Computational methods that infer the function of proteins are key to understanding life at the molecular level. In recent years, representation learning has emerged as a powerful paradigm to discover new patterns among entities as varied as images, words, speech, molecules. In typical representation learning, there is only one source of data or one level of abstraction at which the learned representation occurs. However, proteins can be described by their primary, secondary, tertiary, and quaternary structure or even as nodes in protein-protein interaction networks. Given that protein function is an emergent property of all these levels of interactions in this work, we learn joint representations from both amino acid sequence and multilayer networks representing tissue-specific protein-protein interactions. Using these hybrid representations, we show that simple machine learning models trained using these hybrid representations outperform existing network-based methods on the task of tissue-specific protein function prediction on 13 out of 13 tissues. Furthermore, these representations outperform existing ones by 14% on average.
This paper introduces a method to incorporate both sequence information and graph information to learn the protein representations. The idea is very straightforward. Basically, it used the embedding from OhmNet [Marinka et al, 2017] for the graph information and used the sequence information from UniRep [Ethan et al, 2019] or SeqVec [Michael et al, 2019]. It uses one experiment to show the performance of the combination of the two pieces of information.
This paper should be rejected for the following reasons: 
(1) The paper is obviously in the preliminary form without too much polish.
 (2) The simple combination of the results from two published articles is not that interesting
(3) the presentation of the paper and idea is not in an acceptable form (the authors should at least draw a figure to show the big idea of the paper).
(4) the experiment is not convincing (there is only one experiment and it is not compared with the other state-of-the-art methods; since an embedding of a protein can be of broad usage, the authors should give its performance on four tasks: protein function prediction (GO term) [Maxat et al, 2018], enzyme function prediction (EC number) [Yu et al, 2018], protein secondary structure prediction [Sheng et al, 2016], protein contact map prediction [Jinbo Xu, 2019]) 
(5) The learned embedding is not well discussed. The author should at least visualize the embeddings and check the physical and biological meaning of those embeddings, if possible. 
Since this manuscript would be for sure and have to be largely rewritten in the future, I would not give too many detailed suggestions but some high-level suggestions if the authors would like to refine this manuscript further and submit it somewhere else or ICLR next year:
(1) Further improve the idea of combining different sources of information. Combining different pieces of information will definitely be helpful but the authors should figure out a way to use them in a more natural way.
(2) Compared with other methods, which can combine different sources of information.
(3) Run more experiments on various tasks instead of one: protein function prediction (GO term), enzyme function prediction (EC number), protein secondary structure prediction, protein contact map prediction
(4) Refine the representation of the paper.
References:
[Marinka et al, 2017] Predicting multicellular function through multi-layer tissue networks, 2017, https://arxiv.org/abs/1707.04638
[Ethan et al, 2019] Unified rational protein engineering with sequence-based deep representation learning, 2019, Nature Methods
[Michael et al, 2019] Modeling the Language of Life – Deep Learning Protein Sequences, 2019, https://www.biorxiv.org/content/10.1101/614313v2
[Maxat et al, 2018] DeepGO: predicting protein functions from sequence and interactions using a deep ontology-aware classifier, 2018, Bioinformatics
[Yu et al, 2018] DEEPre: sequence-based enzyme EC number prediction by deep learning, 2018, Bioinformatics
[Sheng et al, 2016] Protein Secondary Structure Prediction Using Deep Convolutional Neural Fields, 2016, Scientific Reports
[Jinbo Xu, 2019] Distance-based protein folding powered by deep learning, 2019, PNAS