Diet Networks: Thin Parameters for Fat Genomics
Learning tasks such as those involving genomic data often poses a serious challenge: the number of input features can be orders of magnitude larger than the number of training examples, making it difficult to avoid overfitting, even when using the known regularization techniques. We focus here on tasks in which the input is a description of the genetic variation specific to a patient, the single nucleotide polymorphisms (SNPs), yielding millions of ternary inputs. Improving the ability of deep learning to handle such datasets could have an important impact in medical research, more specifically in precision medicine, where high-dimensional data regarding a particular patient is used to make predictions of interest. Even though the amount of data for such tasks is increasing, this mismatch between the number of examples and the number of inputs remains a concern. Naive implementations of classifier neural networks involve a huge number of free parameters in their first layer (number of input features times number of hidden units): each input feature is associated with as many parameters as there are hidden units. We propose a novel neural network parametrization which considerably reduces the number of free parameters. It is based on the idea that we can first learn or provide a distributed representation for each input feature (e.g. for each position in the genome where variations are observed in data), and then learn (with another neural network called the parameter prediction network) how to map a feature*s distributed representation (based on the feature*s identity not its value) to the vector of parameters specific to that feature in the classifier neural network (the weights which link the value of the feature to each of the hidden units). This approach views the problem of producing the parameters associated with each feature as a multi-task learning problem. We show experimentally on a population stratification task of interest to medical studies that the proposed approach can significantly reduce both the number of parameters and the error rate of the classifier.
The paper presents an application of deep learning to genomic SNP data with a comparison of possible approaches for dealing with the very high data dimensionality. The approach looks very interesting but the experiments are too limited to draw firm conclusions about the strengths of different approaches. The presentation would benefit from more precise math. Quality: The basic idea of the paper is interesting and the applied deep learning methodology appears reasonable. The experimental evaluation is rather weak as it only covers a single data set and a very limited number of cross validation folds. Given the significant variation in the performances of all the methods, it seems the differences between the better-performing methods are probably not statistically significant. More comprehensive empirical validation could clearly strengthen the paper. Clarity: The writing is generally good both in terms of the biology and ML, but more mathematical rigour would make it easier to understand precisely what was done. The different architectures are explained on an intuitive level and might benefit from a clear mathematical definition. I was ultimately left unsure of what the *raw end2end* model is - given so few parameters it cannot work on raw 300k dimensional input but I could not figure out what kind of embedding was used. The results in Fig. 3 might be clearer if scaled so that maximum for each class is 1 to avoid confounding from different numbers of subjects in different classes. In the text, please use the standard italics math font for all symbols such as N, N_d, ... Originality: The application and the approach appear quite novel. Significance: There is clearly strong interest for deep learning in the genomics area and the paper seeks to address some of the major bottlenecks here. It is too early to tell whether the specific techniques proposed in the paper will be the ultimate solution, but at the very least the paper provides interesting new ideas for others to work on. Other comments: I think releasing the code as promised would be a must.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Diet Networks: Thin Parameters for Fat Genomics
Learning tasks such as those involving genomic data often poses a serious challenge: the number of input features can be orders of magnitude larger than the number of training examples, making it difficult to avoid overfitting, even when using the known regularization techniques. We focus here on tasks in which the input is a description of the genetic variation specific to a patient, the single nucleotide polymorphisms (SNPs), yielding millions of ternary inputs. Improving the ability of deep learning to handle such datasets could have an important impact in medical research, more specifically in precision medicine, where high-dimensional data regarding a particular patient is used to make predictions of interest. Even though the amount of data for such tasks is increasing, this mismatch between the number of examples and the number of inputs remains a concern. Naive implementations of classifier neural networks involve a huge number of free parameters in their first layer (number of input features times number of hidden units): each input feature is associated with as many parameters as there are hidden units. We propose a novel neural network parametrization which considerably reduces the number of free parameters. It is based on the idea that we can first learn or provide a distributed representation for each input feature (e.g. for each position in the genome where variations are observed in data), and then learn (with another neural network called the parameter prediction network) how to map a feature*s distributed representation (based on the feature*s identity not its value) to the vector of parameters specific to that feature in the classifier neural network (the weights which link the value of the feature to each of the hidden units). This approach views the problem of producing the parameters associated with each feature as a multi-task learning problem. We show experimentally on a population stratification task of interest to medical studies that the proposed approach can significantly reduce both the number of parameters and the error rate of the classifier.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



The paper presents an application of deep learning to genomic SNP data with a comparison of possible approaches for dealing with the very high data dimensionality. The approach looks very interesting but the experiments are too limited to draw firm conclusions about the strengths of different approaches. The presentation would benefit from more precise math. Quality: The basic idea of the paper is interesting and the applied deep learning methodology appears reasonable. The experimental evaluation is rather weak as it only covers a single data set and a very limited number of cross validation folds. Given the significant variation in the performances of all the methods, it seems the differences between the better-performing methods are probably not statistically significant. More comprehensive empirical validation could clearly strengthen the paper. Clarity: The writing is generally good both in terms of the biology and ML, but more mathematical rigour would make it easier to understand precisely what was done. The different architectures are explained on an intuitive level and might benefit from a clear mathematical definition. I was ultimately left unsure of what the *raw end2end* model is - given so few parameters it cannot work on raw 300k dimensional input but I could not figure out what kind of embedding was used. The results in Fig. 3 might be clearer if scaled so that maximum for each class is 1 to avoid confounding from different numbers of subjects in different classes. In the text, please use the standard italics math font for all symbols such as N, N_d, ... Originality: The application and the approach appear quite novel. Significance: There is clearly strong interest for deep learning in the genomics area and the paper seeks to address some of the major bottlenecks here. It is too early to tell whether the specific techniques proposed in the paper will be the ultimate solution, but at the very least the paper provides interesting new ideas for others to work on. Other comments: I think releasing the code as promised would be a must.
assistant
The paper presents an application of deep learning to genomic SNP data with a comparison of possible approaches for dealing with the very high data dimensionality. The approach looks very interesting but the experiments are too limited to draw firm conclusions about the strengths of different approaches. The presentation would benefit from more precise math. Quality: The basic idea of the paper is interesting and the applied deep learning methodology appears reasonable. The experimental evaluation is rather weak as it only covers a single data set and a very limited number of cross validation folds. Given the significant variation in the performances of all the methods, it seems the differences between the better-performing methods are probably not statistically significant. More comprehensive empirical validation could clearly strengthen the paper. Clarity: The writing is generally good both in terms of the biology and ML, but more mathematical rigour would make it easier to understand precisely what was done. The different architectures are explained on an intuitive level and might benefit from a clear mathematical definition. I was ultimately left unsure of what the *raw end2end* model is - given so few parameters it cannot work on raw 300k dimensional input but I could not figure out what kind of embedding was used. The results in Fig. 3 might be clearer if scaled so that maximum for each class is 1 to avoid confounding from different numbers of subjects in different classes. In the text, please use the standard italics math font for all symbols such as N, N_d, ... Originality: The application and the approach appear quite novel. Significance: There is clearly strong interest for deep learning in the genomics area and the paper seeks to address some of the major bottlenecks here. It is too early to tell whether the specific techniques proposed in the paper will be the ultimate solution, but at the very least the paper provides interesting new ideas for others to work on. Other comments: I think releasing the code as promised would be a must.