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2025 Journal article Open Access OPEN
Shedding light on uncertainties in machine learning: formal derivation and optimal model selection
Del Corso G., Colantonio S., Caudai C.
The concept of uncertainty has always been important in the field of mathematical modeling. In particular, the growing application of Machine Learning and Deep Learning methods in many scientific fields has led to the implementation and use of new uncertainty quantification techniques aimed at distinguishing between reliable and unreliable predictions. However, the novelty of this discipline and the plethora of articles produced, ranging from theoretical results to purely applied experiments, has resulted in a very fragmented and cluttered literature. In this review, we have attempted to combine the well-established mathematical background of the Bayesian framework with the practical aspect of modern state-of-the-art emerging techniques in order to meet the urgent need for clarity on key concepts related to uncertainty quantification. First, we introduced the different sources of uncertainty, ranging from epistemic/reducible to aleatoric/irreducible, providing both a rigorous mathematical derivation and several examples to facilitate understanding. The review then details some of the most important techniques for uncertainty quantification. These methods are compared in terms of their advantages and drawbacks and classified in terms of their intrusiveness, in order to provide the practitioner with a useful vademecum for selecting the optimal model depending on the application context.Source: JOURNAL OF THE FRANKLIN INSTITUTE, vol. 362 (issue 3)
Project(s): ProCAncer-I via OpenAIRE, FAITH via OpenAIRE

See at: CNR IRIS Open Access | www.sciencedirect.com Open Access | CNR IRIS Restricted


2024 Other Open Access OPEN
Stretch your tentacles, POLPO-net: a POLymorphic PrObabilistic approach to greedily approximate model uncertainties
Del Corso G., Caudai C., Kuruoglu E. E., Colantonio S.
A POLymorphic PrObabilistic approach to greedily approximate uncertainties avoiding re-training of costly deep neural networks.

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2024 Conference article Open Access OPEN
Advancing sustainability: research initiatives at the Signals and Images Lab
Bruno A., Caudai C., Conti F., Leone G. R., Magrini M., Martinelli M., Moroni D., Muhammad A. Ch, Papini O., Pascali M. A., Pieri G., Reggiannini M., Righi M., Salerno E., Scozzari A., Tampucci M.
In this paper, we aim to briefly survey the relations of the work conducted at the Signals and Images Lab of CNR-ISTI, Pisa, with the themes of sustainability. We explore both the broader implications and the application-specific aspects of our work, highlighting references to published research and collaborative projects undertaken with key stakeholders and industrial partners.Source: CEUR WORKSHOP PROCEEDINGS, vol. 3762, pp. 499-504. Napoli, Italy, 29-30/05/2024

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2024 Other Open Access OPEN
U-ProBE: a graphical Python interface to handle uncertainties in deep learning models
Bandini L., Bacciu D., Del Corso G., Caudai C.
La piattaforma U-Probe mira ad essere un supporto ad un crescente numero di utilizzatori che hanno necessità di valutare le performances di un modello e soprat- tutto la sua incertezza. Per questo motivo la piattaforma è dotata di una intuitiva interfaccia grafica semplice da utilizzare anche per i non addetti ai lavori. L’analisi dell’incertezza delle predizioni di un modello di Machine Learning o Deep Learning può essere effettuata utilizzando varie tecniche. Alcune di queste sono intrusive (anche dette by design), tali tecniche vanno a modificare l’architettura in- troducendo strumenti probabilistici che possono fornire importanti indicazioni sulle caratteristiche delle predizioni, a livello di affidabilità e incertezza. Tali tecniche comprendono ad esempio le Bayesian Neural Networks, i Variational Autoencoders ed i Deep Gaussian Processes. Sono tecniche molto performanti, sia nel mitigare l’overfitting che nell’uncertainty quantification, di contro sono però molto costose e richiedono molte risorse di calcolo e di tempo per l’allenamento dei modelli. Esistono poi le tecniche semi-intrusive, i cui più conosciuti rappresentanti sono i Deep En- semble; esse rappresentano una ampia classe di approcci che in generale combinano più modelli secondo criteri specifici in modo da valutare l’efficienza, l’incertezza e l’affidabilità delle predizioni senza interferire troppo con le architetture di partenza, ma richiedendo comunque un ampio dispendio di risorse. In questo lavoro abbiamo deciso di utilizzare per i nostri scopi esclusivamente metodi post-hoc, cioè non intrusivi, come il Trust Score ed il Monte Carlo Dropout, che sono in grado di fare efficaci valutazioni sull’incertezza delle predizioni quando il modello è stato già allenato, senza andare a interferire con le fasi di apprendimento o a modificare i parametri già imparati dal modello durante la back propagation. Tali metodi sono leggermente meno performanti dei metodi intrusivi, ma hanno il vantaggio di essere estremamente più rapidi e meno costosi.

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2024 Conference article Open Access OPEN
From Covid-19 detection to cancer grading: how medical-AI is boosting clinical diagnostics and may improve treatment
Berti A., Buongiorno R., Carloni G., Caudai C., Conti F., Del Corso G., Germanese D., Moroni D., Pachetti E., Pascali M. A., Colantonio S.
The integration of artificial intelligence (AI) into medical imaging has guided an era of transformation in healthcare. This paper presents the research activities that a multidisciplinary research group within the Signals and Images Lab of the Institute of Information Science and Technologies of the National Research Council of Italy is carrying out to explore the great potential of AI in medical imaging. From the convolutional neural network-based segmentation of Covid-19 lung patterns to the radiomic signature for benign/malignant breast nodule discrimination, to the automatic grading of prostate cancer, this work highlights the paradigm shift that AI has brought to medical imaging, revolutionizing diagnosis and patient care.Source: CEUR WORKSHOP PROCEEDINGS, vol. 3762, pp. 336-341. Naples, Italy, 29-30/05/2024

See at: ceur-ws.org Open Access | CNR IRIS Open Access | CNR IRIS Restricted


2024 Journal article Open Access OPEN
ANN uncertainty estimates in assessing fatty liver content from ultrasound data
G. Del Corso, M. A. Pascali, C. Caudai, L. De Rosa, A. Salvati, M. Mancini, L. Ghiadoni, F. Bonino, M. R. Brunetto, S. Colantonio, F. Faita
Background and objective This article uses three different probabilistic convolutional architectures applied to ultrasound image analysis for grading Fatty Liver Content (FLC) in Metabolic Dysfunction Associated Steatotic Liver Disease (MASLD) patients. Steatosis is a new silent epidemic and its accurate measurement is an impelling clinical need, not only for hepatologists, but also for experts in metabolic and cardiovascular diseases. This paper aims to provide a robust comparison between different uncertainty quantification strategies to identify advantages and drawbacks in a real clinical setting. Methods We used a classical Convolutional Neural Network, a Monte Carlo Dropout, and a Bayesian Convolutional Neural Network with the goal of not only comparing the goodness of the predictions, but also to have access to an evaluation of the uncertainty associated with the outputs. Results We found that even if the prediction based on a single ultrasound view is reliable (relative RMSE [5.93%-12.04%]), networks based on two ultrasound views outperform them (relative RMSE [5.35%-5.87%]). In addition, the results show that the introduction of a “not confident” category contributes to increase the percentage of correctly predicted cases and to decrease the percentage of mispredicted cases, especially for semi-intrusive methods. Conclusions The possibility of having access to information about the confidence with which the network produces its outputs is a great advantage, both from the point of view of physicians who want to use neural networks as computer-aided diagnosis, and for developers who want to limit overfitting and obtain information about dataset problems in terms of out-of-distribution detection.Source: COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL, vol. 24, pp. 603-610

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2024 Journal article Open Access OPEN
Adaptive machine learning approach for importance evaluation of multimodal breast cancer radiomic features
Del Corso G., Germanese D., Caudai C., Anastasi G., Belli P., Formica A., Nicolucci A., Palma S., Pascali M. A., Pieroni S., Trombadori C., Colantonio S., Franchini M., Molinaro S.
Breast cancer holds the highest diagnosis rate among female tumors and is the leading cause of death among women. Quantitative analysis of radiological images shows the potential to address several medical challenges, including the early detection and classification of breast tumors. In the P.I.N.K study, 66 women were enrolled. Their paired Automated Breast Volume Scanner (ABVS) and Digital Breast Tomosynthesis (DBT) images, annotated with cancerous lesions, populated the first ABVS+DBT dataset. This enabled not only a radiomic analysis for the malignant vs. benign breast cancer classification, but also the comparison of the two modalities. For this purpose, the models were trained using a leave-one-out nested cross-validation strategy combined with a proper threshold selection approach. This approach provides statistically significant results even with medium-sized data sets. Additionally it provides distributional variables of importance, thus identifying the most informative radiomic features. The analysis proved the predictive capacity of radiomic models even using a reduced number of features. Indeed, from tomography we achieved AUC-ROC 89.9% using 19 features and 92.1% using 7 of them; while from ABVS we attained an AUC-ROC of 72.3% using 22 features and 85.8% using only 3 features. Although the predictive power of DBT outperforms ABVS, when comparing the predictions at the patient level, only 8.7% of lesions are misclassified by both methods, suggesting a partial complementarity. Notably, promising results (AUC-ROC ABVS-DBT 71.8% - 74.1% ) were achieved using non-geometric features, thus opening the way to the integration of virtual biopsy in medical routine.Source: JOURNAL OF IMAGING INFORMATICS IN MEDICINE
Project(s): "Mortalità Zero - verso la personalizzazione degli interventi diagnostici"

See at: CNR IRIS Open Access | link.springer.com Open Access | CNR IRIS Restricted | CNR IRIS Restricted


2024 Other Open Access OPEN
NA DAtabase: generator of probabilistic synthetic geometrical shape dataset
Volpini F., Caudai C., Del Corso G., Colantonio S.
In this technical report, we detail NA DA (Not-A-DAtabase), an open-source software writ- ten in Python that generates datasets of regular two-dimensional geometric shapes based on probabilistic distributions (https://github.com/GDelCorso/NA DAtabase.git). NA DA comes with an intuitive GUI (Graphical User Interface) that allows users to define shapes, colors, and distributions of features of datasets consisting of image sets and CSV files containing metadata for each element. These databases can be saved to provide a unique identifier of the dataset, allowing perfect reproducibility or easy modification of the dataset using the GUI or directly by calling the generator class. Therefore, NA DA is a tool to help and support the investigation of trustworthiness, overconfidence, uncertainty, and computation time of machine learning and deep learning models.

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2023 Conference article Open Access OPEN
Medical waste sorting: a computer vision approach for assisted primary sorting
Bruno A, Caudai C, Leone Gr, Martinelli M, Moroni D, Crotti F
Medical waste, i.e. waste produced during medical activities in hospitals, clinics and laboratories, represents hazardous waste whose management requires special care and high costs. However, this kind of waste contains a large fraction of highly valued materials that can enter a circular economy process. To this end, in this paper, we propose a computer vision approach for assisting in the primary sorting of med- ical waste. The feasibility of our approach is demonstrated on representative datasets we collected and made available to the community.

See at: CNR IRIS Open Access | ieeexplore.ieee.org Open Access | ISTI Repository Open Access | CNR IRIS Restricted | CNR IRIS Restricted


2023 Journal article Open Access OPEN
Raman spectroscopy and topological machine learning for cancer grading
Conti F, D'Acunto M, Caudai C, Colantonio C, Gaeta R, Moroni D, Pascali Ma
In the last decade, Raman Spectroscopy is establishing itself as a highly promising technique for the classification of tumour tissues as it allows to obtain the biochemical maps of the tissues under investigation, making it possible to observe changes among different tissues in terms of biochemical constituents (proteins, lipid structures, DNA, vitamins, and so on). In this paper, we aim to show that techniques emerging from the cross-fertilization of persistent homology and machine learning can support the classification of Raman spectra extracted from cancerous tissues for tumour grading. In more detail, topological features of Raman spectra and machine learning classifiers are trained in combination as an automatic classification pipeline in order to select the best-performing pair. The case study is the grading of chondrosarcoma in four classes: cross and leave-one-patient-out validations have been used to assess the classification accuracy of the method. The binary classification achieves a validation accuracy of 81% and a test accuracy of 90%. Moreover, the test dataset has been collected at a different time and with different equipment. Such results are achieved by a support vector classifier trained with the Betti Curve representation of the topological features extracted from the Raman spectra, and are excellent compared with the existing literature. The added value of such results is that the model for the prediction of the chondrosarcoma grading could easily be implemented in clinical practice, possibly integrated into the acquisition system.Source: SCIENTIFIC REPORTS, vol. 13

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2023 Contribution to book Open Access OPEN
Introduction to machine learning in medicine
Buongiorno R, Caudai C, Colantonio S, Germanese D
This chapter aimed to describe, as simply as possible, what Machine Learning is and how it can be used fruitfully in the medical field.DOI: 10.1007/978-3-031-25928-9_3
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2023 Journal article Open Access OPEN
Mediterranean extensive green roof self-sustainability mediated by substrate composition and plant strategy
Vannucchi F., Bibbiani C., Caudai C., Bretzel F.
In the cultivation of extensive green roofs (EGRs), substrate composition is a key aspect together with the evaluation of suitable recycled materials. Recycling materials as amendments can improve the establishment of a self-sustainable EGR, thus providing ecosystem services and benefits from a circular economy and climate change perspective. This study investigates the effects of compost and paper sludge on water retention, substrate temperature attenuation and plant diversity in an EGR experiment. The substrates were composed of tephra (V), compost (C) and paper sludge (P) as follows: VC, as control, VPC and VP. Herbaceous species with different ecological functionality (succulents, annuals, perennials, legumes, geophytes) were sown and/or transplanted with no cultivation inputs. Plant community composition -abundance- and diversity-richness-, substrate water retention and temperature were analyzed. The VPC and VC had the same average substrate temperature, with values lower than VP. The water retention capacity was higher in VC, thanks to the presence of compost. The substrate with paper sludge (VPC and VP) showed the highest species diversity. The VPC substrate was the best compromise for EGR temperature mitigation and plant diversity improvement. Plant functional types in EGRs can be increased, and thus the biodiversity, by modulating the quality and percentage of amendments. The substrate composition can also affect water retention and substrate temperature. In addition, the use of recycling paper sludge in growing media is a winning strategy to reduce waste.Source: HORTICULTURAE, vol. 9 (issue 10)

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2023 Conference article Open Access OPEN
Exploring the potentials and challenges of AI in supporting clinical diagnostics and remote assistance for the health and well-being of individuals
Berti A, Buongiorno R, Carloni G, Caudai C, Del Corso G, Germanese D, Pachetti E, Pascali Ma, Colantonio S
Innovative technologies powered by Artificial Intelligence have the big potential to support new models of care delivery, disease prevention and quality of life promotion. The ultimate goal is a paradigm shift towards more personalized, accessible, effective, and sustainable care and health systems. Nevertheless, despite the advances in the field over the last years, the adoption and deployment of AI technologies remains limited in clinical practice and real-world settings. This paper summarizes the activities that a multidisciplinary research group within the Signals and Images Lab of the Institute of Information Science and Technologies of the National Research Council of Italy is carrying out for exploring both the potential of AI in health and well-being as well as the challenges to their uptake in real-world settingsSource: CEUR WORKSHOP PROCEEDINGS. Pisa, Italy, 29-30/05/2023
Project(s): ProCAncer-I via OpenAIRE

See at: ceur-ws.org Open Access | CNR IRIS Open Access | ISTI Repository Open Access | CNR IRIS Restricted


2023 Conference article Open Access OPEN
Exploring the potentials and challenges of Artificial Intelligence in supporting clinical diagnostics and remote assistance for the health and well-being of individuals
Berti A., Buongiorno R., Carloni G., Caudai C., Del Corso G., Germanese D., Pachetti E., Pascali M. A., Colantonio S.
Innovative technologies powered by Artificial Intelligence have the big potential to support new models of care delivery, disease prevention and quality of life promotion. The ultimate goal is a paradigm shift towards more personalized, accessible, effective, and sustainable care and health systems. Nevertheless, despite the advances in the field over the last years, the adoption and deployment of AI technologies remains limited in clinical practice and real-world settings. This paper summarizes the activities that a multidisciplinary research group within the Signals and Images Lab of the Institute of Information Science and Technologies of the National Research Council of Italy is carrying out for exploring both the potential of AI in health and well-being as well as the challenges to their uptake in real-world settings.Source: CEUR WORKSHOP PROCEEDINGS, vol. 3486. Pisa, Italy, 29-30/05/2023

See at: ceur-ws.org Open Access | CNR IRIS Open Access | CNR IRIS Restricted


2022 Journal article Open Access OPEN
Short-term abandonment versus mowing in a mediterranean-temperate meadow: effects on floristic composition, plant functionality, and soil properties - a case study
Vannucchi F, Lazzeri V, Rosellini I, Scatena M, Caudai C, Bretzel F
Hay meadows are secondary grasslands maintained by mowing, and their ecological importance resides in the inherent biodiversity and carbon stocking. We investigated the plant community and soil properties of a sub humid acid grassland near the Fucecchio marshes (Italy), managed as a hay meadow, mowed once a year, and not fertilized. Part of the meadow had been abandoned for three years. We analysed the soil properties (i.e., organic carbon and total nitrogen content, available phosphorus, pH, cation-exchange capacity, texture, and conductibility) and the plant community structure (composition, functionality, and species richness) of the two sides of the meadow (mowed and abandoned). Our aim was to highlight the changes in soil properties and vegetation community, and to find out to what extent abandonment can affect those dynamics. Our results showed that after short-term abandonment, soil pH, C and N increased; litter biomass and perennial forbs increased; and annual forbs decreased. New species colonising after abandonment, thus enriching the flora, may keep spreading and eventually hinder the growth of the specialists if mowing is not resumed. Certain valuable meadow habitats need constant human intervention to maintain their peculiar vegetation, most especially if they are a buffer zone in the proximity of natural protected areas.Source: AGRICULTURE, vol. 12 (issue 1)

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2022 Journal article Open Access OPEN
Low productivity substrate leads to functional diversification of green roof plant assemblage
Vannucchi F, Buoncristiano A, Scatena M, Caudai C, Bretzel F
Green roofs are roof free spaces where living organisms can find an appropriate habitat to colonise. The establishment of plant species with different functionality can enhance biodiversity and provide ecosystem services. However, drought and nutrient availability can affect the plant development. The extensive green roof was set up in Pisa (Italy) in 2014, 12 modules of 10 cm depth were filled with three substrates composed of compost from municipal mixed waste, pelletised paper sludge, and commercial tephra product (Vulcaflor), as follows: Vulcaflor + compost, Vulcaflor + pellet + compost, and Vulcaflor + pellet, characterised by decreasing level of nitrogen content. The species planted in 2014 were chosen from the herbaceous spontaneous vegetation of urban and rural swards not often mowed, plus two sedum species. After the establishment phase, the green roof community was progressively dominated by Sedum species and other species were seeded in 2016. In 2018-19 the plant functional types and the community structure were monitored. Besides seasonal fluctuations, nitrogen shaped the composition of the community, and Sedum species showed high cover values in nitrogen-richer substrates. Annual forbs colonised the plots with a lower nitrogen content. In summer, the number of species drastically fell, and Sedum album was dominant in the three substrates. Seedling recruitment regenerated the community in the cooler season, increasing the diversity in the poor substrate. The scarcity of nitrogen led to the development of stress-tolerator annuals increasing the biodiversity in the rainy-cool season. Annual species constitute a transient seed bank which enables the system to regenerate when rain follows periods of heat and drought.Source: ECOLOGICAL ENGINEERING, vol. 176

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2022 Conference article Open Access OPEN
Data models for an imaging bio-bank for colorectal, prostate and gastric cancer: the NAVIGATOR project
Berti A, Carloni G, Colantonio S, Pascali Ma, Manghi P, Pagano P, Buongiorno R, Pachetti E, Caudai C, Di Gangi D, Carlini E, Falaschi Z, Ciarrocchi E, Neri E, Bertelli E, Miele V, Carpi R, Bagnacci G, Di Meglio N, Mazzei Ma, Barucci A
Researchers nowadays may take advantage of broad collections of medical data to develop personalized medicine solutions. Imaging bio-banks play a fundamental role, in this regard, by serving as organized repositories of medical images associated with imaging biomarkers. In this context, the NAVIGATOR Project aims to advance colorectal, prostate, and gastric oncology translational research by leveraging quantitative imaging and multi-omics analyses. As Project's core, an imaging bio-bank is being designed and implemented in a web-accessible Virtual Research Environment (VRE). The VRE serves to extract the imaging biomarkers and further process them within prediction algorithms. In our work, we present the realization of the data models for the three cancer use-cases of the Project. First, we carried out an extensive requirements analysis to fulfill the necessities of the clinical partners involved in the Project. Then, we designed three separate data models utilizing entity-relationship diagrams. We found diagrams' modeling for colorectal and prostate cancers to be more straightforward, while gastric cancer required a higher level of complexity. Future developments of this work would include designing a common data model following the Observational Medical Outcomes Partnership Standards. Indeed, a common data model would standardize the logical infrastructure of data models and make the bio-bank easily interoperable with other bio-banks.

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2022 Journal article Open Access OPEN
NAVIGATOR: an Italian regional imaging biobank to promote precision medicine for oncologic patients
Borgheresi R, Barucci A, Colantonio S, Aghakhanyan G, Assante M, Bertelli E, Carlini E, Carpi R, Caudai C, Cavallero D, Cioni D, Cirillo R, Colcelli V, Dell'Amico A, Di Gangi D, Erba Pa, Faggioni L, Falaschi Z, Gabelloni M, Gini R, Lelii L, Liò P, Lorito A, Lucarini S, Manghi P, Mangiacrapa F, Marzi C, Mazzei Ma, Mercatelli L, Mirabile A, Mungai F, Miele V, Olmastroni M, Pagano P, Paiar F, Panichi G, Pascali Ma, Pasquinelli F, Shortrede Je, Tumminello L, Volterrani L, Neri E, On Behalf Of The Navigator Consortium Group
NAVIGATOR is an Italian regional project to boost precision medicine in oncology with the aim to make it more predictive, preventive, and personalised by advancing translational research based on quantitative imaging and integrative omics analyses. The project's goal is to develop an open imaging biobank for the collection and preservation of a large amount of standardised imaging multimodal datasets, including computed tomography, magnetic resonance imaging, and positron emission tomography data, together with the corresponding patient-related and omics-related relevant information extracted from regional healthcare services using an adapted privacy-preserving model. The project is based on an open-source imaging biobank and an open-science oriented virtual research environment (VRE). Available integrative omics and multi-imaging data of three use cases (prostate cancer, rectal cancer, and gastric cancer) will be collected. All data confined in NAVIGATOR (i.e. standard and novel imaging biomarkers, non-imaging data, health agency data) will be used to create a digital patient model, to support the reliable prediction of the disease phenotype and risk stratification. The VRE that relies on a well-established infrastructure, called D4Science.org, will further provide a multiset infrastructure for processing the integrative omics data, extracting specific radiomic signatures, and for identification and testing of novel imaging biomarkers through big data analytics and artificial intelligence.Source: EUROPEAN RADIOLOGY EXPERIMENTAL, vol. 6 (issue 53)

See at: eurradiolexp.springeropen.com Open Access | CNR IRIS Open Access | ISTI Repository Open Access | CNR IRIS Restricted


2022 Other Open Access OPEN
SI-Lab annual research report 2021
Righi M, Leone G R, Carboni A, Caudai C, Colantonio S, Kuruoglu E E, Leporini B, Magrini M, Paradisi P, Pascali M A, Pieri G, Reggiannini M, Salerno E, Scozzari A, Tonazzini A, Fusco G, Galesi G, Martinelli M, Pardini F, Tampucci M, Berti A, Bruno A, Buongiorno R, Carloni G, Conti F, Germanese D, Ignesti G, Matarese F, Omrani A, Pachetti E, Papini O, Benassi A, Bertini G, Coltelli P, Tarabella L, Straface S, Salvetti O, Moroni D
The Signal & Images Laboratory is an interdisciplinary research group in computer vision, signal analysis, intelligent vision systems and multimedia data understanding. It is part of the Institute of Information Science and Technologies (ISTI) of the National Research Council of Italy (CNR). This report accounts for the research activities of the Signal and Images Laboratory of the Institute of Information Science and Technologies during the year 2021.DOI: 10.32079/isti-ar-2022/003
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2021 Journal article Open Access OPEN
Integration of multiple resolution data in 3D chromatin reconstruction using ChromStruct
Caudai C, Zoppè M, Tonazzini A, Merelli I, Salerno E
The three-dimensional structure of chromatin in the cellular nucleus carries important information that is connected to physiological and pathological correlates and dysfunctional cell behaviour. As direct observation is not feasible at present, on one side, several experimental techniques have been developed to provide information on the spatial organization of the DNA in the cell; on the other side, several computational methods have been developed to elaborate experimental data and infer 3D chromatin conformations. The most relevant experimental methods are Chromosome Conformation Capture and its derivatives, chromatin immunoprecipitation and sequencing techniques (CHIP-seq), RNA-seq, fluorescence in situ hybridization (FISH) and other genetic and biochemical techniques. All of them provide important and complementary information that relate to the three-dimensional organization of chromatin. However, these techniques employ very different experimental protocols and provide information that is not easily integrated, due to different contexts and different resolutions. Here, we present an open-source tool, which is an expansion of the previously reported code ChromStruct, for inferring the 3D structure of chromatin that, by exploiting a multilevel approach, allows an easy integration of information derived from different experimental protocols and referred to different resolution levels of the structure, from a few kilobases up to Megabases. Our results show that the introduction of chromatin modelling features related to CTCF CHIA-PET data, histone modification CHIP-seq, and RNA-seq data produce appreciable improvements in ChromStruct's 3D reconstructions, compared to the use of HI-C data alone, at a local level and at a very high resolution.Source: BIOLOGY, vol. 10 (issue 4), p. 338

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