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.
@article{arxiv.1706.00125,
title = {Convolutional Kitchen Sinks for Transcription Factor Binding Site Prediction},
author = {Alyssa Morrow and Vaishaal Shankar and Devin Petersohn and Anthony Joseph and Benjamin Recht and Nir Yosef},
journal= {arXiv preprint arXiv:1706.00125},
year = {2017}
}