Transcription factor (TF) DNA-binding is a central mechanism in gene regulation. Biologists would like to know where and when these factors bind DNA. Hence, they require accurate DNA-binding models to enable binding prediction to any DNA sequence. Recent technological advancements measure the binding of a single TF to thousands of DNA sequences. One of the prevailing techniques, high-throughput SELEX, measures protein–DNA binding by high-throughput sequencing over several cycles of enrichment. Unfortunately, current computational methods to infer the binding preferences from high-throughput SELEX data do not exploit the richness of these data, and are under-using the most advanced computational technique, deep neural networks.

中文翻译：

---

DeepSELEX：使用多类CNN从HT-SELEX数据推断DNA结合偏好

转录因子（TF）DNA结合是基因调控的主要机制。生物学家想知道这些因素在何时何地结合DNA。因此，他们需要准确的DNA结合模型来预测与任何DNA序列的结合。最近的技术进步测量了单个TF与数千个DNA序列的结合。一种流行的技术是高通量SELEX，它通过在多个富集循环中进行高通量测序来测量蛋白质与DNA的结合。不幸的是，当前从高通量SELEX数据推断出绑定偏好的计算方法并未利用这些数据的丰富性，并且在使用最先进的计算技术即深度神经网络。