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1por Yang, Lin, Orenstein, Yaron, Jolma, Arttu, Yin, Yimeng, Taipale, Jussi, Shamir, Ron, Rohs, Remo“…Using these data, we demonstrated the contributions of DNA shape readout across diverse TF families and its importance in core motif‐flanking regions. Statistical machine‐learning models combined with feature‐selection techniques helped to reveal the nucleotide position‐dependent DNA shape readout in TF‐binding sites and the TF family‐specific position dependence. …”
Publicado 2017
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2por Hadžić, Tarik, Park, Dongkook, Abruzzi, Katharine C., Yang, Lin, Trigg, Jennifer S., Rohs, Remo, Rosbash, Michael, Taghert, Paul H.“…DIMM binds preferentially to certain E-boxes within first introns of specific gene isoforms. Statistical machine learning revealed that flanking regions of putative DIMM binding sites contribute to its DNA binding specificity. …”
Publicado 2015
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3“…We validated this approach, which only requires nucleotide sequence as input, based on direct comparison with NLPB calculations for available crystal structures. Using statistical machine-learning approaches, we showed that adding EP as a biophysical feature can improve the predictive power of quantitative binding specificity models across 27 transcription factor families. …”
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