2023

• Martorell Marugán, Jordi; Chierici, Marco; Jurman, Giuseppe; Alarcón-Riquelme, Marta E.; Carmona-Sáez, Pedro; Differential diagnosis of systemic lupus erythematosus and Sjögren’s syndrome using machine learning and multi-omics data; in « COMPUTERS IN BIOLOGY AND MEDICINE»; Vol 152; 2023; pp 106373

• Minnai, Francesca; Noci, Sara; Chierici, Marco; Cotroneo, Chiara; Bartolini, Barbara; Incarbone, Matteo; Tosi, Davide; Mattioni, Giovanni; Jurman, Giuseppe; Dragani, Tommaso A.; Colombo, Francesca; Genetic predisposition to lung adenocarcinoma outcome is a feature already present in patients’ non‐involved lung tissue; in « CANCER SCIENCE»; Vol 114;; 2023; pp 281 – 294

• Valeri, Federico; Bartolucci, Maurizio; Cantoni, Elena; Carpi, Roberto; Cisbani, Evaristo; Cupparo, Ilaria; Doria, Sandra; Gori, Cesare; Grigioni, Mauro; Lasagni, Lorenzo; Marconi, Alessandro; Mazzoni, Lorenzo Nicola; Miele, Vittorio; Pradella, Silvia; Risaliti, Guido; Sanguineti, Valentina; Sona, Diego; Vannucchi, Letizia; Taddeucci, Adriana; UNet and MobileNet CNN-based model observers for CT protocol optimization: comparative performance evaluation by means of phantom CT images; in « JOURNAL OF MEDICAL IMAGING»; Vol 10; 2023; pp S1190

  2022

• Chierici, Marco; Puica, Nicolae; Pozzi, Matteo; Capistrano, Antonello; Dorian Donzella, Marcello; Colangelo, Antonio; Osmani, Venet; Jurman, Giuseppe; Automatically detecting Crohn’s disease and Ulcerative Colitis from endoscopic imaging; in « BMC MEDICAL INFORMATICS AND DECISION MAKING»; Vol 22>; 2022; pp 300

• Levi Mortera, Stefano; Vernocchi, Pamela; Basadonne, Ilaria; Zandonà, Alessandro; Chierici, Marco; Durighello, Martina; Marzano, Valeria; Gardini, Simone; Gasbarrini, Antonio; Urbani, Andrea; Vicari, Stefano; Roncada, Paola; Furlanello, Cesare; Venuti, Paola; Putignani, Lorenza; A metaproteomic-based gut microbiota profiling in children affected by autism spectrum disorders; in « JOURNAL OF PROTEOMICS»; Vol 251>; 2022; pp 104407

• Buonaiuto, Silvia; Biase, Immacolata Di; Aleotti, Valentina; Ravaei, Amin; Marino, Adriano De; Damaggio, Gianluca; Chierici, Marco; Pulijala, Madhuri; D’Ambrosio, Palmira; Esposito, Gabriella; Ayub, Qasim; Furlanello, Cesare; Greco, Pantaleo; Capalbo, Antonio; Rubini, Michele; Biase, Sebastiano Di; Colonna, Vincenza; Prioritization of putatively detrimental variants in euploid miscarriages; in «SCIENTIFIC REPORTS »; Vol 12; 2022

  2021

• Giulia Bertò, Daniel Bullock, Pietro Astolfi, Soichi Hayashi, Luca Zigiotto, Luciano Annicchiarico, Francesco Corsini, Alessandro De Benedictis, Silvio Sarubbo, Franco Pestilli,  Paolo Avesani , Emanuele Olivetti, “Classifyber, a robust streamline-based linear classifier for white matter bundle segmentation”, Neuroimage, 2021,  https://doi.org/10.1016/j.neuroimage.2020.117402

• Jones, Wendell; Gong, Binsheng; Novoradovskaya, Natalia; Li, Dan; Kusko, Rebecca; Richmond, Todd A.; Johann, Donald J.; Bisgin, Halil; Sahraeian, Sayed Mohammad Ebrahim; Bushel, Pierre R.; Pirooznia, Mehdi; Wilkins, Katherine; Chierici, Marco; Bao, Wenjun; Basehore, Lee Scott; Lucas, Anne Bergstrom; Burgess, Daniel; Butler, Daniel J.; Cawley, Simon; Chang, Chia-Jung; Chen, Guangchun; Chen, Tao; Chen, Yun-Ching; Craig, Daniel J.; del Pozo, Angela; Foox, Jonathan; Francescatto, Margherita; Fu, Yutao; Furlanello, Cesare; Giorda, Kristina; Grist, Kira P.; Guan, Meijian; Hao, Yingyi; Happe, Scott; Hariani, Gunjan; Haseley, Nathan; Jasper, Jeff; Jurman, Giuseppe; Kreil, David Philip; Łabaj, Paweł; Lai, Kevin; Li, Jianying; Li, Quan-Zhen; Li, Yulong; Li, Zhiguang; Liu, Zhichao; López, Mario Solís; Miclaus, Kelci; Miller, Raymond; Mittal, Vinay K.; Mohiyuddin, Marghoob; Pabón-Peña, Carlos; Parsons, Barbara L.; Qiu, Fujun; Scherer, Andreas; Shi, Tieliu; Stiegelmeyer, Suzy; Suo, Chen; Tom, Nikola; Wang, Dong; Wen, Zhining; Wu, Leihong; Xiao, Wenzhong; Xu, Chang; Yu, Ying; Zhang, Jiyang; Zhang, Yifan; Zhang, Zhihong; Zheng, Yuanting; Mason, Christopher E.; Willey, James C.; Tong, Weida; Shi, Leming; Xu, Joshua,  A verified genomic reference sample for assessing performance of cancer panels detecting small variants of low allele frequency ,  in «GENOME BIOLOGY», vol. 22, n. 1, 2021 , pp. 111

• Chicco, Davide; Jurman, Giuseppe,  An ensemble learning approach for enhanced classification of patients with hepatitis and cirrhosis ,  in «IEEE ACCESS», vol. 9, 2021 , pp. 24485 – 24498

• Bussola, Nicole; Marcolini, Alessia; Maggio, Valerio; Jurman, Giuseppe; Furlanello, Cesare,  AI Slipping on Tiles: Data Leakage in Digital Pathology ,  Proceedings of International Conference on Pattern Recognition (ICPR 2021), Springer, 2021 , pp. 167 – 182

• Gabrielli S, Rizzi S, Bassi G, Carbone S, Maimone R, Marchesoni M, Forti S. Engagement and Effectiveness of a Healthy Coping Intervention via Chatbot for university students: proof-of-concept study during the COVID-19 pandemic. JMIR Mhealth Uhealth. 2021 Apr 20. doi: 10.2196/27965. Epub ahead of print. PMID: 33950849. URL: https://pubmed.ncbi.nlm.nih.gov/33950849/

• Gios L, Crema Falceri G, Patil L, Testa S, Micocci S, Sforzin S, Turra E, Conforti D, Malfatti G, Moz M, Nicolini A, Guarda P, Bacchiega A, Mion C, Marchesoni M, Maimone R, Molini PB, Zanella A, Osmani V, Mayora-Ibarra O, Forti S. Using eHealth platforms and Apps to support monitoring and management of home-quarantined COVID-19 patients. Insights from the experience of the Province of Trento, Italy. JMIR Form Res. 2021 Apr 13. doi: 10.2196/25713. Epub ahead of print. PMID: 33909586. URL: https://pubmed.ncbi.nlm.nih.gov/33909586/

• V. Balaraman, B. Magnini Domain-aware dialogue state tracker for multi-domain dialogue systems IEEE/ACM Transactions on Audio, Speech, and Language Processing 29, 866-873 (2021)

• The interplay of a conversational ontology and AI planning for health dialogue management  Teixeira, M.S., Maran, V., Dragoni, M. Proceedings of the ACM Symposium on Applied Computing, 2021, pp. 611–619

• Beyond arrows in process models: A user study on activity dependences and their rationales  Adamo, G., Di Francescomarino, C., Ghidini, C., Maggi, F.M. Information Systems, 2021, 100, 101762

• What is a process model composed of?: A systematic literature review of meta-models in BPM  Adamo, G., Ghidini, C., Di Francescomarino, C. Software and Systems Modeling, 2021

  2020

• Pietro Astolfi, Ruben Verhagen, Laurent Petit, Emanuele Olivetti, Jonathan Masci, Davide Boscaini,  Paolo Avesani , “Tractogram filtering of anatomically non-plausible fibers with geometric deep learning”,  Medical Image Computing and Computer Assisted Intervention (MICCAI 2020),  https://doi.org/10.1007/978-3-030-59728-3_29

• Gabriele Amorosino, Denis Peruzzo, Pietro Astolfi, Daniela Redaelli,  Paolo Avesani , Filippo Arrigoni, Emanuele Olivetti, “Automatic Tissue Segmentation with Deep Learning in Patients with Congenital or Acquired Distortion of Brain Anatomy”, Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology (MLCN 2020),  https://doi.org/10.1007/978-3-030-66843-3_2

• Pietro Astolfi, Alessandro De Benedictis, Silvio Sarubbo, Giulia Bertó, Emanuele Olivetti, Diego Sona,  Paolo Avesani , “A Stem-Based Dissection of Inferior Fronto-Occipital Fasciculus with A Deep Learning Model”,  IEEE 17th International Symposium on Biomedical Imaging (ISBI 2020),  https://doi.org/10.1109/ISBI45749.2020.9098483

• Paolo Avesani,  Brent McPherson, Soichi Hayashi, Cesar F Caiafa, Robert Henschel, Eleftherios Garyfallidis, Lindsey Kitchell, Daniel Bullock, Andrew Patterson, Emanuele Olivetti, Olaf Sporns, Andrew J Saykin, Lei Wang, Ivo Dinov, David Hancock, Bradley Caron, Yiming Qian, Franco Pestilli, “The open diffusion data derivatives, brain data upcycling via integrated publishing of derivatives and reproducible open cloud services”, Nature Scientific Data 6(69), 2019,  https://doi.org/10.1038/s41597-019-0073-y

• Chicco, Davide;  Jurman, Giuseppe ,  The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation,  in «BMC GENOMICS», vol. 21, n. 1, 2020 , pp. 6

• Franch, Gabriele; Maggio, Valerio; Coviello, Luca; Pendesini, Marta;  Jurman, Giuseppe ; Furlanello, Cesare,  TAASRAD19, a high-resolution weather radar reflectivity dataset for precipitation nowcasting ,  in «SCIENTIFIC DATA», vol. 7, n. 1, 2020

• Melaiu, Ombretta; Chierici, Marco; Lucarini, Valeria;  Jurman, Giuseppe ; Conti, Libenzio; De Vito, Rita; Boldrini, Renata; Cifaldi, Loredana; Castellano, Aurora; Furlanello, Cesare; Barnaba, Vincenzo; Locatelli, Franco; Furci, Doriana,  Cellular and gene signatures of tumor-infiltrating dendritic cells and natural killer cells predict favorable clinical outcome of neuroblastoma ,  in «NATURE COMMUNICATIONS», vol. 11, 2020 , pp. 5992

• Gabrielli S, Rizzi S, Carbone S, Donisi V. A Chatbot-Based Coaching Intervention for Adolescents to Promote Life Skills: Pilot Study, JMIR Hum Factors 2020;7(1):e16762, URL: https://humanfactors.jmir.org/2020/1/e16762 DOI: 10.2196/16762

• Merz V, Ferro A, Piras EM, Zanutto A, Caffo O, Messina C. Electronic Medical Record-Assisted Telephone Follow-Up of Breast Cancer Survivors During the COVID-19 Pandemic: A Single Institution Experience. JCO Oncol Pract. 2021 Jan;17(1):e44-e52. doi: 10.1200/OP.20.00643. Epub 2020 Dec 22. PMID: 33351674. URL: https://ascopubs.org/doi/10.1200/OP.20.00643

• Eccher C, Gios L, Zanutto A, Bizzarri G, Conforti D, Forti S. TreC platform. An integrated and evolving care model for patients’ empowerment and data repository. J Biomed Inform. 2020 Feb;102:103359. doi: 10.1016/j.jbi.2019.103359. Epub 2020 Jan 7. PMID: 31917253. URL: https://www.sciencedirect.com/science/article/pii/S1532046419302795?via%3Dihub  

• Maxhuni A. Hernandez P., Morales E., Sucar E., Osmani V., Mayora O. Unobtrusive Stress Assessment Using Smartphones. IEEE Transactions on Mobile Computing. February 2020. URL: https://ieeexplore.ieee.org/document/9001213  

• S. Louvan, B. Magnini Recent Neural Methods on Slot Filling and Intent Classification for Task-Oriented Dialogue Systems: A Survey In: Proceedings of the 28th International Conference on Computational Linguistics (COLING) (2020)

• Explainable AI meets persuasiveness: Translating reasoning results into behavioral change advice  Dragoni, M., Donadello, I., Eccher, C. Artificial Intelligence in Medicine, 2020, 105, 101840

• Explainability in predictive process monitoring: When understanding helps improving  Rizzi, W., Di Francescomarino, C., Maggi, F.M. Lecture Notes in Business Information Processing, 2020, 392 LNBIP, pp. 141–158

2019

• Boldrini, Renata; De Pasquale Maria, Debora; Melaiu, Ombretta; Chierici, Marco; Jurman, Giuseppe; De Benedetti Maria, Chiara; Salfi Nunzio, C; Castellano, Aurora; Collini, Paola; Furlanello, Cesare; Pistoia, Vito; Cifaldi, Loredana; Terenziani, Monica; Fruci, Doriana,  Tumor-infiltrating T cells and PD-L1 expression in childhood malignant extracranial germ-cell tumors ,  in «ONCOIMMUNOLOGY», vol. 8, n. 2, 2019

• Bizzego, Andrea; Bussola, Nicole; Chierici, Marco; Maggio, Valerio; Francescatto, Margherita; Cima, Luca; Cristoforetti, Marco; Jurman, Giuseppe; Furlanello, Cesare,  Evaluating reproducibility of AI algorithms in digital pathology with DAPPER,  in «PLOS COMPUTATIONAL BIOLOGY», vol. 15, n. 3, 2019 , pp. e1006269

• Chierici, Marco; Giulini, Marco; Bussola, Nicole; Jurman, Giuseppe; Furlanello, Cesare,  Machine learning models for predicting endocrine disruption potential of environmental chemicals ,  in «JOURNAL OF ENVIRONMENTAL SCIENCE AND HEALTH. PART C, ENVIRONMENTAL CARCINOGENESIS & ECOTOXICOLOGY REVIEWS», vol. 36, n. 4, 2019 , pp. 237 – 251

• An Evolutionary Strategy for Concept-Based Multi-Domain Sentiment Analysis  Dragoni, M. IEEE Computational Intelligence Magazine, 2019, 14(2), pp. 18–27, 8686319