在发育中的神经视网膜中增强和沉默的主动学习。

Cell systems Pub Date : 2025-01-15 Epub Date: 2025-01-07 DOI:10.1016/j.cels.2024.12.004
Ryan Z Friedman, Avinash Ramu, Sara Lichtarge, Yawei Wu, Lloyd Tripp, Daniel Lyon, Connie A Myers, David M Granas, Maria Gause, Joseph C Corbo, Barak A Cohen, Michael A White
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引用次数: 0

摘要

深度学习是一种很有前途的顺调控元素建模策略。然而,在基因组序列上训练的模型往往不能解释为什么相同的转录因子可以在不同的背景下激活或抑制转录。为了解决这一限制,我们开发了一种主动学习方法来训练模型,以区分由光受体转录因子锥杆同源盒(CRX)结合位点组成的增强子和沉默子。在几乎对基因组中所有结合的CRX位点进行模型训练后,我们将合成生物学与不确定性采样相结合,以生成额外的信息性训练数据。这使我们能够在多轮大规模并行报告分析的数据上迭代地训练模型。所产生的模型能够区分具有相同序列但相反功能的CRX位点,这使得主动学习成为训练调节DNA模型的有效策略。本文的透明同行评议过程记录包含在补充信息中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Active learning of enhancers and silencers in the developing neural retina.

Deep learning is a promising strategy for modeling cis-regulatory elements. However, models trained on genomic sequences often fail to explain why the same transcription factor can activate or repress transcription in different contexts. To address this limitation, we developed an active learning approach to train models that distinguish between enhancers and silencers composed of binding sites for the photoreceptor transcription factor cone-rod homeobox (CRX). After training the model on nearly all bound CRX sites from the genome, we coupled synthetic biology with uncertainty sampling to generate additional rounds of informative training data. This allowed us to iteratively train models on data from multiple rounds of massively parallel reporter assays. The ability of the resulting models to discriminate between CRX sites with identical sequence but opposite functions establishes active learning as an effective strategy to train models of regulatory DNA. A record of this paper's transparent peer review process is included in the supplemental information.

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