  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.
