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1. Validity of Feature Importance in Low-Performing Machine Learning for Tabular Biomedical Data

Author

Lee, Youngro, Baruzzo, Giacomo, Kim, Jeonghwan, Seo, Jongmo, and Di Camillo, Barbara

Subjects
Statistics - Machine Learning, Computer Science - Machine Learning
Abstract

In tabular biomedical data analysis, tuning models to high accuracy is considered a prerequisite for discussing feature importance, as medical practitioners expect the validity of feature importance to correlate with performance. In this work, we challenge the prevailing belief, showing that low-performing models may also be used for feature importance. We propose experiments to observe changes in feature rank as performance degrades sequentially. Using three synthetic datasets and six real biomedical datasets, we compare the rank of features from full datasets to those with reduced sample sizes (data cutting) or fewer features (feature cutting). In synthetic datasets, feature cutting does not change feature rank, while data cutting shows higher discrepancies with lower performance. In real datasets, feature cutting shows similar or smaller changes than data cutting, though some datasets exhibit the opposite. When feature interactions are controlled by removing correlations, feature cutting consistently shows better stability. By analyzing the distribution of feature importance values and theoretically examining the probability that the model cannot distinguish feature importance between features, we reveal that models can still distinguish feature importance despite performance degradation through feature cutting, but not through data cutting. We conclude that the validity of feature importance can be maintained even at low performance levels if the data size is adequate, which is a significant factor contributing to suboptimal performance in tabular medical data analysis. This paper demonstrates the potential for utilizing feature importance analysis alongside statistical analysis to compare features relatively, even when classifier performance is not satisfactory.

Published
2024

2. Effect of Clinical History on Predictive Model Performance for Renal Complications of Diabetes

Author

Cas, Davide Dei, Di Camillo, Barbara, Fadini, Gian Paolo, Sparacino, Giovanni, and Longato, Enrico

Subjects
Quantitative Biology - Quantitative Methods, Computer Science - Machine Learning
Abstract

Diabetes is a chronic disease characterised by a high risk of developing diabetic nephropathy, which, in turn, is the leading cause of end-stage chronic kidney disease. The early identification of individuals at heightened risk of such complications or their exacerbation can be of paramount importance to set a correct course of treatment. In the present work, from the data collected in the DARWIN-Renal (DApagliflozin Real-World evIdeNce-Renal) study, a nationwide multicentre retrospective real-world study, we develop an array of logistic regression models to predict, over different prediction horizons, the crossing of clinically relevant glomerular filtration rate (eGFR) thresholds for patients with diabetes by means of variables associated with demographic, anthropometric, laboratory, pathology, and therapeutic data. In doing so, we investigate the impact of information coming from patient's past visits on the model's predictive performance, coupled with an analysis of feature importance through the Boruta algorithm. Our models yield very good performance (AUROC as high as 0.98). We also show that the introduction of information from patient's past visits leads to improved model performance of up to 4%. The usefulness of past information is further corroborated by a feature importance analysis., Comment: 6 pages, 3 tables. In Proceedings of 19th International Conference on Computational Intelligence methods for Bioinformatics and Biostatistics (CIBB 2024), Benevento, Italy, September 4-6, 2024

Published
2024

3. Exploring the Impact of Environmental Pollutants on Multiple Sclerosis Progression

Author

Marinello, Elena, Tavazzi, Erica, Longato, Enrico, Bosoni, Pietro, Dagliati, Arianna, Vazifehdan, Mahin, Bellazzi, Riccardo, Trescato, Isotta, Guazzo, Alessandro, Vettoretti, Martina, Tavazzi, Eleonora, Ahmad, Lara, Bergamaschi, Roberto, Cavalla, Paola, Manera, Umberto, Chio, Adriano, and Di Camillo, Barbara

Subjects
Computer Science - Machine Learning
Abstract

Multiple Sclerosis (MS) is a chronic autoimmune and inflammatory neurological disorder characterised by episodes of symptom exacerbation, known as relapses. In this study, we investigate the role of environmental factors in relapse occurrence among MS patients, using data from the H2020 BRAINTEASER project. We employed predictive models, including Random Forest (RF) and Logistic Regression (LR), with varying sets of input features to predict the occurrence of relapses based on clinical and pollutant data collected over a week. The RF yielded the best result, with an AUC-ROC score of 0.713. Environmental variables, such as precipitation, NO2, PM2.5, humidity, and temperature, were found to be relevant to the prediction.

Published
2024

4. Challenges and opportunities for digital twins in precision medicine: a complex systems perspective

Author

De Domenico, Manlio, Allegri, Luca, Caldarelli, Guido, d'Andrea, Valeria, Di Camillo, Barbara, Rocha, Luis M., Rozum, Jordan, Sbarbati, Riccardo, and Zambelli, Francesco

Subjects
Physics - Biological Physics, Nonlinear Sciences - Adaptation and Self-Organizing Systems, Quantitative Biology - Quantitative Methods
Abstract

The adoption of digital twins (DTs) in precision medicine is increasingly viable, propelled by extensive data collection and advancements in artificial intelligence (AI), alongside traditional biomedical methodologies. However, the reliance on black-box predictive models, which utilize large datasets, presents limitations that could impede the broader application of DTs in clinical settings. We argue that hypothesis-driven generative models, particularly multiscale modeling, are essential for boosting the clinical accuracy and relevance of DTs, thereby making a significant impact on healthcare innovation. This paper explores the transformative potential of DTs in healthcare, emphasizing their capability to simulate complex, interdependent biological processes across multiple scales. By integrating generative models with extensive datasets, we propose a scenario-based modeling approach that enables the exploration of diverse therapeutic strategies, thus supporting dynamic clinical decision-making. This method not only leverages advancements in data science and big data for improving disease treatment and prevention but also incorporates insights from complex systems and network science, quantitative biology, and digital medicine, promising substantial advancements in patient care.

Published
2024

7. Intelligent Disease Progression Prediction: Overview of iDPP@CLEF 2024

Author

Birolo, Giovanni, Bosoni, Pietro, Faggioli, Guglielmo, Aidos, Helena, Bergamaschi, Roberto, Cavalla, Paola, Chiò, Adriano, Dagliati, Arianna, de Carvalho, Mamede, Di Nunzio, Giorgio Maria, Fariselli, Piero, Dominguez, Jose Manuel García, Gromicho, Marta, Guazzo, Alessandro, Longato, Enrico, Madeira, Sara C., Manera, Umberto, Marchesin, Stefano, Menotti, Laura, Silvello, Gianmaria, Tavazzi, Eleonora, Tavazzi, Erica, Trescato, Isotta, Vettoretti, Martina, Di Camillo, Barbara, Ferro, Nicola, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Goeuriot, Lorraine, editor, Mulhem, Philippe, editor, Quénot, Georges, editor, Schwab, Didier, editor, Di Nunzio, Giorgio Maria, editor, Soulier, Laure, editor, Galuščáková, Petra, editor, García Seco de Herrera, Alba, editor, Faggioli, Guglielmo, editor, and Ferro, Nicola, editor

Published
2024
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8. iDPP@CLEF 2024: The Intelligent Disease Progression Prediction Challenge

Author

Aidos, Helena, Bergamaschi, Roberto, Cavalla, Paola, Chiò, Adriano, Dagliati, Arianna, Di Camillo, Barbara, de Carvalho, Mamede Alves, Ferro, Nicola, Fariselli, Piero, Dominguez, Jose Manuel García, Madeira, Sara C., Tavazzi, Eleonora, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Goharian, Nazli, editor, Tonellotto, Nicola, editor, He, Yulan, editor, Lipani, Aldo, editor, McDonald, Graham, editor, Macdonald, Craig, editor, and Ounis, Iadh, editor

Published
2024
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12. Results and lessons learned from the sbv IMPROVER metagenomics diagnostics for inflammatory bowel disease challenge

Author

Khachatryan, Lusine, Xiang, Yang, Ivanov, Artem, Glaab, Enrico, Graham, Garrett, Granata, Ilaria, Giordano, Maurizio, Maddalena, Lucia, Piccirillo, Marina, Manipur, Ichcha, Baruzzo, Giacomo, Cappellato, Marco, Avot, Batiste, Stan, Adrian, Battey, James, Lo Sasso, Giuseppe, Boue, Stephanie, Ivanov, Nikolai V., Peitsch, Manuel C., Hoeng, Julia, Falquet, Laurent, Di Camillo, Barbara, Guarracino, Mario R., Ulyantsev, Vladimir, Sierro, Nicolas, and Poussin, Carine

Published
2023
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13. Cellular population dynamics shape the route to human pluripotency

Author

Panariello, Francesco, Gagliano, Onelia, Luni, Camilla, Grimaldi, Antonio, Angiolillo, Silvia, Qin, Wei, Manfredi, Anna, Annunziata, Patrizia, Slovin, Shaked, Vaccaro, Lorenzo, Riccardo, Sara, Bouche, Valentina, Dionisi, Manuela, Salvi, Marcello, Martewicz, Sebastian, Hu, Manli, Cui, Meihua, Stuart, Hannah, Laterza, Cecilia, Baruzzo, Giacomo, Schiebinger, Geoffrey, Di Camillo, Barbara, Cacchiarelli, Davide, and Elvassore, Nicola

Published
2023
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14. Intelligent Disease Progression Prediction: Overview of iDPP@CLEF 2023

Author

Faggioli, Guglielmo, Guazzo, Alessandro, Marchesin, Stefano, Menotti, Laura, Trescato, Isotta, Aidos, Helena, Bergamaschi, Roberto, Birolo, Giovanni, Cavalla, Paola, Chiò, Adriano, Dagliati, Arianna, de Carvalho, Mamede, Di Nunzio, Giorgio Maria, Fariselli, Piero, García Dominguez, Jose Manuel, Gromicho, Marta, Longato, Enrico, Madeira, Sara C., Manera, Umberto, Silvello, Gianmaria, Tavazzi, Eleonora, Tavazzi, Erica, Vettoretti, Martina, Di Camillo, Barbara, Ferro, Nicola, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Arampatzis, Avi, editor, Kanoulas, Evangelos, editor, Tsikrika, Theodora, editor, Vrochidis, Stefanos, editor, Giachanou, Anastasia, editor, Li, Dan, editor, Aliannejadi, Mohammad, editor, Vlachos, Michalis, editor, Faggioli, Guglielmo, editor, and Ferro, Nicola, editor

Published
2023
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15. DAVI: A Dataset for Automatic Variant Interpretation

Author

Longhin, Francesca, Guazzo, Alessandro, Longato, Enrico, Ferro, Nicola, Di Camillo, Barbara, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Arampatzis, Avi, editor, Kanoulas, Evangelos, editor, Tsikrika, Theodora, editor, Vrochidis, Stefanos, editor, Giachanou, Anastasia, editor, Li, Dan, editor, Aliannejadi, Mohammad, editor, Vlachos, Michalis, editor, Faggioli, Guglielmo, editor, and Ferro, Nicola, editor

Published
2023
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16. Dealing with Data Scarcity in Rare Diseases: Dynamic Bayesian Networks and Transfer Learning to Develop Prognostic Models of Amyotrophic Lateral Sclerosis

Author

Longato, Enrico, Tavazzi, Erica, Chió, Adriano, Mora, Gabriele, Sparacino, Giovanni, Di Camillo, Barbara, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Juarez, Jose M., editor, Marcos, Mar, editor, Stiglic, Gregor, editor, and Tucker, Allan, editor

Published
2023
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17. iDPP@CLEF 2023: The Intelligent Disease Progression Prediction Challenge

Author

Aidos, Helena, Bergamaschi, Roberto, Cavalla, Paola, Chiò, Adriano, Dagliati, Arianna, Di Camillo, Barbara, de Carvalho, Mamede Alves, Ferro, Nicola, Fariselli, Piero, Dominguez, Jose Manuel García, Madeira, Sara C., Tavazzi, Eleonora, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Kamps, Jaap, editor, Goeuriot, Lorraine, editor, Crestani, Fabio, editor, Maistro, Maria, editor, Joho, Hideo, editor, Davis, Brian, editor, Gurrin, Cathal, editor, Kruschwitz, Udo, editor, and Caputo, Annalina, editor

Published
2023
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18. Explainable Deep-Learning Model Reveals Past Cardiovascular Disease in Patients with Diabetes Using Free-Form Visit Reports

Author

Guazzo, Alessandro, Longato, Enrico, Fadini, Gian Paolo, Morieri, Mario Luca, Sparacino, Giovanni, Di Camillo, Barbara, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Nicosia, Giuseppe, editor, Ojha, Varun, editor, La Malfa, Emanuele, editor, La Malfa, Gabriele, editor, Pardalos, Panos, editor, Di Fatta, Giuseppe, editor, Giuffrida, Giovanni, editor, and Umeton, Renato, editor

Published
2023
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19. Discriminative power of DNA-based, volatilome, near infrared spectroscopy, elements and stable isotopes methods for the origin authentication of typical Italian mountain cheese using sPLS-DA modeling

Author

Cardin, Marco, Mounier, Jérôme, Coton, Emmanuel, Cardazzo, Barbara, Perini, Matteo, Bertoldi, Daniela, Pianezze, Silvia, Segato, Severino, Di Camillo, Barbara, Cappellato, Marco, Coton, Monika, Carraro, Lisa, Currò, Sarah, Lucchini, Rosaria, Mohammadpour, Hooriyeh, and Novelli, Enrico

Published
2024
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21. Artificial intelligence and statistical methods for stratification and prediction of progression in amyotrophic lateral sclerosis: A systematic review

Author

Tavazzi, Erica, Longato, Enrico, Vettoretti, Martina, Aidos, Helena, Trescato, Isotta, Roversi, Chiara, Martins, Andreia S., Castanho, Eduardo N., Branco, Ruben, Soares, Diogo F., Guazzo, Alessandro, Birolo, Giovanni, Pala, Daniele, Bosoni, Pietro, Chiò, Adriano, Manera, Umberto, de Carvalho, Mamede, Miranda, Bruno, Gromicho, Marta, Alves, Inês, Bellazzi, Riccardo, Dagliati, Arianna, Fariselli, Piero, Madeira, Sara C., and Di Camillo, Barbara

Published
2023
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23. Intelligent Disease Progression Prediction: Overview of iDPP@CLEF 2022

Author

Guazzo, Alessandro, Trescato, Isotta, Longato, Enrico, Hazizaj, Enidia, Dosso, Dennis, Faggioli, Guglielmo, Di Nunzio, Giorgio Maria, Silvello, Gianmaria, Vettoretti, Martina, Tavazzi, Erica, Roversi, Chiara, Fariselli, Piero, Madeira, Sara C., de Carvalho, Mamede, Gromicho, Marta, Chiò, Adriano, Manera, Umberto, Dagliati, Arianna, Birolo, Giovanni, Aidos, Helena, Di Camillo, Barbara, Ferro, Nicola, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Barrón-Cedeño, Alberto, editor, Da San Martino, Giovanni, editor, Degli Esposti, Mirko, editor, Sebastiani, Fabrizio, editor, Macdonald, Craig, editor, Pasi, Gabriella, editor, Hanbury, Allan, editor, Potthast, Martin, editor, Faggioli, Guglielmo, editor, and Ferro, Nicola, editor

Published
2022
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25. Stratification of amyotrophic lateral sclerosis patients: a crowdsourcing approach.

Author

Kueffner, Robert, Zach, Neta, Bronfeld, Maya, Norel, Raquel, Atassi, Nazem, Balagurusamy, Venkat, Di Camillo, Barbara, Chio, Adriano, Cudkowicz, Merit, Dillenberger, Donna, Garcia-Garcia, Javier, Hardiman, Orla, Hoff, Bruce, Knight, Joshua, Leitner, Melanie L, Li, Guang, Mangravite, Lara, Norman, Thea, Wang, Liuxia, ALS Stratification Consortium, Xiao, Jinfeng, Fang, Wen-Chieh, Peng, Jian, Yang, Chen, Chang, Huan-Jui, and Stolovitzky, Gustavo

Subjects
ALS Stratification Consortium
Abstract

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease where substantial heterogeneity in clinical presentation urgently requires a better stratification of patients for the development of drug trials and clinical care. In this study we explored stratification through a crowdsourcing approach, the DREAM Prize4Life ALS Stratification Challenge. Using data from >10,000 patients from ALS clinical trials and 1479 patients from community-based patient registers, more than 30 teams developed new approaches for machine learning and clustering, outperforming the best current predictions of disease outcome. We propose a new method to integrate and analyze patient clusters across methods, showing a clear pattern of consistent and clinically relevant sub-groups of patients that also enabled the reliable classification of new patients. Our analyses reveal novel insights in ALS and describe for the first time the potential of a crowdsourcing to uncover hidden patient sub-populations, and to accelerate disease understanding and therapeutic development.

Published
2019

31. Recurrent Neural Network to Predict Renal Function Impairment in Diabetic Patients via Longitudinal Routine Check-up Data

Author

Longato, Enrico, Fadini, Gian Paolo, Sparacino, Giovanni, Avogaro, Angelo, Di Camillo, Barbara, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Tucker, Allan, editor, Henriques Abreu, Pedro, editor, Cardoso, Jaime, editor, Pereira Rodrigues, Pedro, editor, and Riaño, David, editor

Published
2021
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40. An Optimized Data Structure for High Throughput 3D Proteomics Data: mzRTree

Author

Nasso, Sara, Silvestri, Francesco, Tisiot, Francesco, Di Camillo, Barbara, Pietracaprina, Andrea, and Toffolo, Gianna Maria

Subjects
Computer Science - Computational Engineering, Finance, and Science, Computer Science - Data Structures and Algorithms, Quantitative Biology - Quantitative Methods, J.3, E.2
Abstract

As an emerging field, MS-based proteomics still requires software tools for efficiently storing and accessing experimental data. In this work, we focus on the management of LC-MS data, which are typically made available in standard XML-based portable formats. The structures that are currently employed to manage these data can be highly inefficient, especially when dealing with high-throughput profile data. LC-MS datasets are usually accessed through 2D range queries. Optimizing this type of operation could dramatically reduce the complexity of data analysis. We propose a novel data structure for LC-MS datasets, called mzRTree, which embodies a scalable index based on the R-tree data structure. mzRTree can be efficiently created from the XML-based data formats and it is suitable for handling very large datasets. We experimentally show that, on all range queries, mzRTree outperforms other known structures used for LC-MS data, even on those queries these structures are optimized for. Besides, mzRTree is also more space efficient. As a result, mzRTree reduces data analysis computational costs for very large profile datasets., Comment: Paper details: 10 pages, 7 figures, 2 tables. To be published in Journal of Proteomics. Source code available at http://www.dei.unipd.it/mzrtree

Published
2010
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42. A large-scale evaluation of computational protein function prediction

Author

Radivojac, Predrag, Clark, Wyatt T, Oron, Tal Ronnen, Schnoes, Alexandra M, Wittkop, Tobias, Sokolov, Artem, Graim, Kiley, Funk, Christopher, Verspoor, Karin, Ben-Hur, Asa, Pandey, Gaurav, Yunes, Jeffrey M, Talwalkar, Ameet S, Repo, Susanna, Souza, Michael L, Piovesan, Damiano, Casadio, Rita, Wang, Zheng, Cheng, Jianlin, Fang, Hai, Gough, Julian, Koskinen, Patrik, Törönen, Petri, Nokso-Koivisto, Jussi, Holm, Liisa, Cozzetto, Domenico, Buchan, Daniel WA, Bryson, Kevin, Jones, David T, Limaye, Bhakti, Inamdar, Harshal, Datta, Avik, Manjari, Sunitha K, Joshi, Rajendra, Chitale, Meghana, Kihara, Daisuke, Lisewski, Andreas M, Erdin, Serkan, Venner, Eric, Lichtarge, Olivier, Rentzsch, Robert, Yang, Haixuan, Romero, Alfonso E, Bhat, Prajwal, Paccanaro, Alberto, Hamp, Tobias, Kaßner, Rebecca, Seemayer, Stefan, Vicedo, Esmeralda, Schaefer, Christian, Achten, Dominik, Auer, Florian, Boehm, Ariane, Braun, Tatjana, Hecht, Maximilian, Heron, Mark, Hönigschmid, Peter, Hopf, Thomas A, Kaufmann, Stefanie, Kiening, Michael, Krompass, Denis, Landerer, Cedric, Mahlich, Yannick, Roos, Manfred, Björne, Jari, Salakoski, Tapio, Wong, Andrew, Shatkay, Hagit, Gatzmann, Fanny, Sommer, Ingolf, Wass, Mark N, Sternberg, Michael JE, Škunca, Nives, Supek, Fran, Bošnjak, Matko, Panov, Panče, Džeroski, Sašo, Šmuc, Tomislav, Kourmpetis, Yiannis AI, van Dijk, Aalt DJ, Braak, Cajo JF ter, Zhou, Yuanpeng, Gong, Qingtian, Dong, Xinran, Tian, Weidong, Falda, Marco, Fontana, Paolo, Lavezzo, Enrico, Di Camillo, Barbara, Toppo, Stefano, Lan, Liang, Djuric, Nemanja, Guo, Yuhong, Vucetic, Slobodan, Bairoch, Amos, Linial, Michal, Babbitt, Patricia C, Brenner, Steven E, Orengo, Christine, and Rost, Burkhard

Subjects
Generic health relevance, Algorithms, Animals, Computational Biology, Databases, Protein, Exoribonucleases, Forecasting, Humans, Molecular Biology, Molecular Sequence Annotation, Proteins, Species Specificity, Biological Sciences, Technology, Medical and Health Sciences, Developmental Biology
Abstract

Automated annotation of protein function is challenging. As the number of sequenced genomes rapidly grows, the overwhelming majority of protein products can only be annotated computationally. If computational predictions are to be relied upon, it is crucial that the accuracy of these methods be high. Here we report the results from the first large-scale community-based critical assessment of protein function annotation (CAFA) experiment. Fifty-four methods representing the state of the art for protein function prediction were evaluated on a target set of 866 proteins from 11 organisms. Two findings stand out: (i) today's best protein function prediction algorithms substantially outperform widely used first-generation methods, with large gains on all types of targets; and (ii) although the top methods perform well enough to guide experiments, there is considerable need for improvement of currently available tools.

Published
2013

47. A Bayesian Network for Probabilistic Reasoning and Imputation of Missing Risk Factors in Type 2 Diabetes

Author

Sambo, Francesco, Facchinetti, Andrea, Hakaste, Liisa, Kravic, Jasmina, Di Camillo, Barbara, Fico, Giuseppe, Tuomilehto, Jaakko, Groop, Leif, Gabriel, Rafael, Tiinamaija, Tuomi, Cobelli, Claudio, Goebel, Randy, Series editor, Tanaka, Yuzuru, Series editor, Wahlster, Wolfgang, Series editor, Holmes, John H., editor, Bellazzi, Riccardo, editor, Sacchi, Lucia, editor, and Peek, Niels, editor

Published
2015
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49. Role of the Extracytoplasmic Function Sigma Factor SigE in the Stringent Response of Mycobacterium tuberculosis

Author

Baruzzo, Giacomo, primary, Serafini, Agnese, additional, Finotello, Francesca, additional, Sanavia, Tiziana, additional, Cioetto-Mazzabò, Laura, additional, Boldrin, Francesca, additional, Lavezzo, Enrico, additional, Barzon, Luisa, additional, Toppo, Stefano, additional, Provvedi, Roberta, additional, Manganelli, Riccardo, additional, and Di Camillo, Barbara, additional

Published
2023
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50. Results and Lessons Learned from the sbv IMPROVER Metagenomics Diagnostics for Inflammatory Bowel Disease Challenge

Author

Luxembourg Centre for Systems Biomedicine (LCSB): Biomedical Data Science (Glaab Group) [research center], Khachatryan, Lusine, Xiang, Yang, Ivanov, Artem, Glaab, Enrico, Graham, Garrett, Granata, Ilaria, Giordano, Maurizio, Maddalena, Lucia, Piccirillo, Marina, Manipur, Ichcha, Baruzzo, Giacomo, Cappellato, Marco, Avot, Batiste, Stan, Adrian, Battey, James, Lo Sasso, Giuseppe, Boue, Stephanie, Ivanov, Nikolai V., Peitsch, Manuel C., Hoeng, Julia, Falquet, Laurent, Di Camillo, Barbara, Guarracino, Mario, Ulyantsev, Vladimir, Sierro, Nicolas, Poussin, Carine, Luxembourg Centre for Systems Biomedicine (LCSB): Biomedical Data Science (Glaab Group) [research center], Khachatryan, Lusine, Xiang, Yang, Ivanov, Artem, Glaab, Enrico, Graham, Garrett, Granata, Ilaria, Giordano, Maurizio, Maddalena, Lucia, Piccirillo, Marina, Manipur, Ichcha, Baruzzo, Giacomo, Cappellato, Marco, Avot, Batiste, Stan, Adrian, Battey, James, Lo Sasso, Giuseppe, Boue, Stephanie, Ivanov, Nikolai V., Peitsch, Manuel C., Hoeng, Julia, Falquet, Laurent, Di Camillo, Barbara, Guarracino, Mario, Ulyantsev, Vladimir, Sierro, Nicolas, and Poussin, Carine

Abstract

A growing body of evidence links gut microbiota changes with inflammatory bowel disease (IBD), raising the potential benefit of exploiting metagenomics data for non-invasive IBD diagnostics. The sbv IMPROVER Metagenomics Diagnosis for Inflammatory Bowel Disease Challenge (MEDIC) investigated computational metagenomics methods for discriminating IBD and nonIBD subjects. Participants in this challenge were given independent training and test metagenomics data from IBD and nonIBD subjects, which could be wither either raw read data (sub-challenge 1, SC1) or processed taxonomy- and function-based profiles (sub-challenge 2, SC2). A total of 81 anonymized submissions were received between September 2019 and March 2020. Most participants’ predictions performed better than random predictions in classifying IBD vs nonIBD, Ulcerative Colitis (UC) vs nonIBD, and Crohn’s Disease (CD) vs nonIBD. However, discrimination between UC and CD remains challenging, with the classification quality similar to the set of random predictions. We analyzed the class prediction accuracy, the metagenomics features by the teams, and computational methods used. These results will be openly shared with the scientific community to help advance IBD research and illustrate the application of a range of computational methodologies for effective metagenomic classification.

Published
2023

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