Using Ontologies To Improve Performance In Massively Multi-label Prediction
Massively multi-label prediction/classification problems arise in environments like health-care or biology where it is useful to make very precise predictions. One challenge with massively multi-label problems is that there is often a long-tailed frequency distribution for the labels, resulting in few positive examples for the rare labels. We propose a solution to this problem by modifying the output layer of a neural network to create a Bayesian network of sigmoids which takes advantage of ontology relationships between the labels to help share information between the rare and the more common labels. We apply this method to the two massively multi-label tasks of disease prediction (ICD-9 codes) and protein function prediction (Gene Ontology terms) and obtain significant improvements in per-label AUROC and average precision.
 This is a clear and well written paper that attempts to improve our ability to predict in the setting of massive multi-label data which, as the authors highlight, is an increasingly import problem in biology and healthcare. Strengths: The idea of using the hierarchical structure of the labels is innovative and well-motivated. The experimental design and description of the methods is excellent. Weaknesses: Overall the results are not consistently strong and there is a key baseline missing. The approach only seems help in the "rare label, small data" regime, which limits the applicability of the method but is still worthy of consideration. My biggest reservation is that the authors did not include a baseline where the classes are reweighted according to their frequency. Multilabel binary cross-entropy is very easy to modify to incorporate class weights (e.g. upweight the minority class for each label) and without this baseline I am unable to discern how well the method works relative to this simple baseline. One more dataset would also strengthen the results, and since I am suggesting more work I will also try to be helpful and be specific. Predicting mesh terms from abstracts would qualify as a massive multilabel task and there is plenty of public data available here: https://www.nlm.nih.gov/databases/download/pubmed_medline.html Finally, there is one relevant paper that the authors may wish to consider in their review section: https://www.biorxiv.org/content/early/2018/07/10/365965
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Using Ontologies To Improve Performance In Massively Multi-label Prediction
Massively multi-label prediction/classification problems arise in environments like health-care or biology where it is useful to make very precise predictions. One challenge with massively multi-label problems is that there is often a long-tailed frequency distribution for the labels, resulting in few positive examples for the rare labels. We propose a solution to this problem by modifying the output layer of a neural network to create a Bayesian network of sigmoids which takes advantage of ontology relationships between the labels to help share information between the rare and the more common labels. We apply this method to the two massively multi-label tasks of disease prediction (ICD-9 codes) and protein function prediction (Gene Ontology terms) and obtain significant improvements in per-label AUROC and average precision.
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>



 This is a clear and well written paper that attempts to improve our ability to predict in the setting of massive multi-label data which, as the authors highlight, is an increasingly import problem in biology and healthcare. Strengths: The idea of using the hierarchical structure of the labels is innovative and well-motivated. The experimental design and description of the methods is excellent. Weaknesses: Overall the results are not consistently strong and there is a key baseline missing. The approach only seems help in the "rare label, small data" regime, which limits the applicability of the method but is still worthy of consideration. My biggest reservation is that the authors did not include a baseline where the classes are reweighted according to their frequency. Multilabel binary cross-entropy is very easy to modify to incorporate class weights (e.g. upweight the minority class for each label) and without this baseline I am unable to discern how well the method works relative to this simple baseline. One more dataset would also strengthen the results, and since I am suggesting more work I will also try to be helpful and be specific. Predicting mesh terms from abstracts would qualify as a massive multilabel task and there is plenty of public data available here: https://www.nlm.nih.gov/databases/download/pubmed_medline.html Finally, there is one relevant paper that the authors may wish to consider in their review section: https://www.biorxiv.org/content/early/2018/07/10/365965
assistant
 This is a clear and well written paper that attempts to improve our ability to predict in the setting of massive multi-label data which, as the authors highlight, is an increasingly import problem in biology and healthcare. Strengths: The idea of using the hierarchical structure of the labels is innovative and well-motivated. The experimental design and description of the methods is excellent. Weaknesses: Overall the results are not consistently strong and there is a key baseline missing. The approach only seems help in the "rare label, small data" regime, which limits the applicability of the method but is still worthy of consideration. My biggest reservation is that the authors did not include a baseline where the classes are reweighted according to their frequency. Multilabel binary cross-entropy is very easy to modify to incorporate class weights (e.g. upweight the minority class for each label) and without this baseline I am unable to discern how well the method works relative to this simple baseline. One more dataset would also strengthen the results, and since I am suggesting more work I will also try to be helpful and be specific. Predicting mesh terms from abstracts would qualify as a massive multilabel task and there is plenty of public data available here: https://www.nlm.nih.gov/databases/download/pubmed_medline.html Finally, there is one relevant paper that the authors may wish to consider in their review section: https://www.biorxiv.org/content/early/2018/07/10/365965