sites

Cited by 2 publications

References 17 publications

PhyloPGM: boosting regulatory function prediction accuracy using evolutionary information

PhyloPGM: boosting regulatory function prediction accuracy using evolutionary information

Prediction of Transcription Factor Binding Sites of SP1 on Human Chromosome1

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