SUMMARY: Network medicine leverages the quantification of information flow within sub-cellular networks to elucidate disease etiology and comorbidity, as well as to predict drug efficacy and identify potential therapeutic targets. However, current Network Medicine toolsets often lack computationally efficient data processing pipelines that support diverse scoring functions, network distance metrics, and null models. These limitations hamper their application in large-scale molecular screening, hypothesis testing, and ensemble modeling. To address these challenges, we introduce NetMedPy, a highly efficient and versatile computational package designed for comprehensive Network Medicine analyses. AVAILABILITY AND IMPLEMENTATION: NetMedPy is an open-source Python package under an MIT license. Source code, documentation, and installation instructions can be downloaded from https://github.com/menicgiulia/NetMedPy and https://pypi.org/project/NetMedPy. The package can run on any standard desktop computer or computing cluster.

In this study, we estimate the generalization of the performance of previously proposed predictive models for nutrient value prediction across different recipe datasets. For this purpose, we introduce a quantitative indicator that determines the level of generalization of using the developed predictive model for new unseen data not presented in the training process. On a predefined corpus of recipe embeddings from six publicly available recipe datasets (i.e., projecting them in the same meta-feature vector space), we train predictive models on one of the six recipe datasets and test the models on the rest of the datasets. In parallel, we define and calculate generalizability indexes which are numbers that indicate how generalizable a predictive model is i.e., how well will a predictive model learned on one dataset perform on another one not involved in the training. The evaluation results prove the validity of these indexes – their relation with the accuracy of the predictions. Further, we define three sampling techniques for selecting representative data instances that will cover all parts from the feature space uniformly (involving data from all datasets) and further will improve the generalization of a predictive model. We train predictive models with these generalized datasets and test them on instances from the six recipe datasets that are not selected and included in the generalized datasets. The results from the evaluation of these predictive models show improvement compared to the results from the predictive models trained on one recipe dataset and tested on the others separately.

Although recipe data are very easy to come by nowadays, it is really hard to find a complete recipe dataset – with list of ingredients, nutrient values per ingredients, and per recipe, allergens, etc. Recipe datasets are usually collected from social media websites where users post and publish recipes. Usually written with little to no structure, using both standardized and non-standardized units of measurement. We collect six different recipe datasets, publicly available, in different formats and some including data in different languages. Bringing all of these datasets to the needed format for applying a machine learning (ML) pipeline for nutrient prediction [1], [2], includes data normalization using dictionary based named entity recognition (NER), rule based NER, as well as conversions using external domain specific resources. From the list of ingredients, domain-specific embeddings are created using the same embedding space for all recipes – one ingredient dataset is generated. The result from this normalization process are two corpora – one with predefined ingredient embeddings and one with predefined recipe embeddings. On all six recipe dataset the ML pipeline is evaluated. The results from this use case also confirm that the embeddings merged using the domain heuristic yield better results than the baselines.

In this paper, we have proposed a new pipeline for landscape analysis of time-series machine learning datasets that enables us to better understand a benchmarking problem landscape, allows us to select a diverse benchmark datasets portfolio, and reduces the presence of performance assessment bias via bootstrapping evaluation. Combining a large multi-domain representation corpus of time-series specific features and the results of a large empirical study of time-series classification (TSC) benchmark, we showcase the capability of the pipeline to point out issues with non-redundancy and representativeness in the benchmark. By observing discrepancy between the empirical results of the bootstrap evaluation and recently adopted practices in TSC literature when introducing novel methods, we warn on the potentially harmful effects of tuning the methods on certain parts of the landscape (unless this is an explicit and desired goal of the study). Finally, we propose a set of datasets uniformly distributed across the landscape space one should consider when benchmarking novel TSC methods.

Human understanding and knowledge about food and nutrition is constantly evolving, and has significantly improved recently, one of the main contributor to this is data. The possibilities of gaining knowledge from food and nutrition-related data are yet to be explored. One of the most important information about food is nutrient content, which is very relevant for patients suffering from various diseases, professional athletes, and slowly part of everyday life of many for health or fitness goals. In this paper, we explore the effect of domain bias in a predictive study in the food and nutrition domain. Having a machine learning pipeline for predicting nutrient values with learned vector representations from short text description of recipes, we introduce domain knowledge before the prediction algorithms are applied. On a large corpus of recipe data containing short description and nutrient values we introduce word and paragraph embeddings, learn concept representations for the textual descriptions, introduce domain knowledge for clustering the data, and apply machine learning algorithms for predicting the nutrient content of the recipes. We explore the impact of the domain knowledge by introducing two different criteria of clustering the dataset - using graph embedding of the FoodEx2 codes, and using the traffic light labelling system from the Food Standards Agency; at the end we compare the two different criteria. The results from this study imply that inferring domain knowledge beforehand is crucial for the task of predicting nutrient content.

Although food and nutrition have been studied for centuries, modern nutritional science is surprisingly young. Human knowledge about food and nutrition has evolved drastically with time, and it has especially expanded the last few decades, with information and data being mass produced and available everywhere and in any form it is really easy to get overwhelmed and confused when it comes to what is right and what is wrong. Macronutrient assessment is a crucial task for individuals suffering from various diseases, and also very relevant for professional athletes, and nowadays it is becoming part of everyday life for many people, because of health or fitness reasons. Assessing the nutritional components of food is very challenging and requires reliant source of data. In this paper, we introduce an idea of finding similar recipes with regard to their macronutrient values based on learned recipe vector representation. On a scientifically proven dataset of recipe data containing description and macronutrient values we introduce word and paragraph embeddings, learn concept representations for the textual descriptions, proceed with calculating similarity between the embeddings, and then compare with the similarity between nutrient values. The results show a strong correlation between these two similarities. This study is a promising beginning for continuing in this direction-introducing multi-label classification or regression for predicting nutrient content of food.