Convolutional Kitchen Sinks for Transcription Factor Binding Site Prediction [article]

Alyssa Morrow, Vaishaal Shankar, Devin Petersohn, Anthony Joseph, Benjamin Recht, Nir Yosef
<span title="2017-05-31">2017</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
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.
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="">arXiv:1706.00125v1</a> <a target="_blank" rel="external noopener" href="">fatcat:36buw2z3rvao7deae3a62nxzke</a> </span>
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