| 1. |
- Di Fatta, G., et al.
(författare)
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Preface
- 2011
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Ingår i: IEEE International Conference on Data Mining. Proceedings. - : Institute of Electrical and Electronics Engineers (IEEE). - 1550-4786. ; , s. xlviii-xlvix
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Tidskriftsartikel (refereegranskat)
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| 2. |
- D’Aniello, G., et al.
(författare)
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Situation Awareness in the Cloud-Edge Continuum
- 2024
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Ingår i: Lecture Notes on Data Engineering and Communications Technologies. - : Springer Science and Business Media Deutschland GmbH. ; , s. 307-316
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- Edge Computing is becoming a promising computing paradigm that addresses the limitations of cloud computing concerning latency, autonomy, and costs. To facilitate more intelligent applications made possible by the Edge Computing paradigm, it is essential to integrate intelligence and adaptability into devices located at the network’s edge. The paper explores the potential integration of Situation Awareness (SA) capabilities into the Cloud-Edge continuum. This integration aims to empower smarter applications while effectively managing challenges related to low latency, high autonomy, and cost-effective solutions. Within our illustrative example in healthcare, we showcase how the proposed SA cloud-edge continuum architecture enables efficient data processing and decision-making.
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| 3. |
- Fortino, V, et al.
(författare)
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Biomarkers of nanomaterials hazard from multi-layer data
- 2022
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Ingår i: Nature communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 13:1, s. 3798-
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Tidskriftsartikel (refereegranskat)abstract
- There is an urgent need to apply effective, data-driven approaches to reliably predict engineered nanomaterial (ENM) toxicity. Here we introduce a predictive computational framework based on the molecular and phenotypic effects of a large panel of ENMs across multiple in vitro and in vivo models. Our methodology allows for the grouping of ENMs based on multi-omics approaches combined with robust toxicity tests. Importantly, we identify mRNA-based toxicity markers and extensively replicate them in multiple independent datasets. We find that models based on combinations of omics-derived features and material intrinsic properties display significantly improved predictive accuracy as compared to physicochemical properties alone.
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| 7. |
- Federico, A, et al.
(författare)
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The integration of large-scale public data and network analysis uncovers molecular characteristics of psoriasis
- 2022
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Ingår i: Human genomics. - : Springer Science and Business Media LLC. - 1479-7364. ; 16:1, s. 62-
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Tidskriftsartikel (refereegranskat)abstract
- In recent years, a growing interest in the characterization of the molecular basis of psoriasis has been observed. However, despite the availability of a large amount of molecular data, many pathogenic mechanisms of psoriasis are still poorly understood. In this study, we performed an integrated analysis of 23 public transcriptomic datasets encompassing both lesional and uninvolved skin samples from psoriasis patients. We defined comprehensive gene co-expression network models of psoriatic lesions and uninvolved skin. Moreover, we curated and exploited a wide range of functional information from multiple public sources in order to systematically annotate the inferred networks. The integrated analysis of transcriptomics data and co-expression networks highlighted genes that are frequently dysregulated and show aberrant patterns of connectivity in the psoriatic lesion compared with the unaffected skin. Our approach allowed us to also identify plausible, previously unknown, actors in the expression of the psoriasis phenotype. Finally, we characterized communities of co-expressed genes associated with relevant molecular functions and expression signatures of specific immune cell types associated with the psoriasis lesion. Overall, integrating experimental driven results with curated functional information from public repositories represents an efficient approach to empower knowledge generation about psoriasis and may be applicable to other complex diseases.
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| 8. |
- Lv, Zhihan, Dr. 1984-, et al.
(författare)
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Behavioral Modeling and Prediction in Social Perception and Computing : A Survey
- 2023
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Ingår i: IEEE Transactions on Computational Social Systems. - : Institute of Electrical and Electronics Engineers (IEEE). - 2329-924X. ; 10:4, s. 2008-2021
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Tidskriftsartikel (refereegranskat)abstract
- More data are generated through interaction between cyber space, physical space, and social space thanks to mobile network technology, giving birth to the so-called cyber–physical social intelligent ecosystem (C&P-SIE). This survey studies the development of physical social intelligence. First, it classifies and discusses the behavior modeling, learning, and adaptation applications of C&P-SIE from intelligent transportation, healthcare, public service, economy, and social networking. Then, it prospects the application of behavior modeling in the C&P-SIE from the perspectives of information security, data-driven techniques, and modeling learning under cooperative artificial intelligence technologies. The research provides a theoretical basis and new opportunities for the digital and intelligent development of smart cities and social systems. IEEE
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