Multidisciplinary, Artificial Intelligence, Computational Mathematics, Health Informatics
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Scopus Publications
Scopus Publications
Digital health and artificial intelligence: a research approach to enable sustainable and personalised local healthcare Monica Moroni, Lisa Novello, Giulia Malfatti, Lorenzo Gios, Roberto Bonmassari, Maurizio Del Greco, Massimiliano Maines, Michele Moretti, Sandro Inchiostro, Federica Romanelli, Elisabetta Racano, Tania Elena Maggi, Valentina Fiabane, Adele Compagnone, Lorena Filippi, Roberta Pasquini, Marta Betta, Lucia Pavanello, Andrea Manica, Diego Cagol, Enrico Santoprete, Simone Cecchetto, Giorgia Bincoletto, Diego Conforti, Andrea Nicolini, Giuseppe Jurman Health Policy and Technology, 2026 Background The integration of Artificial Intelligence (AI) into healthcare services and technologies offers substantial potential for personalised medicine. The Autonomous Province of Trento (Italy) provides a unique setting for AI-driven healthcare research, due to its unified healthcare system, advanced IT infrastructure, and strong public-private collaborations. This paper explores an initiative aimed at improving healthcare accessibility and promoting innovation through AI in three clinical domains: Cardiology, Diabetic Retinopathy, and Paediatric Ophthalmology. Methods The project employs a structured approach, involving specialised working groups addressing clinical needs, AI techniques, legal and ethical compliance and data management. The initiative aims to develop predictive models aligned with European and national data protection regulations. Results Three primary clinical objectives were defined: estimating individual risk profiles in heart failure patients, personalising screening intervals for diabetic retinopathy, and supporting early diagnosis of anterior segment opacities in infants. Data relevant for the selected outcomes were identified. A dedicated platform for compliant, secure and structured access to data was developed. A data analysis plan was designed, including data processing, models selection, optimization and evaluation. All research protocols were approved by the local Ethics Committee. Discussion The initiative investigates the AI potential to improve clinical outcomes and establish a sustainable, personalised healthcare system. Key challenges include data accessibility, regulatory compliance, and adherence to ethical standards. The project's comprehensive framework offers a model for broader applications. Future research will focus on model validation and expanding the initiative to other clinical domains. Public Interest Summary This article presents the "Digital Health and Artificial Intelligence" project, an initiative funded by The Autonomous Province of Trento (Italy) to enhance healthcare accessibility and foster innovative healthcare models using technology and Artificial Intelligence (AI). The current work presents the design and preparatory work for the implementation of three AI-based solutions for research purposes, encompassing three areas: i) Cardiology, ii) Diabetic Retinopathy, and iii) Paediatric Ophthalmology. The paper outlines the legal and organizational frameworks, mathematical modelling and data management emphasising the necessity of cross-disciplinary endeavour and collaboration. Overall, this project represents a forward-looking initiative promoting research conducted on citizen data to address healthcare needs through innovative AI-driven approaches in line with legal and ethical standards.
The advantages of our proposed Saturn coefficient over continuity and trustworthiness for UMAP dimensionality reduction evaluation Davide Chicco, Simone Melzi, Francesca Gasparini, Giuseppe Jurman Peerj Computer Science, 2026 Understanding the structure of a dataset is an easy task when the dimensions are two or three, but it can become extremely difficult when a dataset consists of tens, hundreds, or thousands of variables. Dimensionality reduction methods are computational techniques with solid mathematical foundations that allow for the projection of high-dimensional datasets into smaller data spaces. These low-dimensional representations of the original data, usually consisting of two variables, can then be plotted and inspected by researchers to gain an understanding of the original data structure. Uniform Manifold Approximation and Projection (UMAP) is one of the most effective and popular algorithms for dimensionality reduction, and has been proven effective on biomedical datasets, in particular. Even though UMAP is commonly utilized by thousands of researchers worldwide, no consensus has been reached on how to assess the output of dimensionality reduction informatively: to date, researchers often evaluate UMAP’s outcomes by eyeballing its two-dimensional plots each time. Of course, this approach is rather arbitrary, as different individuals might interpret a 2D plot in a different way. Some numerical coefficients for assessing UMAP’s conservation of global and local structure exist (continuity and trustworthiness, respectively), but they suffer from several flaws and can be misleading in multiple cases. To address these issues, we present here our Saturn coefficient, a new simple statistical metric that expresses the conservation of local structure and the conservation of global structure in UMAP through a real value ranging from 0 (no preservation) to 1 (complete preservation). In this study, we describe the rationale behind our Saturn coefficient and validate its results compared to continuity and trustworthiness on four artificial datasets and ten real-world biomedical datasets. Additionally, we propose a novel validation procedure based on the preservation of the clusters found by HDBSCAN (hierarchical density-based spatial clustering of applications with noise) in the original dataset within its dimensionality reduction representation (HDBSCANess). Our results demonstrate the validity of our Saturn coefficient across all artificial datasets and in seven out of fifteen real-world biomedical datasets. We therefore recommend the use of our Saturn coefficient to anyone wishing to assess UMAP results: our statistic, for example, can be used to test several sets of UMAP hyperparameters and to select the best configuration among them. Moreover, we also provide the software implementation of our Saturn coefficient as a standalone R package openly available on CRAN at https://doi.org/10.32614/CRAN.package.SaturnCoefficient . SaturnCoefficient and as a standalone Python package openly available on PyPI at https://pypi.org/project/SaturnScore .
Coherent cross-modal generation of synthetic biomedical data to advance multimodal precision medicine Raffaele Marchesi, Nicolò Lazzaro, Walter Endrizzi, Gianluca Leonardi, Matteo Pozzi, Flavio Ragni, Stefano Bovo, Monica Moroni, Venet Osmani, Giuseppe Jurman Plos Computational Biology, 2026 Integration of multimodal, multi-omics data is critical for advancing precision medicine, yet its application is frequently limited by incomplete datasets where one or more modalities are missing. To address this challenge, we developed a generative framework capable of synthesizing any missing modality from an arbitrary subset of available modalities. We introduce Coherent Denoising, a novel ensemble-based generative diffusion method that aggregates predictions from multiple specialized, single-condition models and enforces consensus during the sampling process. We compare this approach against a multi-condition, generative model that uses a flexible masking strategy to handle arbitrary subsets of inputs. The results show that our architectures successfully generate high-fidelity data that preserve the complex biological signals required for downstream tasks. We demonstrate that the generated synthetic data can be used to maintain the performance of predictive models on incomplete patient profiles and can leverage counterfactual analysis to guide the prioritization of diagnostic tests. We validated the framework’s efficacy on a large-scale multimodal, multi-omics cohort from The Cancer Genome Atlas (TCGA) of over 10,000 samples spanning across 20 tumor types, using data modalities such as copy-number alterations (CNA), transcriptomics (RNA-Seq), proteomics (RPPA), and histopathology (WSI). This work establishes a robust and flexible generative framework to address sparsity in multimodal datasets, providing a key step toward improving precision oncology.
Comment on “Using genomic data and machine learning to predict antibiotic resistance: A tutorial paper” Davide Chicco, Giuseppe Jurman Plos Computational Biology, 2025 A recent study by Faye Orcales and colleagues proposes a teaching curriculum on supervised machine learning applied to genomics data aimed at predicting antibiotic resistance. The article describes a traditional machine learning pipeline step-by-step in a way that is accessible to anyone, including novices. However, the authors provide a misleading piece of advice in the “Evaluating model performance” section, where they recommend that readers use accuracy and the F1 score for binary classification. We write this short formal comment on that article to reaffirm and explain why accuracy and the F1 score should be avoided in the evaluation of binary classification and why the Matthews correlation coefficient (MCC) should be employed instead. We also take this opportunity to warn readers about the dangers of k -fold cross-validation, which is suggested as a standard method for dividing data into training set and test set, but has several flaws and pitfalls.
The Venus score for the assessment of the quality and trustworthiness of biomedical datasets Davide Chicco, Alessandro Fabris, Giuseppe Jurman Biodata Mining, 2025 Biomedical datasets are the mainstays of computational biology and health informatics projects, and can be found on multiple data platforms online or obtained from wet-lab biologists and physicians. The quality and the trustworthiness of these datasets, however, can sometimes be poor, producing bad results in turn, which can harm patients and data subjects. To address this problem, policy-makers, researchers, and consortia have proposed diverse regulations, guidelines, and scores to assess the quality and increase the reliability of datasets. Although generally useful, however, they are often incomplete and impractical. The guidelines of Datasheets for Datasets, in particular, are too numerous; the requirements of the Kaggle Dataset Usability Score focus on non-scientific requisites (for example, including a cover image); and the European Union Artificial Intelligence Act (EU AI Act) sets forth sparse and general data governance requirements, which we tailored to datasets for biomedical AI. Against this backdrop, we introduce our new Venus score to assess the data quality and trustworthiness of biomedical datasets. Our score ranges from 0 to 10 and consists of ten questions that anyone developing a bioinformatics, medical informatics, or cheminformatics dataset should answer before the release. In this study, we first describe the EU AI Act, Datasheets for Datasets, and the Kaggle Dataset Usability Score, presenting their requirements and their drawbacks. To do so, we reverse-engineer the weights of the influential Kaggle Score for the first time and report them in this study. We distill the most important data governance requirements into ten questions tailored to the biomedical domain, comprising the Venus score. We apply the Venus score to twelve datasets from multiple subdomains, including electronic health records, medical imaging, microarray and bulk RNA-seq gene expression, cheminformatics, physiologic electrogram signals, and medical text. Analyzing the results, we surface fine-grained strengths and weaknesses of popular datasets, as well as aggregate trends. Most notably, we find a widespread tendency to gloss over sources of data inaccuracy and noise, which may hinder the reliable exploitation of data and, consequently, research results. Overall, our results confirm the applicability and utility of the Venus score to assess the trustworthiness of biomedical data.
A simple guide to the use of Student’s t-test, Mann-Whitney U test, Chi-squared test, and Kruskal-Wallis test in biostatistics Davide Chicco, Andrea Sichenze, Giuseppe Jurman Biodata Mining, 2025 In an age when machine learning and artificial intelligence are broadly employed, traditional statistics can still provide insightful information and results quickly and at a low computational cost. Statistics, in fact, offers many useful tools to researchers, including a series of univariate statistical tests that can identify relationships between pairs of numeric samples: Student's t-test, Mann-Whitney U test, Chi-squared test, and Kruskal-Wallis test. These tests generate several outcomes, including probability values (p-values) that can express a numerical quantity which accepts or rejects the null hypothesis, based on a certain threshold used. Although effective, these tests are often misused or employed in the wrong contexts, especially among biostatistics studies. Many scientific researchers do not seem to know how to choose one test over the others, and this misuse can lead to incorrect results and wrong conclusions. Here we present a simple theoretical and practical guide to the use of these four tests, first describing their theoretical properties and then displaying the results obtained by applying these tests to real-world medical datasets. Eventually, we explain when and how to use each test based on the data types of the samples considered. Our study can have a strong impact on scientific research by potentially influencing future studies involving these tests. Our recommendations, in turn, can help researchers produce more reliable and sound scientific results, thus increasing the quality of multiple scientific studies across various fields.
Neuropsychological tests and machine learning: identifying predictors of MCI and dementia progression Carlotta Cazzolli, Marco Chierici, Monica Dallabona, Chiara Guella, Giuseppe Jurman Aging Clinical and Experimental Research, 2025 Background Early prediction of progression in dementia is of major importance for providing patients with adequate clinical care, with considerable impact on the organization of the whole healthcare system. Aims The main task is tailoring robust and consolidated machine learning models to detect which neuropsychological tests are more effective in predicting a patient’s mental status. In a translational medicine perspective, such identification tool should find its place in the clinician’s toolbox as a support throughout his daily diagnostic routine. A second objective involves predicting the patient’s diagnosis based on the results of the cognitive assessment. Methods 281 patients with MCI or dementia diagnosis were assessed through 14 commonly administered neuropsychological tests designed to evaluate different cognitive domains. A suite of machine learning models, trained on different subsets of data, was used to detect the most informative tests and to predict the patient’s diagnosis. Two external validation datasets containing MMSE and FAB tests were involved in this second task. Results The tests qualitatively and statistically associated to a cognitive decline are MMSE, FAB, BSTR, AM, and VSF, of which at least three were considered the most informative also by machine learning. 73% average accuracy was obtained in the diagnosis prediction on three subsets of original and external data. Discussion Detecting the most informative tests could reduce the visits’ time and prevent the cognitive assessment from being biased by external factors. Machine learning models’ prediction represents a useful baseline for the clinician’s actual diagnosis and a reliable insight into the future development of the patient’s cognitive status.
Machine Learning Analysis Applied to Prediction of Early Progression Independent of Relapse Activity in Multiple Sclerosis Patients Valentina Poretto, Walter Endrizzi, Matteo Betti, Stefano Bovo, Angelo Bellinvia, Flavio Ragni, Caterina Lapucci, Monica Moroni, Sabrina Marangoni, Emilio Portaccio, Chiara Longo, Lorenzo Gios, Marco Chierici, Giuseppe Jurman, Bruno Giometto, Matilde Inglese, Venet Osmani, Manuela Marenco, Antonio Uccelli, Maria Pia Amato European Journal of Neurology, 2025 Background Predicting prognosis in people with multiple sclerosis (pwMS) at early disease stages still remains an unmet need. Machine learning (ML) strategies demonstrated good reliability when applied for prediction in medicine. This study aimed at developing a predictive algorithm comparing different ML approaches, by using routine demographic, clinical and radiological data from a large multicentric cohort of newly diagnosed pwMS. Methods Demographic, clinical, radiological and biochemical data were retrospectively collected at three Italian MS centers at baseline and four timepoints thereafter (6, 12, 24, and 36 months). Data from the first evaluation and subsequent 2‐year follow‐up were analyzed, comparing different ML models (Random Forest, Extra Trees, XGBoost, Logistic Regression and Support Vector Classifier) to predict progression independent of relapse activity (PIRA) at year 3. To understand how features impacted the selected model's output, a ML explainability analysis was performed on the whole cohort and on specific subsets of patients, those aged under 45 and those NEDA‐3 at the 2‐year follow‐up. Results Data from 719 pwMS (age 34.6 ± 11.2 years); female sex 501 (70%) were analyzed. Ninety‐two pwMS (13%) developed PIRA at year 3. Random Forest achieved the highest score, with a test set area under the ROC curve (AUC) of 0.75 ± 0.06. Features with the highest predictive impact were Expanded Disability Status Scale at 24 months, age at symptom onset and disease duration at baseline. Conclusion Our results showed the feasibility of applying ML techniques to predict short‐term PIRA in newly diagnosed pwMS by using routine clinical practice data, paving the way for tailored and personalized approaches.
Mapping B cells and the immune landscape of tertiary lymphoid structures reveals their clinical impact in neuroblastoma Ombretta Melaiu, Marco Chierici, Paula Gragera, Nicolò Lazzaro, Lucia L Petrilli, Judith Wienke, Francisca J Bergsma, Bronte Manouk Verhoeven, Cristiano De Stefanis, Valentina D’Oria, Maria C Benedetti, Giovanni Barillari, Rita Alaggio, Maria Antonietta De Ioris, Maria Vinci, Ninib Baryawno, Rita Carsetti, Giuseppe Jurman, Jan J Molenaar, Franco Locatelli, Doriana Fruci Journal for Immunotherapy of Cancer, 2025 Background Immunotherapy has transformed cancer treatment, highlighting the importance of effective antitumor immunity to fight cancer. However, its success in pediatric cancer remains limited, underscoring the urgent need to identify new immunotherapeutic targets. In this study, we explored the clinical relevance of B cells and tertiary lymphoid structures (TLS) in neuroblastoma (NB), a pediatric tumor with a heterogeneous immune landscape. Methods We analyzed 87 treatment-naïve NB specimens, spanning both localized and metastatic disease previously characterized for T-cell and dendritic cell (DC) infiltration. B cells were detected by immunohistochemistry, and plasma cells were quantified using multiple immunofluorescence. Spatial organization and functional status of immune cells within TLSs were assessed by imaging mass cytometry using a 29-antibody panel. In parallel, gene expression profiles were obtained through NanoString PanCancer Immune Profiling and further validated using publicly available bulk and single-cell RNA-sequencing data from untreated and treated NB samples. These transcriptomic datasets were used to support protein-level findings and to identify prognostic gene signatures. Results B-cell infiltration in NB tumors strongly correlated with the presence of T cells and DCs at both protein and transcriptomic levels, and was associated with improved prognosis. Similar to other solid tumors, B cells in NB were either scattered throughout the tumor or organized into TLSs of varying maturity. Spatial proteomic and transcriptomic analyses revealed that localized tumors often contain mature TLSs, with functional B cells able to antigen presentation and immunoglobulin expression, alongside high cytotoxic T cells. In contrast, metastatic tumors primarily exhibited immature TLSs, with evidence of B-cell and T-cell dysfunction. Importantly, we identified gene signatures associated with B cells and TLSs that not only predicted survival in NB but were also prognostic in multiple adult cancers. Conclusions Our findings highlight a central role for B cells and TLSs in shaping the immune microenvironment of NB. Their presence and maturation status are linked to clinical outcome, suggesting their potential as prognostic biomarkers and targets for novel immunotherapeutic strategies in pediatric oncology.
Bottlenecks in advancing and applying multiomic data integration—common data resources as rate-limiting drivers—the high-impact use case of atherosclerotic cardiovascular disease Stephanie Bezzina Wettinger, Kanita Karaduzovic-Hadziabdic, Ritienne Attard, Rosienne Farrugia, Brooke N Wolford, Marco Chierici, Giuseppe Jurman, Panagiotis Alexiou, José L Peñalvo, Rafael S Costa, José Basílio, František Sabovčik, Rui Vitorino, Johannes A Schmid, Rajesh Shigdel, Baiba Vilne, Artemis G Hatzigeorgiou, Miron Sopic, Yvan Devaux, Paolo Magni, Maria Tellez-Plaza, David P Kreil, Aleksandra Gruca Briefings in Bioinformatics, 2025 Despite striking successes in identifying novel biomarkers for improved patient stratification and predicting disease progression, numerous challenges remain in the effective integration and exploitation of multiomic data in biomedical applications beyond cancer, for which most bioinformatics strategies are developed and validated. That focus on cancer severely limits the effective development and advancement of algorithms in machine learning and artificial intelligence that do not suffer degraded out-of-domain performance. Generalizability and interpretability of models, however, are also required for robust insights that may translate into clinical practice. Work across different independent datasets is critical for establishing models robust towards unwanted variation in assays, protocols, and cohort populations. Disease-specific context like ethnicity, socioeconomic background, sex, lifestyle, disease phase, and tissue type also strongly affect molecular profiles. We here discuss atherosclerotic cardiovascular disease (ASCVD) as a high-impact non-cancer use case for the challenges remaining in the development and application of the latest bioinformatics approaches to multiomics data integration. ASCVD remains the leading cause of death globally. Disease aetiology, progression, and therapy outcome depend on a complex interplay of genetic, environmental, and lifestyle factors. Integrating these diverse data types effectively remains a challenge but holds transformative potential for personalized medicine. Discovery and access to data of sufficient diversity and extent form key bottlenecks. We here compile a first comprehensive overview of key data sets in ASCVD to complement the established cancer-focused resources as a foundation for future effective development and application of state-of-the-art bioinformatics tools for multiomic data integration.
Machine Learning Predicts Risk of Falls in Parkison's Disease Patients in a Multicenter Observational Study Maria Chiara Malaguti, Chiara Longo, Monica Moroni, Flavio Ragni, Stefano Bovo, Marco Chierici, Lorenzo Gios, Laura Avanzino, Roberta Marchese, Francesca Di Biasio, Matteo Pardini, Denise Cerne, Paola Mandich, Manuela Marenco, Antonio Uccelli, Bruno Giometto, Giuseppe Jurman, Venet Osmani, and European Journal of Neurology, 2025
Neuropsychological and clinical variables associated with cognitive trajectories in patients with Alzheimer's disease Marianna Riello, Monica Moroni, Stefano Bovo, Flavio Ragni, Manuela Buganza, Raffaella Di Giacopo, Marco Chierici, Lorenzo Gios, Matteo Pardini, Federico Massa, Monica Dallabona, Elisa Vanzetta, Cristina Campi, Michele Piana, Sara Garbarino, Manuela Marenco, Venet Osmani, Giuseppe Jurman, Antonio Uccelli, Bruno Giometto, and Frontiers in Aging Neuroscience, 2025
AI-Based Modular Warning Machine for Risk Identification in Proximity Healthcare Chiara Razzetta, Shahryar Noei, Federico Barbarossa, Edoardo Spairani, Monica Roascio, Elisa Barbi, Giulia Ciacci, Sara Sommariva, Sabrina Guastavino, Michele Piana, Matteo Lenge, Gabriele Arnulfo, Giovanni Magenes, Elvira Maranesi, Giulio Amabili, Anna Maria Massone, Federico Benvenuto, Giuseppe Jurman, Diego Sona, Cristina Campi Conference Proceedings 2025 IEEE International Conference on Metrology for Extended Reality Artificial Intelligence and Neural Engineering Metroxraine 2025, 2025
Forecasting daily total pollen concentrations on a global scale László Makra, Luca Coviello, Andrea Gobbi, Giuseppe Jurman, Cesare Furlanello, Mauro Brunato, Lewis H. Ziska, Jeremy J. Hess, Athanasios Damialis, Maria Pilar Plaza Garcia, Gábor Tusnády, Lilit Czibolya, István Ihász, Áron József Deák, Edit Mikó, Zita Dorner, Susan K. Harry, Nicolas Bruffaerts, Ann Packeu, Annika Saarto, Linnea Toiviainen, Maria Louna‐Korteniemi, Sanna Pätsi, Michel Thibaudon, Gilles Oliver, Athanasios Charalampopoulos, Despoina Vokou, Ewa Maria Przedpelska‐Wasowicz, Ellý Renée Guðjohnsen, Maira Bonini, Sevcan Celenk, Cumali Ozaslan, Jae‐Won Oh, Krista Sullivan, Linda Ford, Michelle Kelly, Estelle Levetin, Dorota Myszkowska, Elena Severova, Regula Gehrig, María Del Carmen Calderón‐Ezquerro, César Guerrero Guerra, Manuel Andres Leiva‐Guzmán, Germán Darío Ramón, Laura Beatriz Barrionuevo, Jonny Peter, Dilys Berman, Connie H. Katelaris, Janet M. Davies, Pamela Burton, Paul J. Beggs, Sandra María Vergamini, Rosa María Valencia‐Barrera, Claudia Traidl‐Hoffmann Allergy European Journal of Allergy and Clinical Immunology, 2024
Scoring Tumor-Infiltrating Lymphocytes in breast DCIS: A guideline-driven artificial intelligence approach Proceedings of Machine Learning Research, 2024
A temporally and spatially explicit, data-driven estimation of airborne ragweed pollen concentrations across Europe László Makra, István Matyasovszky, Gábor Tusnády, Lewis H. Ziska, Jeremy J. Hess, László G. Nyúl, Daniel S. Chapman, Luca Coviello, Andrea Gobbi, Giuseppe Jurman, Cesare Furlanello, Mauro Brunato, Athanasios Damialis, Athanasios Charalampopoulos, Heinz Müller-Schärer, Norbert Schneider, Bence Szabó, Zoltán Sümeghy, Anna Páldy, Donát Magyar, Karl-Christian Bergmann, Áron József Deák, Edit Mikó, Michel Thibaudon, Gilles Oliver, Roberto Albertini, Maira Bonini, Branko Šikoparija, Predrag Radišić, Mirjana Mitrović Josipović, Regula Gehrig, Elena Severova, Valentina Shalaboda, Barbara Stjepanović, Nicoleta Ianovici, Uwe Berger, Andreja Kofol Seliger, Ondřej Rybníček, Dorota Myszkowska, Katarzyna Dąbrowska-Zapart, Barbara Majkowska-Wojciechowska, Elzbieta Weryszko-Chmielewska, Łukasz Grewling, Piotr Rapiejko, Malgorzata Malkiewicz, Ingrida Šaulienė, Olexander Prykhodo, Anna Maleeva, Victoria Rodinkova, Olena Palamarchuk, Jana Ščevková, James M. Bullock Science of the Total Environment, 2023
Author Correction: Discovery of widespread transcription initiation at microsatellites predictable by sequence-based deep neural network (Nature Communications, (2021), 12, 1, (3297), 10.1038/s41467-021-23143-7) Mathys Grapotte, Manu Saraswat, Chloé Bessière, Christophe Menichelli, Jordan A. Ramilowski, Jessica Severin, Yoshihide Hayashizaki, Masayoshi Itoh, Michihira Tagami, Mitsuyoshi Murata, Miki Kojima-Ishiyama, Shohei Noma, Shuhei Noguchi, Takeya Kasukawa, Akira Hasegawa, Harukazu Suzuki, Hiromi Nishiyori-Sueki, Martin C. Frith, , Imad Abugessaisa, Stuart Aitken, Bronwen L. Aken, Intikhab Alam, Tanvir Alam, Rami Alasiri, Ahmad M. N. Alhendi, Hamid Alinejad-Rokny, Mariano J. Alvarez, Robin Andersson, Takahiro Arakawa, Marito Araki, Taly Arbel, John Archer, Alan L. Archibald, Erik Arner, Peter Arner, Kiyoshi Asai, Haitham Ashoor, Gaby Astrom, Magda Babina, J. Kenneth Baillie, Vladimir B. Bajic, Archana Bajpai, Sarah Baker, Richard M. Baldarelli, Adam Balic, Mukesh Bansal, Arsen O. Batagov, Serafim Batzoglou, Anthony G. Beckhouse, Antonio P. Beltrami, Carlo A. Beltrami, Nicolas Bertin, Sharmodeep Bhattacharya, Peter J. Bickel, Judith A. Blake, Mathieu Blanchette, Beatrice Bodega, Alessandro Bonetti, Hidemasa Bono, Jette Bornholdt, Michael Bttcher, Salim Bougouffa, Mette Boyd, Jeremie Breda, Frank Brombacher, James B. Brown, Carol J. Bult, A. Maxwell Burroughs, Dave W. Burt, Annika Busch, Giulia Caglio, Andrea Califano, Christopher J. Cameron, Carlo V. Cannistraci, Alessandra Carbone, Ailsa J. Carlisle, Piero Carninci, Kim W. Carter, Daniela Cesselli, Jen-Chien Chang, Julie C. Chen, Yun Chen, Marco Chierici, John Christodoulou, Yari Ciani, Emily L. Clark, Mehmet Coskun, Maria Dalby, Emiliano Dalla, Carsten O. Daub, Carrie A. Davis, Michiel J. L. de Hoon, Derek de Rie, Elena Denisenko, Bart Deplancke, Michael Detmar, Ruslan Deviatiiarov, Diego Di Bernardo, Alexander D. Diehl, Lothar C. Dieterich, Emmanuel Dimont, Sarah Djebali, Taeko Dohi, Jose Dostie, Finn Drablos, Albert S. B. Edge, Matthias Edinger, Anna Ehrlund, Karl Ekwall, Arne Elofsson, Mitsuhiro Endoh, Hideki Enomoto, Saaya Enomoto, Mohammad Faghihi, Michela Fagiolini, Mary C. Farach-Carson, Geoffrey J. Faulkner, Alexander Favorov, Ana Miguel Fernandes, Carmelo Ferrai, Alistair R. R. Forrest, Lesley M. Forrester, Mattias Forsberg, Alexandre Fort, Margherita Francescatto, Tom C. Freeman, Martin Frith, Shinji Fukuda, Manabu Funayama, Cesare Furlanello, Masaaki Furuno, Chikara Furusawa, Hui Gao, Iveta Gazova, Claudia Gebhard, Florian Geier, Teunis B. H. Geijtenbeek, Samik Ghosh, Yanal Ghosheh, Thomas R. Gingeras, Takashi Gojobori, Tatyana Goldberg, Daniel Goldowitz, Julian Gough, Dario Greco, Andreas J. Gruber, Sven Guhl, Roderic Guigo, Reto Guler, Oleg Gusev, Stefano Gustincich, Thomas J. Ha, Vanja Haberle, Paul Hale, Bjrn M. Hallstrom, Michiaki Hamada, Lusy Handoko, Mitsuko Hara, Matthias Harbers, Jennifer Harrow, Jayson Harshbarger, Takeshi Hase, Akira Hasegawa, Kosuke Hashimoto, Taku Hatano, Nobutaka Hattori, Ryuhei Hayashi, Yoshihide Hayashizaki, Meenhard Herlyn, Peter Heutink, Winston Hide, Kelly J. Hitchens, Shannon Ho Sui, Peter A. C. ’t Hoen, Chung Chau Hon, Fumi Hori, Masafumi Horie, Katsuhisa Horimoto, Paul Horton, Rui Hou, Edward Huang, Yi Huang, Richard Hugues, David Hume, Hans Ienasescu, Kei Iida, Tomokatsu Ikawa, Toshimichi Ikemura, Kazuho Ikeo, Norihiko Inoue, Yuri Ishizu, Yosuke Ito, Masayoshi Itoh, Anna V. Ivshina, Boris R. Jankovic, Piroon Jenjaroenpun, Rory Johnson, Mette Jorgensen, Hadi Jorjani, Anagha Joshi, Giuseppe Jurman, Bogumil Kaczkowski, Chieko Kai, Kaoru Kaida, Kazuhiro Kajiyama, Rajaram Kaliyaperumal, Eli Kaminuma, Takashi Kanaya, Hiroshi Kaneda, Philip Kapranov, Artem S. Kasianov, Takeya Kasukawa, Toshiaki Katayama, Sachi Kato, Shuji Kawaguchi, Jun Kawai, Hideya Kawaji, Hiroshi Kawamoto, Yuki I. Kawamura, Satoshi Kawasaki, Tsugumi Kawashima, Judith S. Kempfle, Tony J. Kenna, Juha Kere, Levon Khachigian, Hisanori Kiryu, Mami Kishima, Hiroyuki Kitajima, Toshio Kitamura, Hiroaki Kitano, Enio Klaric, Kjetil Klepper, S. Peter Klinken, Edda Kloppmann, Alan J. Knox, Yuichi Kodama, Yasushi Kogo, Miki Kojima, Soichi Kojima, Norio Komatsu, Hiromitsu Komiyama, Tsukasa Kono, Haruhiko Koseki, Shigeo Koyasu, Anton Kratz, Alexander Kukalev, Ivan Kulakovskiy, Anshul Kundaje, Hiroshi Kunikata, Richard Kuo, Tony Kuo, Shigehiro Kuraku, Vladimir A. Kuznetsov, Tae Jun Kwon, Matt Larouche, Timo Lassmann, Andy Law, Kim-Anh Le-Cao, Charles-Henri Lecellier, Weonju Lee, Boris Lenhard, Andreas Lennartsson, Kang Li, Ruohan Li, Berit Lilje, Leonard Lipovich, Marina Lizio, Gonzalo Lopez, Shigeyuki Magi, Gloria K. Mak, Vsevolod Makeev, Riichiro Manabe, Michiko Mandai, Jessica Mar, Kazuichi Maruyama, Taeko Maruyama, Elizabeth Mason, Anthony Mathelier, Hideo Matsuda, Yulia A. Medvedeva, Terrence F. Meehan, Niklas Mejhert, Alison Meynert, Norihisa Mikami, Akiko Minoda, Hisashi Miura, Yohei Miyagi, Atsushi Miyawaki, Yosuke Mizuno, Hiromasa Morikawa, Mitsuru Morimoto, Masaki Morioka, Soji Morishita, Kazuyo Moro, Efthymios Motakis, Hozumi Motohashi, Abdul Kadir Mukarram, Christine L. Mummery, Christopher J. Mungall, Yasuhiro Murakawa, Masami Muramatsu, Mitsuyoshi Murata, Kazunori Nagasaka, Takahide Nagase, Yutaka Nakachi, Fumio Nakahara, Kenta Nakai, Kumi Nakamura, Yasukazu Nakamura, Yukio Nakamura, Toru Nakazawa, Guy P. Nason, Chirag Nepal, Quan Hoang Nguyen, Lars K. Nielsen, Kohji Nishida, Koji M. Nishiguchi, Hiromi Nishiyori, Kazuhiro Nitta, Shuhei Noguchi, Shohei Noma, Cedric Notredame, Soichi Ogishima, Naganari Ohkura, Hiroshi Ohno, Mitsuhiro Ohshima, Takashi Ohtsu, Yukinori Okada, Mariko Okada-Hatakeyama, Yasushi Okazaki, Per Oksvold, Valerio Orlando, Ghim Sion Ow, Mumin Ozturk, Mikhail Pachkov, Triantafyllos Paparountas, Suraj P. Parihar, Sung-Joon Park, Giovanni Pascarella, Robert Passier, Helena Persson, Ingrid H. Philippens, Silvano Piazza, Charles Plessy, Ana Pombo, Fredrik Ponten, Stéphane Poulain, Thomas M. Poulsen, Swati Pradhan, Carolina Prezioso, Clare Pridans, Xiang-Yang Qin, John Quackenbush, Owen Rackham, Jordan Ramilowski, Timothy Ravasi, Michael Rehli, Sarah Rennie, Tiago Rito, Patrizia Rizzu, Christelle Robert, Marco Roos, Burkhard Rost, Filip Roudnicky, Riti Roy, Morten B. 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A verified genomic reference sample for assessing performance of cancer panels detecting small variants of low allele frequency Wendell Jones, Binsheng Gong, Natalia Novoradovskaya, Dan Li, Rebecca Kusko, Todd A. Richmond, Donald J. Johann, Halil Bisgin, Sayed Mohammad Ebrahim Sahraeian, Pierre R. Bushel, Mehdi Pirooznia, Katherine Wilkins, Marco Chierici, Wenjun Bao, Lee Scott Basehore, Anne Bergstrom Lucas, Daniel Burgess, Daniel J. Butler, Simon Cawley, Chia-Jung Chang, Guangchun Chen, Tao Chen, Yun-Ching Chen, Daniel J. Craig, Angela del Pozo, Jonathan Foox, Margherita Francescatto, Yutao Fu, Cesare Furlanello, Kristina Giorda, Kira P. Grist, Meijian Guan, Yingyi Hao, Scott Happe, Gunjan Hariani, Nathan Haseley, Jeff Jasper, Giuseppe Jurman, David Philip Kreil, Paweł Łabaj, Kevin Lai, Jianying Li, Quan-Zhen Li, Yulong Li, Zhiguang Li, Zhichao Liu, Mario Solís López, Kelci Miclaus, Raymond Miller, Vinay K. Mittal, Marghoob Mohiyuddin, Carlos Pabón-Peña, Barbara L. Parsons, Fujun Qiu, Andreas Scherer, Tieliu Shi, Suzy Stiegelmeyer, Chen Suo, Nikola Tom, Dong Wang, Zhining Wen, Leihong Wu, Wenzhong Xiao, Chang Xu, Ying Yu, Jiyang Zhang, Yifan Zhang, Zhihong Zhang, Yuanting Zheng, Christopher E. Mason, James C. Willey, Weida Tong, Leming Shi, Joshua Xu Genome Biology, 2021
Discovery of widespread transcription initiation at microsatellites predictable by sequence-based deep neural network Mathys Grapotte, Manu Saraswat, Chloé Bessière, Christophe Menichelli, Jordan A. Ramilowski, Jessica Severin, Yoshihide Hayashizaki, Masayoshi Itoh, Michihira Tagami, Mitsuyoshi Murata, Miki Kojima-Ishiyama, Shohei Noma, Shuhei Noguchi, Takeya Kasukawa, Akira Hasegawa, Harukazu Suzuki, Hiromi Nishiyori-Sueki, Martin C. Frith, , Imad Abugessaisa, Stuart Aitken, Bronwen L. Aken, Intikhab Alam, Tanvir Alam, Rami Alasiri, Ahmad M. N. Alhendi, Hamid Alinejad-Rokny, Mariano J. Alvarez, Robin Andersson, Takahiro Arakawa, Marito Araki, Taly Arbel, John Archer, Alan L. Archibald, Erik Arner, Peter Arner, Kiyoshi Asai, Haitham Ashoor, Gaby Astrom, Magda Babina, J. Kenneth Baillie, Vladimir B. Bajic, Archana Bajpai, Sarah Baker, Richard M. Baldarelli, Adam Balic, Mukesh Bansal, Arsen O. Batagov, Serafim Batzoglou, Anthony G. Beckhouse, Antonio P. Beltrami, Carlo A. 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AI Slipping on Tiles: Data Leakage in Digital Pathology Nicole Bussola, Alessia Marcolini, Valerio Maggio, Giuseppe Jurman, Cesare Furlanello Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2021
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The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance Charles Wang, Binsheng Gong, Pierre R Bushel, Jean Thierry-Mieg, Danielle Thierry-Mieg, Joshua Xu, Hong Fang, Huixiao Hong, Jie Shen, Zhenqiang Su, Joe Meehan, Xiaojin Li, Lu Yang, Haiqing Li, Paweł P Łabaj, David P Kreil, Dalila Megherbi, Stan Gaj, Florian Caiment, Joost van Delft, Jos Kleinjans, Andreas Scherer, Viswanath Devanarayan, Jian Wang, Yong Yang, Hui-Rong Qian, Lee J Lancashire, Marina Bessarabova, Yuri Nikolsky, Cesare Furlanello, Marco Chierici, Davide Albanese, Giuseppe Jurman, Samantha Riccadonna, Michele Filosi, Roberto Visintainer, Ke K Zhang, Jianying Li, Jui-Hua Hsieh, Daniel L Svoboda, James C Fuscoe, Youping Deng, Leming Shi, Richard S Paules, Scott S Auerbach, Weida Tong Nature Biotechnology, 2014
A machine learning pipeline for discriminant pathways identification Annalisa Barla, Giuseppe Jurman, Roberto Visintainer, Margherita Squillario, Michele Filosi, Samantha Riccadonna, Cesare Furlanello Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2012
Repeatability of published microarray gene expression analyses John P A Ioannidis, David B Allison, Catherine A Ball, Issa Coulibaly, Xiangqin Cui, Aedín C Culhane, Mario Falchi, Cesare Furlanello, Laurence Game, Giuseppe Jurman, Jon Mangion, Tapan Mehta, Michael Nitzberg, Grier P Page, Enrico Petretto, Vera van Noort Nature Genetics, 2009
A grid environment for high-throughput proteomics Mario Cannataro, Annalisa Barla, Roberto Flor, Giuseppe Jurman, Stefano Merler, Silvano Paoli, Giuseppe Tradigo, Pierangelo Veltri, Cesare Furlanello IEEE Transactions on Nanobioscience, 2007
Deriving the kernel from training data Stefano Merler, Giuseppe Jurman, Cesare Furlanello Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2007
Proteome profiling without selection bias A. Barla, B. Irler, S. Merler, G. Jurman, S. Paoli, C. Furlanello Proceedings IEEE Symposium on Computer Based Medical Systems, 2006
Strategies for containing an influenza pandemic: The case of Italy Stefano Merler, Giuseppe Jurman, Cesare Furlanello, Caterina Rizzo, Antonino Bella, Marco Massari, Marta Luisa Ciofi degli Atti 2006 1st Bio Inspired Models of Network Information and Computing Systems Bionetics, 2006
Semisupervised profiling of gene expressions and clinical data Silvano Paoli, Giuseppe Jurman, Davide Albanese, Stefano Merler, Cesare Furlanello Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2006
Exact bagging with k-Nearest neighbour classifiers Bruno Caprile, Stefano Merler, Cesare Furlanello, Giuseppe Jurman Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2004
Control of selection bias in microarray data analysis Minerva Biotecnologica, 2003
Gene Selection and Classification by Entropy-based Recursive Feature Elimination Proceedings of the International Joint Conference on Neural Networks, 2003
