Reviews: Protein contact prediction from amino acid co-evolution using convolutional networks for graph-valued images

Overall the paper presents a novel application of CNNs to co-evolution Protein Contact Prediction (PCP) with state-of-the-art results. The paper is a strong contribution to the field of coevolution PCP. The method has the potential to improve ab initio structure prediction accuracy, which could have wide ranging impacts on studying proteins without solved structural complexes. The paper itself contains flaws in the experimental setup, how the methods are described, and how the results are presented. Key issues: - Experimental setup: not enough unique training examples are used to compare to existing methods. More than 100K examples exist for this specific task, but only 307 are used.