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1. Plant promoter-proximal pausing?

   https://doi.org/10.1038/s41477-021-00970-6  

   Morffy, Nicholas; Strader, Lucia C. (July 2021, Nature Plants)
2. Molecular models of bidirectional promoter regulation

   https://doi.org/10.1016/j.sbi.2024.102865  

   Nemsick, Sarah; Hansen, Anders S (August 2024, Current Opinion in Structural Biology)

   Approximately 11% of human genes are transcribed by a bidirectional promoter (BDP), defined as two genes with <1 kb between their transcription start sites. Despite their evolutionary conservation and enrichment for housekeeping genes and oncogenes, the regulatory role of BDPs remains unclear. BDPs have been suggested to facilitate gene coregulation and/or decrease expression noise. This review discusses these potential regulatory functions through the context of six prospective underlying mechanistic models: a single nucleosome free region, shared transcription factor/regulator binding, cooperative negative supercoiling, bimodal histone marks, joint activation by enhancer(s), and RNA-mediated recruitment of regulators. These molecular mechanisms may act independently and/or cooperatively to facilitate the coregulation and/or decreased expression noise predicted of BDPs. 

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3. The energetics of activator–promoter recognition

   https://doi.org/10.1016/j.coisb.2022.100434  

   Boeger, Hinrich (September 2022, Current Opinion in Systems Biology)
4. Recurrent enhancer-promoter interactions across samples

   https://doi.org/10.1101/2025.09.27.678855  

   Weston, Mathew; Gunjala, Satvik; Hu, Haiyan; Li, Xiaoman (September 2025, bioRxiv)

   Abstract Enhancer-promoter interactions (EPIs) are fundamental to gene regulation, and understanding their recurrence across diverse biological samples is key to deciphering chromatin architecture. In this study, we systematically analyzed the recurrence of EPIs across 49 Hi-C and 95 HiChIP datasets. We found that the majority of EPIs identified in a given sample were also present in other samples, regardless of the assay type (Hi-C or HiChIP) or the enhancer annotations used. Interestingly, EPIs that appeared unique to individual samples were typically surrounded by fewer neighboring EPIs, suggesting they may not represent truly sample-specific interactions. Our findings indicate that most human EPIs have already been captured and that cells primarily reuse subsets of these shared EPIs across different cell types and conditions. This study provides new insights into the pervasive and reusable nature of EPIs in the human genome, with important implications for chromatin conformation studies. 

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5. Net Promoter Sentiment Classifier Using OHPL-ALL

   https://doi.org/10.1109/BigData47090.2019.9006404  

   Vanderheyden, Bob; Xie, Ying; Rachumallu, Mohan (December 2019, 2019 IEEE International Conference on Big Data (Big Data))

   Net Promotor Score is an important business measurement process where customers are surveyed and asked to rate their likelihood of recommending the company's products and/or services. In many applications, customers are asked to respond on an 11-point ordinal scale of 0 to 10. In developing the score, the data are reformulated into a labelled 3 class scale (0-6: Detractor, 7-8: Passive and 9-10: Promoter). [1] Many companies that choose to use Net Promoter Score as a core management metric integrate the measurement into all phases of the company and seek every opportunity to assess company performance in terms of likelihood to promote the company. In addition to a variety of survey opportunities, the ability to score comments in survey, social media and blogs with promoter rating may provide an additional valuable source of business insight. Even on a three-point scale, Net Promoter is an ordinal classification problem. A number of successful algorithms, that develop ordinal classifiers have been developed. [2] None of the top performing classifiers can be used for applications like text classification or image classification, since they don't employ deep learning. Any appropriate strategy must utilize the ordering information of classes without imposing a strong continuous assumption or fixed spacing assumption on the ordinal classes. In this paper, we use a novel Deep Learning methodology called OHPLnet (Ordinal Hyperplane Loss Network) that is specifically designed for data with ordinal classes. [3] The algorithm is used to develop predictions of the eleven classes, that may be used in the standard Net Promoter Score generation process. 

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