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1. Imitating the respiratory activity of the brain stem by using artificial neural networks: exploratory study on an animal model of lactic acidosis and proof of concept

Author

Perchiazzi, Gaetano, Kawati, Rafael, Pellegrini, Mariangela, Liangpansakul, Jasmine, Colella, Roberto, Bollella, Paolo, Rangaiah, Pramod, Cannone, Annamaria, Venkataramana, Deepthi Hulithala, Perez, Mauricio, Stramaglia, Sebastiano, Torsi, Luisa, Bellotti, Roberto, and Augustine, Robin

Published
2024
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2. Microbiome preterm birth DREAM challenge: Crowdsourcing machine learning approaches to advance preterm birth research

Author

Golob, Jonathan L, Oskotsky, Tomiko T, Tang, Alice S, Roldan, Alennie, Chung, Verena, Ha, Connie WY, Wong, Ronald J, Flynn, Kaitlin J, Parraga-Leo, Antonio, Wibrand, Camilla, Minot, Samuel S, Oskotsky, Boris, Andreoletti, Gaia, Kosti, Idit, Bletz, Julie, Nelson, Amber, Gao, Jifan, Wei, Zhoujingpeng, Chen, Guanhua, Tang, Zheng-Zheng, Novielli, Pierfrancesco, Romano, Donato, Pantaleo, Ester, Amoroso, Nicola, Monaco, Alfonso, Vacca, Mirco, De Angelis, Maria, Bellotti, Roberto, Tangaro, Sabina, Kuntzleman, Abigail, Bigcraft, Isaac, Techtmann, Stephen, Bae, Daehun, Kim, Eunyoung, Jeon, Jongbum, Joe, Soobok, Community, The Preterm Birth DREAM, Theis, Kevin R, Ng, Sherrianne, Lee, Yun S, Diaz-Gimeno, Patricia, Bennett, Phillip R, MacIntyre, David A, Stolovitzky, Gustavo, Lynch, Susan V, Albrecht, Jake, Gomez-Lopez, Nardhy, Romero, Roberto, Stevenson, David K, Aghaeepour, Nima, Tarca, Adi L, Costello, James C, and Sirota, Marina

Subjects
Paediatrics, Biomedical and Clinical Sciences, Infant Mortality, Prevention, Genetics, Perinatal Period - Conditions Originating in Perinatal Period, Pediatric, Human Genome, Preterm, Low Birth Weight and Health of the Newborn, Reproductive health and childbirth, Good Health and Well Being, Pregnancy, Female, Infant, Newborn, Humans, Premature Birth, Crowdsourcing, Phylogeny, Vagina, Microbiota, Preterm Birth DREAM Community, 16S harmonization, DREAM challenge, crowdsourced, machine learning, microbiome, predictive modeling, preterm birth, vaginal microbiome, Biomedical and clinical sciences
Abstract

Every year, 11% of infants are born preterm with significant health consequences, with the vaginal microbiome a risk factor for preterm birth. We crowdsource models to predict (1) preterm birth (PTB;

Published
2024

3. Characterization of Innovative Technologies in Healthcare 4.0 Through the Analysis of Italian Patents

Author

Demarinis Loiotile, Annamaria, Amoroso, Nicola, Bellotti, Roberto, Lovell, Nigel H., Advisory Editor, Oneto, Luca, Advisory Editor, Piotto, Stefano, Advisory Editor, Rossi, Federico, Advisory Editor, Samsonovich, Alexei V., Advisory Editor, Babiloni, Fabio, Advisory Editor, Liwo, Adam, Advisory Editor, Magjarevic, Ratko, Advisory Editor, Bochicchio, Mario, editor, Siciliano, Pietro, editor, Monteriù, Andrea, editor, Bettelli, Alice, editor, and De Fano, Domenico, editor

Published
2024
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4. Towards a Healthcare 4.0 Vocabulary: A Patent-Based Approach

Author

Demarinis Loiotile, Annamaria, De Nicolò, Francesco, Agrimi, Adriana, Conti, Giuseppe, Amoroso, Nicola, Bellotti, Roberto, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Rocha, Alvaro, editor, Adeli, Hojjat, editor, Dzemyda, Gintautas, editor, Moreira, Fernando, editor, and Colla, Valentina, editor

Published
2024
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7. Planck Formula for the Gluon Parton Distribution in the Proton

Author

Bellantuono, Loredana, Bellotti, Roberto, and Buccella, Franco

Subjects
High Energy Physics - Phenomenology, Nuclear Theory
Abstract

We describe the gluon parton distribution function (PDF) in the proton, deduced by data from the ATLAS and HERA experiments, in the framework of the parton statistical model. The best fit parameters involved in the Planck formula that describes the gluon distribution are consistent with the results obtained from analysis of deep inelastic scattering processes. Remarkably, the agreement between the statistical model and the experimental gluon distributions is found with the same value of the "temperature" parameter $\bar{x}$ found by fitting the valence parton distributions from deep inelastic scattering. This result corroborates the validity of the statistical approach in the gluon sector., Comment: 4 pages, 1 figure

Published
2022
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9. Mind the gap in university rankings: a complex network approach towards fairness

Author

Bellantuono, Loredana, Monaco, Alfonso, Amoroso, Nicola, Aquaro, Vincenzo, Bardoscia, Marco, Loiotile, Annamaria Demarinis, Lombardi, Angela, Tangaro, Sabina, and Bellotti, Roberto

Subjects
Physics - Physics and Society
Abstract

University rankings are increasingly adopted for academic comparison and success quantification, even to establish performance-based criteria for funding assignment. However, rankings are not neutral tools, and their use frequently overlooks disparities in the starting conditions of institutions. In this research, we detect and measure structural biases that affect in inhomogeneous ways the ranking outcomes of universities from diversified territorial and educational contexts. Moreover, we develop a fairer rating system based on a fully data-driven debiasing strategy that returns an equity-oriented redefinition of the achieved scores. The key idea consists in partitioning universities in similarity groups, determined from multifaceted data using complex network analysis, and referring the performance of each institution to an expectation based on its peers. Significant evidence of territorial biases emerges for official rankings concerning both the OECD and Italian university systems, hence debiasing provides relevant insights suggesting the design of fairer strategies for performance-based funding allocations., Comment: 19 pages, 3 figures. Supplementary information included as separate file

Published
2021

14. Potential energy of complex networks: a novel perspective

Author

Amoroso, Nicola, Bellantuono, Loredana, Pascazio, Saverio, Lombardi, Angela, Monaco, Alfonso, Tangaro, Sabina, and Bellotti, Roberto

Subjects
Physics - Physics and Society, Condensed Matter - Statistical Mechanics, Physics - Applied Physics, Quantum Physics
Abstract

We present a novel characterization of complex networks, based on the potential of an associated Schr\"odinger equation. The potential is designed so that the energy spectrum of the Schr\"odinger equation coincides with the graph spectrum of the normalized Laplacian. Crucial information is retained in the reconstructed potential, which provides a compact representation of the properties of the network structure. The median potential over several random network realizations is fitted via a Landau-like function, and its length scale is found to diverge as the critical connection probability is approached from above. The ruggedness of the median potential profile is quantified using the Higuchi fractal dimension, which displays a maximum at the critical connection probability. This demonstrates that this technique can be successfully employed in the study of random networks, as an alternative indicator of the percolation phase transition. We apply the proposed approach to the investigation of real-world networks describing infrastructures (US power grid). Curiously, although no notion of phase transition can be given for such networks, the fractality of the median potential displays signatures of criticality. We also show that standard techniques (such as the scaling features of the largest connected component) do not detect any signature or remnant of criticality., Comment: 15 pages, 13 figures

Published
2020

16. Imitating the respiratory activity of the brain stem by using artificial neural networks: exploratory study on an animal model of lactic acidosis and proof of concept

Author

Perchiazzi, Gaetano, primary, Kawati, Rafael, additional, Pellegrini, Mariangela, additional, Liangpansakul, Jasmine, additional, Colella, Roberto, additional, Bollella, Paolo, additional, Rangaiah, Pramod, additional, Cannone, Annamaria, additional, Venkataramana, Deepthi Hulithala, additional, Perez, Mauricio, additional, Stramaglia, Sebastiano, additional, Torsi, Luisa, additional, Bellotti, Roberto, additional, and Augustine, Robin, additional

Published
2024
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28. Brain structural connectivity atrophy in Alzheimer's disease

Author

Amoroso, Nicola, La Rocca, Marianna, Bruno, Stefania, Maggipinto, Tommaso, Monaco, Alfonso, Bellotti, Roberto, and Tangaro, Sabina

Subjects
Physics - Medical Physics, Quantitative Biology - Neurons and Cognition
Abstract

Analysis and quantification of brain structural changes, using Magnetic resonance imaging (MRI), are increasingly used to define novel biomarkers of brain pathologies, such as Alzheimer's disease (AD). Network-based models of the brain have shown that both local and global topological properties can reveal patterns of disease propagation. On the other hand, intra-subject descriptions cannot exploit the whole information context, accessible through inter-subject comparisons. To address this, we developed a novel approach, which models brain structural connectivity atrophy with a multiplex network and summarizes it within a classification score. On an independent dataset multiplex networks were able to correctly segregate, from normal controls (NC), AD patients and subjects with mild cognitive impairment that will convert to AD (cMCI) with an accuracy of, respectively, $0.86 \pm 0.01$ and $0.84 \pm 0.01$. The model also shows that illness effects are maximally detected by parceling the brain in equal volumes of $3000$ $mm^3$ ("patches"), without any $a$ $priori$ segmentation based on anatomical features. A direct comparison to standard voxel-based morphometry on the same dataset showed that the multiplex network approach had higher sensitivity. This method is general and can have twofold potential applications: providing a reliable tool for clinical trials and a disease signature of neurodegenerative pathologies., Comment: 16 pages, 8 figures, 2 tables

Published
2017

29. Estimation of Daily Ground Level Air Pollution in Italian Municipalities with Machine Learning Models Using Sentinel-5P and ERA5 Data

Author

Fania, Alessandro, primary, Monaco, Alfonso, additional, Pantaleo, Ester, additional, Maggipinto, Tommaso, additional, Bellantuono, Loredana, additional, Cilli, Roberto, additional, Lacalamita, Antonio, additional, La Rocca, Marianna, additional, Tangaro, Sabina, additional, Amoroso, Nicola, additional, and Bellotti, Roberto, additional

Published
2024
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30. Machine learning and XAI approaches highlight the strong connection between $$O_3$$ and $$NO_2$$ pollutants and Alzheimer’s disease

Author

Fania, Alessandro, primary, Monaco, Alfonso, additional, Amoroso, Nicola, additional, Bellantuono, Loredana, additional, Cazzolla Gatti, Roberto, additional, Firza, Najada, additional, Lacalamita, Antonio, additional, Pantaleo, Ester, additional, Tangaro, Sabina, additional, Velichevskaya, Alena, additional, and Bellotti, Roberto, additional

Published
2024
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31. Explainable artificial intelligence for microbiome data analysis in colorectal cancer biomarker identification

Author

Novielli, Pierfrancesco, primary, Romano, Donato, additional, Magarelli, Michele, additional, Bitonto, Pierpaolo Di, additional, Diacono, Domenico, additional, Chiatante, Annalisa, additional, Lopalco, Giuseppe, additional, Sabella, Daniele, additional, Venerito, Vincenzo, additional, Filannino, Pasquale, additional, Bellotti, Roberto, additional, De Angelis, Maria, additional, Iannone, Florenzo, additional, and Tangaro, Sabina, additional

Published
2024
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32. Artificial Intelligencefor Automated Detection and Counting of Olive Trees

Author

Cilli, Roberto, primary, Pantaleo, Ester, additional, Amoroso, Nicola, additional, Vivaldi, Gaetano Alessandro, additional, Giannico, Vincenzo, additional, Bellotti, Roberto, additional, Sanesi, Giovanni, additional, Camposeo, Salvatore, additional, Monaco, Alfonso, additional, Elia, Mario, additional, Tangaro, Sabina, additional, and Lafortezza, Raffaele, additional

Published
2024
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35. Complex networks and public funding: the case of the 2007-2013 Italian program

Author

Nicotri, Stefano, Tinelli, Eufemia, Amoroso, Nicola, Garuccio, Elena, and Bellotti, Roberto

Subjects
Physics - Physics and Society, Computer Science - Social and Information Networks, Physics - Data Analysis, Statistics and Probability
Abstract

In this paper we apply techniques of complex network analysis to data sources representing public funding programs and discuss the importance of the considered indicators for program evaluation. Starting from the Open Data repository of the 2007-2013 Italian Program Programma Operativo Nazionale 'Ricerca e Competitivit\`a' (PON R&C), we build a set of data models and perform network analysis over them. We discuss the obtained experimental results outlining interesting new perspectives that emerge from the application of the proposed methods to the socio-economical evaluation of funded programs., Comment: 22 pages, 9 figures

Published
2015
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36. Feature Selection based on Machine Learning in MRIs for Hippocampal Segmentation

Author

Tangaro, Sabina, Amoroso, Nicola, Brescia, Massimo, Cavuoti, Stefano, Chincarini, Andrea, Errico, Rosangela, Inglese, Paolo, Longo, Giuseppe, Maglietta, Rosalia, Tateo, Andrea, Riccio, Giuseppe, and Bellotti, Roberto

Subjects
Physics - Medical Physics, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Learning
Abstract

Neurodegenerative diseases are frequently associated with structural changes in the brain. Magnetic Resonance Imaging (MRI) scans can show these variations and therefore be used as a supportive feature for a number of neurodegenerative diseases. The hippocampus has been known to be a biomarker for Alzheimer disease and other neurological and psychiatric diseases. However, it requires accurate, robust and reproducible delineation of hippocampal structures. Fully automatic methods are usually the voxel based approach, for each voxel a number of local features were calculated. In this paper we compared four different techniques for feature selection from a set of 315 features extracted for each voxel: (i) filter method based on the Kolmogorov-Smirnov test; two wrapper methods, respectively, (ii) Sequential Forward Selection and (iii) Sequential Backward Elimination; and (iv) embedded method based on the Random Forest Classifier on a set of 10 T1-weighted brain MRIs and tested on an independent set of 25 subjects. The resulting segmentations were compared with manual reference labelling. By using only 23 features for each voxel (sequential backward elimination) we obtained comparable state of-the-art performances with respect to the standard tool FreeSurfer., Comment: To appear on "Computational and Mathematical Methods in Medicine", Hindawi Publishing Corporation. 19 pages, 7 figures

Published
2015
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37. The interaction between cannabis use and a CB1-related polygenic co-expression index modulates dorsolateral prefrontal activity during working memory processing

Author

Taurisano, Paolo, Pergola, Giulio, Monda, Anna, Antonucci, Linda A., Di Carlo, Pasquale, Piarulli, Francesco, Passiatore, Roberta, Papalino, Marco, Romano, Raffaella, Monaco, Alfonso, Rampino, Antonio, Bonvino, Aurora, Porcelli, Annamaria, Popolizio, Teresa, Bellotti, Roberto, Bertolino, Alessandro, and Blasi, Giuseppe

Published
2021
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38. Explainable artificial intelligence and microbiome data for food geographical origin: the Mozzarella di Bufala Campana PDO Case of Study.

Author

Magarelli, Michele, Novielli, Pierfrancesco, De Filippis, Francesca, Magliulo, Raffaele, Di Bitonto, Pierpaolo, Diacono, Domenico, Bellotti, Roberto, and Tangaro, Sabina

Subjects
ARTIFICIAL intelligence, SUPERVISED learning, RANDOM forest algorithms, PRODUCTION methods, MACHINE learning, STREET food, FOOD safety
Abstract

Identifying the origin of a food product holds paramount importance in ensuring food safety, quality, and authenticity. Knowing where a food item comes from provides crucial information about its production methods, handling practices, and potential exposure to contaminants. Machine learning techniques play a pivotal role in this process by enabling the analysis of complex data sets to uncover patterns and associations that can reveal the geographical source of a food item. This study aims to investigate the potential use of explainable artificial intelligence for identifying the food origin. The case of study of Mozzarella di Bufala Campana PDO has been considered by examining the composition of the microbiota in each samples. Three different supervised machine learning algorithms have been compared and the best classifier model is represented by Random Forest with an Area Under the Curve (AUC) value of 0.93 and the top accuracy of 0.87.Machine learningmodels effectively classify origin, offering innovative ways to authenticate regional products and support local economies. Further research can explore microbiota analysis and extend applicability to diverse food products and contexts for enhanced accuracy and broader impact. [ABSTRACT FROM AUTHOR]

Published
2024
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40. Insights from Explainable Artificial Intelligence of Pollution and Socioeconomic Influences for Respiratory Cancer Mortality in Italy.

Author

Romano, Donato, Novielli, Pierfrancesco, Diacono, Domenico, Cilli, Roberto, Pantaleo, Ester, Amoroso, Nicola, Bellantuono, Loredana, Monaco, Alfonso, Bellotti, Roberto, and Tangaro, Sabina

Subjects
ARTIFICIAL intelligence, CANCER-related mortality, SOCIOECONOMIC factors, MACHINE learning, AIR pollution
Abstract

Respiratory malignancies, encompassing cancers affecting the lungs, the trachea, and the bronchi, pose a significant and dynamic public health challenge. Given that air pollution stands as a significant contributor to the onset of these ailments, discerning the most detrimental agents becomes imperative for crafting policies aimed at mitigating exposure. This study advocates for the utilization of explainable artificial intelligence (XAI) methodologies, leveraging remote sensing data, to ascertain the primary influencers on the prediction of standard mortality rates (SMRs) attributable to respiratory cancer across Italian provinces, utilizing both environmental and socioeconomic data. By scrutinizing thirteen distinct machine learning algorithms, we endeavor to pinpoint the most accurate model for categorizing Italian provinces as either above or below the national average SMR value for respiratory cancer. Furthermore, employing XAI techniques, we delineate the salient factors crucial in predicting the two classes of SMR. Through our machine learning scrutiny, we illuminate the environmental and socioeconomic factors pertinent to mortality in this disease category, thereby offering a roadmap for prioritizing interventions aimed at mitigating risk factors. [ABSTRACT FROM AUTHOR]

Published
2024
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42. Multidimensional Neuroimaging Processing in ReCaS Datacenter

Author

Lombardi, Angela, Lella, Eufemia, Amoroso, Nicola, Diacono, Domenico, Monaco, Alfonso, Bellotti, Roberto, Tangaro, Sabina, 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, Montella, Raffaele, editor, Ciaramella, Angelo, editor, Fortino, Giancarlo, editor, Guerrieri, Antonio, editor, and Liotta, Antonio, editor

Published
2019
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43. Crowdsourced estimation of cognitive decline and resilience in Alzheimer's disease

Author

Allen, Genevera I, Amoroso, Nicola, Anghel, Catalina, Balagurusamy, Venkat, Bare, Christopher J, Beaton, Derek, Bellotti, Roberto, Bennett, David A, Boehme, Kevin L, Boutros, Paul C, Caberlotto, Laura, Caloian, Cristian, Campbell, Frederick, Neto, Elias Chaibub, Chang, Yu‐Chuan, Chen, Beibei, Chen, Chien‐Yu, Chien, Ting‐Ying, Clark, Tim, Das, Sudeshna, Davatzikos, Christos, Deng, Jieyao, Dillenberger, Donna, Dobson, Richard JB, Dong, Qilin, Doshi, Jimit, Duma, Denise, Errico, Rosangela, Erus, Guray, Everett, Evan, Fardo, David W, Friend, Stephen H, Fröhlich, Holger, Gan, Jessica, St George‐Hyslop, Peter, Ghosh, Satrajit S, Glaab, Enrico, Green, Robert C, Guan, Yuanfang, Hong, Ming‐Yi, Huang, Chao, Hwang, Jinseub, Ibrahim, Joseph, Inglese, Paolo, Iyappan, Anandhi, Jiang, Qijia, Katsumata, Yuriko, Kauwe, John SK, Klein, Arno, Kong, Dehan, Krause, Roland, Lalonde, Emilie, Lauria, Mario, Lee, Eunjee, Lin, Xihui, Liu, Zhandong, Livingstone, Julie, Logsdon, Benjamin A, Lovestone, Simon, Ma, Tsung‐wei, Malhotra, Ashutosh, Mangravite, Lara M, Maxwell, Taylor J, Merrill, Emily, Nagorski, John, Namasivayam, Aishwarya, Narayan, Manjari, Naz, Mufassra, Newhouse, Stephen J, Norman, Thea C, Nurtdinov, Ramil N, Oyang, Yen‐Jen, Pawitan, Yudi, Peng, Shengwen, Peters, Mette A, Piccolo, Stephen R, Praveen, Paurush, Priami, Corrado, Sabelnykova, Veronica Y, Senger, Philipp, Shen, Xia, Simmons, Andrew, Sotiras, Aristeidis, Stolovitzky, Gustavo, Tangaro, Sabina, Tateo, Andrea, Tung, Yi‐An, Tustison, Nicholas J, Varol, Erdem, Vradenburg, George, Weiner, Michael W, Xiao, Guanghua, Xie, Lei, Xie, Yang, Xu, Jia, Yang, Hojin, Zhan, Xiaowei, Zhou, Yunyun, Zhu, Fan, and Zhu, Hongtu

Subjects
Biomedical and Clinical Sciences, Biological Psychology, Clinical Sciences, Neurosciences, Psychology, Acquired Cognitive Impairment, Prevention, Neurodegenerative, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Dementia, Alzheimer's Disease, Aging, Brain Disorders, Neurological, Alzheimer Disease, Apolipoproteins E, Biomarkers, Cognition Disorders, Computational Biology, Databases, Bibliographic, Humans, Predictive Value of Tests, Azheimer's disease, Crowdsource, Big data, Bioinformatics, Cognitive decline, Imaging, Genetics, Alzheimer's Disease Neuroimaging Initiative, Geriatrics, Clinical sciences, Biological psychology
Abstract

Identifying accurate biomarkers of cognitive decline is essential for advancing early diagnosis and prevention therapies in Alzheimer's disease. The Alzheimer's disease DREAM Challenge was designed as a computational crowdsourced project to benchmark the current state-of-the-art in predicting cognitive outcomes in Alzheimer's disease based on high dimensional, publicly available genetic and structural imaging data. This meta-analysis failed to identify a meaningful predictor developed from either data modality, suggesting that alternate approaches should be considered for prediction of cognitive performance.

Published
2016

44. Microbiome preterm birth DREAM challenge: Crowdsourcing machine learning approaches to advance preterm birth research

Author

Golob, Jonathan L., primary, Oskotsky, Tomiko T., additional, Tang, Alice S., additional, Roldan, Alennie, additional, Chung, Verena, additional, Ha, Connie W.Y., additional, Wong, Ronald J., additional, Flynn, Kaitlin J., additional, Parraga-Leo, Antonio, additional, Wibrand, Camilla, additional, Minot, Samuel S., additional, Oskotsky, Boris, additional, Andreoletti, Gaia, additional, Kosti, Idit, additional, Bletz, Julie, additional, Nelson, Amber, additional, Gao, Jifan, additional, Wei, Zhoujingpeng, additional, Chen, Guanhua, additional, Tang, Zheng-Zheng, additional, Novielli, Pierfrancesco, additional, Romano, Donato, additional, Pantaleo, Ester, additional, Amoroso, Nicola, additional, Monaco, Alfonso, additional, Vacca, Mirco, additional, De Angelis, Maria, additional, Bellotti, Roberto, additional, Tangaro, Sabina, additional, Kuntzleman, Abigail, additional, Bigcraft, Isaac, additional, Techtmann, Stephen, additional, Bae, Daehun, additional, Kim, Eunyoung, additional, Jeon, Jongbum, additional, Joe, Soobok, additional, Theis, Kevin R., additional, Ng, Sherrianne, additional, Lee, Yun S., additional, Diaz-Gimeno, Patricia, additional, Bennett, Phillip R., additional, MacIntyre, David A., additional, Stolovitzky, Gustavo, additional, Lynch, Susan V., additional, Albrecht, Jake, additional, Gomez-Lopez, Nardhy, additional, Romero, Roberto, additional, Stevenson, David K., additional, Aghaeepour, Nima, additional, Tarca, Adi L., additional, Costello, James C., additional, and Sirota, Marina, additional

Published
2023
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47. Artificial Intelligence and Complex Network Approaches Reveal Potential Gene Biomarkers for Hepatocellular Carcinoma

Author

Lacalamita, Antonio, primary, Serino, Grazia, additional, Pantaleo, Ester, additional, Monaco, Alfonso, additional, Amoroso, Nicola, additional, Bellantuono, Loredana, additional, Piccinno, Emanuele, additional, Scalavino, Viviana, additional, Dituri, Francesco, additional, Tangaro, Sabina, additional, Bellotti, Roberto, additional, and Giannelli, Gianluigi, additional

Published
2023
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49. TEBAKA: a technological platform for Apulian crop monitoring

Author

Cilli, Roberto, primary, Pantaleo, Ester, additional, Donvito, Giacinto, additional, Antonacci, Marica, additional, Cristella, Leonardo, additional, Vino, Gioacchino, additional, Vivaldi, Gaetano Alessandro, additional, Giannico, Vincenzo, additional, D'Avino, Vincenzo, additional, Amoruso, Vito, additional, Cagnazzo, Cosimo, additional, Sanesi, Giovanni, additional, Matarrese, Manuela, additional, Amoroso, Nicola, additional, and Bellotti, Roberto, additional

Published
2023
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