2021
DOI: 10.1088/1742-6596/1824/1/012001
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Transcription factor binding site detection using convolutional neural networks with a functional group-based data representation

Abstract: Transcription factors (TFs) play an essential role in molecular biology by regulating gene expression. The binding sites of TFs can vary by a large amount and the numerous possible binding locations make their detection a challenging issue. Recently, several machine learning approaches using nucleotide sequence data were applied to classify DNA sequences regarding Transcription Factor Binding Sites (TFBS). We propose a novel training strategy without the traditional 1D nucleotide-based DNA sequence representat… Show more

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Cited by 2 publications
(2 citation statements)
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“…For representing expanded epigenetic alphabet data, we would suggest using an approach where significantly lower frequency of modified bases compared to unmodified has a lower impact. One might instead encode all nucleobases as vectors representing their functional groups, as recently done in a similar unmodified context [ 95 ]. Alternatively, one might adapt recent work which designed filters to create sparse codes, similar to images, that can effectively encode DNA motifs [ 96 ].…”
Section: Discussionmentioning
confidence: 99%
“…For representing expanded epigenetic alphabet data, we would suggest using an approach where significantly lower frequency of modified bases compared to unmodified has a lower impact. One might instead encode all nucleobases as vectors representing their functional groups, as recently done in a similar unmodified context [ 95 ]. Alternatively, one might adapt recent work which designed filters to create sparse codes, similar to images, that can effectively encode DNA motifs [ 96 ].…”
Section: Discussionmentioning
confidence: 99%
“…While large compendia of TF binding profiles in different cell types have been collected for humans [37], this effort remains unfeasible for less well-studied organisms, including most developmental model systems. With sufficient training data, TF binding can be computationally imputed [50][51][52][53][54][55][56][57][58][59][60][61][62][63][64][65], however, this does not necessarily generalize across species [66]. As a result, most current approaches use relatively simple models that combine experimentally measured CRE activity with TF binding motifs to computationally predict TF binding.…”
Section: Multi-omics To Capture Gene Regulationmentioning
confidence: 99%