1. Sensitivity analysis of RF+clust for leave-one-problem-out performance predictionAna Nikolikj, Michal Pluhacek, Carola Doerr, Peter Korošec, Tome Eftimov, 2023, published scientific conference contribution Keywords: automated performance prediction, autoML, single-objective black-box optimization, zero-shot learning
Published in DiRROS: 13.11.2023; Views: 111; Downloads: 51
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2. MsGEN : measuring generalization of nutrient value prediction across different recipe datasetsGordana Ispirova, Tome Eftimov, Sašo Džeroski, Barbara Koroušić-Seljak, 2023, original scientific article Abstract: 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.
Keywords: ML pipeline, predictive modeling, nutrient prediction, recipe datasets
Published in DiRROS: 25.09.2023; Views: 218; Downloads: 111
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3. Algorithm instance footprint : separating easily solvable and challenging problem instancesAna Nikolikj, Sašo Džeroski, Mario Andrés Muñoz, Carola Doerr, Peter Korošec, Tome Eftimov, 2023, published scientific conference contribution Keywords: black-box optimization, algorithms, problem instances, machine learning
Published in DiRROS: 15.09.2023; Views: 143; Downloads: 89
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4. Assessing the generalizability of a performance predictive modelAna Nikolikj, Gjorgjina Cenikj, Gordana Ispirova, Diederick Vermetten, Ryan Dieter Lang, Andries Petrus Engelbrecht, Carola Doerr, Peter Korošec, Tome Eftimov, 2023, published scientific conference contribution Keywords: algorithms, predictive models, machine learning
Published in DiRROS: 15.09.2023; Views: 159; Downloads: 103
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6. DynamoRep : trajectory-based population dynamics for classification of black-box optimization problemsGjorgjina Cenikj, Gašper Petelin, Carola Doerr, Peter Korošec, Tome Eftimov, 2023, published scientific conference contribution Keywords: black-box single-objective optimization, optimization problem classification, problem representation, meta-learning
Published in DiRROS: 30.08.2023; Views: 174; Downloads: 126
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7. FooDis : a food-disease relation mining pipelineGjorgjina Cenikj, Tome Eftimov, Barbara Koroušić-Seljak, 2023, original scientific article Abstract: Nowadays, it is really important and crucial to follow the new biomedical knowledge that is presented in scientific literature. To this end, Information Extraction pipelines can help to automatically extract meaningful relations from textual data that further require additional checks by domain experts. In the last two decades, a lot of work has been performed for extracting relations between phenotype and health concepts, however, the relations with food entities which are one of the most important environmental concepts have never been explored. In this study, we propose FooDis, a novel Information Extraction pipeline that employs state-of-the-art approaches in Natural Language Processing to mine abstracts of biomedical scientific papers and automatically suggests potential cause or treat relations between food and disease entities in different existing semantic resources. A comparison with already known relations indicates that the relations predicted by our pipeline match for 90% of the food-disease pairs that are common in our results and the NutriChem database, and 93% of the common pairs in the DietRx platform. The comparison also shows that the FooDis pipeline can suggest relations with high precision. The FooDis pipeline can be further used to dynamically discover new relations between food and diseases that should be checked by domain experts and further used to populate some of the existing resources used by NutriChem and DietRx.
Keywords: text mining, relation extraction, named entity recognition, named entity linking, food-disease relations
Published in DiRROS: 25.05.2023; Views: 223; Downloads: 92
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8. From language models to large-scale food and biomedical knowledge graphsGjorgjina Cenikj, Lidija Strojnik, Risto Angelski, Nives Ogrinc, Barbara Koroušić-Seljak, Tome Eftimov, 2023, original scientific article Abstract: Knowledge about the interactions between dietary and biomedical factors is scattered throughout uncountable research articles in an unstructured form (e.g., text, images, etc.) and requires automatic structuring so that it can be provided to medical professionals in a suitable format. Various biomedical knowledge graphs exist, however, they require further extension with relations between food and biomedical entities. In this study, we evaluate the performance of three state-of-the-art relation-mining pipelines (FooDis, FoodChem and ChemDis) which extract relations between food, chemical and disease entities from textual data. We perform two case studies, where relations were automatically extracted by the pipelines and validated by domain experts. The results show that the pipelines can extract relations with an average precision around 70%, making new discoveries available to domain experts with reduced human effort, since the domain experts should only evaluate the results, instead of finding, and reading all new scientific papers.
Keywords: biomedical knowledge graphs, relation-mining pipelines, relation extraction, validation
Published in DiRROS: 17.05.2023; Views: 214; Downloads: 95
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