Convolutional Kitchen Sinks for Transcription Factor Binding Site Prediction

Alyssa Morrow; Vaishaal Shankar; Devin Petersohn; Anthony Joseph; Benjamin Recht; Nir Yosef

doi:10.48550/arxiv.1706.00125

Convolutional Kitchen Sinks for Transcription Factor Binding Site Prediction

Alyssa Morrow, Vaishaal Shankar, Devin Petersohn, Anthony Joseph, Benjamin Recht, Nir Yosef

Research output: Contribution to journal › Article

Abstract

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.

Original language	English
Number of pages	5
Journal	arxiv.org
DOIs	https://doi.org/10.48550/arxiv.1706.00125
State	In preparation - 31 May 2017
Externally published	Yes

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title = "Convolutional Kitchen Sinks for Transcription Factor Binding Site Prediction",

abstract = "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.",

author = "Alyssa Morrow and Vaishaal Shankar and Devin Petersohn and Anthony Joseph and Benjamin Recht and Nir Yosef",

year = "2017",

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T1 - Convolutional Kitchen Sinks for Transcription Factor Binding Site Prediction

AU - Morrow, Alyssa

AU - Shankar, Vaishaal

AU - Petersohn, Devin

AU - Joseph, Anthony

AU - Recht, Benjamin

AU - Yosef, Nir

PY - 2017/5/31

Y1 - 2017/5/31

N2 - 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.

AB - 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.

U2 - 10.48550/arxiv.1706.00125

DO - 10.48550/arxiv.1706.00125

M3 - مقالة

SN - 2331-8422

JO - arxiv.org

JF - arxiv.org

ER -

Convolutional Kitchen Sinks for Transcription Factor Binding Site Prediction

Abstract

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