Convolutional Kitchen Sinks for Transcription Factor Binding Site Prediction

By A. Morrow, Vaishaal Shankar, D. Petersohn, Anthony D. Joseph, B. Recht, Nir Yosef. 2017

21 citations recorded by the source corpus.

We present a simple and efficient method for prediction of transcription factor binding sites from DNA sequence. Our method computes a random approximation of a convolutional kernel feature map from DNA sequence and then learns a linear model from the approximated feature map. Our method outperforms state-of-the-art deep learning methods on five out of six test datasets from the ENCODE consortium, while training in less than one eighth the time.

Subfields: Molecular Biology

Citations in the corpus

• Biological Sequence Modeling with Convolutional Kernel Networks (2017, neutral): “As observed by Morrow et al. [2017], the mismatch kernel [Leslie and Kuang, 2004] for modeling sequences may be written as Eq.”