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2023
Contribution to book
Open Access
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.Source: Introduction to Artificial Intelligence, edited by Klontzas M.E., Fanni S.C., Neri E., pp. 39–68. Basel: Springer Nature Switzerland, 2023
DOI: 10.1007/978-3-031-25928-9_3
Metrics:
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.Source: Introduction to Artificial Intelligence, edited by Klontzas M.E., Fanni S.C., Neri E., pp. 39–68. Basel: Springer Nature Switzerland, 2023
DOI: 10.1007/978-3-031-25928-9_3
Metrics:
See at:
ISTI Repository 
| doi.org 
| CNR ExploRA
2014
Report
Open Access
WP1-CNR-ISTI - Ricostruzione di reti di regolazione genica da dati trascrittomici
Caudai C.
Some recently proposed approaches to identify genetic regulatory mechanisms from sequencing experiments are briefly described. Since gene expression is influenced by biologica processes in the cell, and both the processes and the related transcription factors are largely unknown, many attempts have been made to solve this problem by blind source separation techniques. We analyse some of these approaches, each based on a linear data model.Source: Project report, Bandiera InterOmics, 2014
Caudai C.
Some recently proposed approaches to identify genetic regulatory mechanisms from sequencing experiments are briefly described. Since gene expression is influenced by biologica processes in the cell, and both the processes and the related transcription factors are largely unknown, many attempts have been made to solve this problem by blind source separation techniques. We analyse some of these approaches, each based on a linear data model.Source: Project report, Bandiera InterOmics, 2014
See at:
ISTI Repository | CNR ExploRA
2015
Report
Open Access
Architettura ISTI per il progetto INTEROMICS
Caudai C., Righi M., Tampucci M.
Il progetto INTEROMICS (Sviluppo di una piattaforma integrata per l'applicazione delle scienze "omiche" alla definizione dei biomarcatori e profili diagnostici, predittivi e teranostici) prevede lo sviluppo delle competenze per l'intera filiera delle "scienze omiche", con particolare riferimento alla genomica, proteomica, bioinformatica e system biology. Il laboratorio concentra le sue attività nel campo della bioinformatica e dell'analisi delle immagini per impieghi in biologia.Source: ISTI Technical reports, 2015
Caudai C., Righi M., Tampucci M.
Il progetto INTEROMICS (Sviluppo di una piattaforma integrata per l'applicazione delle scienze "omiche" alla definizione dei biomarcatori e profili diagnostici, predittivi e teranostici) prevede lo sviluppo delle competenze per l'intera filiera delle "scienze omiche", con particolare riferimento alla genomica, proteomica, bioinformatica e system biology. Il laboratorio concentra le sue attività nel campo della bioinformatica e dell'analisi delle immagini per impieghi in biologia.Source: ISTI Technical reports, 2015
See at:
ISTI Repository | CNR ExploRA
2016
Journal article
Open Access
Computational estimation of chromosome structure
Caudai C., Salerno E.
Research performed at ISTI-CNR in the framework of the national Flagship Project InterOmics includes the development of algorithms to reconstruct the chromosome structure from "chromosome conformation capture" data. One algorithm now being tested has already produced interesting results. Unlike most popular techniques, it does not derive a classical distance-to-geometry problem from the original contact data, and applies an efficient multiresolution approach to the genome under study.Source: ERCIM news 104 (2016): 21–22.
Caudai C., Salerno E.
Research performed at ISTI-CNR in the framework of the national Flagship Project InterOmics includes the development of algorithms to reconstruct the chromosome structure from "chromosome conformation capture" data. One algorithm now being tested has already produced interesting results. Unlike most popular techniques, it does not derive a classical distance-to-geometry problem from the original contact data, and applies an efficient multiresolution approach to the genome under study.Source: ERCIM news 104 (2016): 21–22.
See at:
ercim-news.ercim.eu | CNR ExploRA
2017
Report
Open Access
Comparison between ChromStruct4 and TADbit
Caudai C., Salerno E., Zoppé M., Tonazzini A.
In this report performances of ChromStruct4 and tadbit have been compared. These are two methods for the inference of chromatin three-dimensional conformations starting from Chromosome Conforma- tion Capture data. tadbit and ChromStruct4 have been tested against the same data sets from real hi-c experiments. With comparative experi- ments, also robustness of ChromStruct4 against biases have been tested.Source: ISTI Technical reports, 2017
Caudai C., Salerno E., Zoppé M., Tonazzini A.
In this report performances of ChromStruct4 and tadbit have been compared. These are two methods for the inference of chromatin three-dimensional conformations starting from Chromosome Conforma- tion Capture data. tadbit and ChromStruct4 have been tested against the same data sets from real hi-c experiments. With comparative experi- ments, also robustness of ChromStruct4 against biases have been tested.Source: ISTI Technical reports, 2017
See at:
ISTI Repository | CNR ExploRA
2014
Report
Open Access
Ricostruzione tridimensionale della struttura della cromatina da dati tipo Chromosome Conformation Capture. Nota riservata, progetto InterOmics
Caudai C.
This document reports part of the WP1-ISTI unit activity in the framework of the national Flagship Project InterOmics, on the 3D chromatin structure analysis from Hi-C experiments. Compared with other methods presented in the literature, our approach uses a new solution model incorporating sound prior knowledge, and a new reconstruction criterion that does not require an explicit translation of the Hi-C data into Euclidean distances between pairs of genomic loci. This approach also allows us to solve the problem in a multiscale setting, by reconstructing significant fragments separately at high resolution and then putting them together through the same criterion applied to lower-resolution data.Source: Project report, Bandiera InterOmics, 2014
Caudai C.
This document reports part of the WP1-ISTI unit activity in the framework of the national Flagship Project InterOmics, on the 3D chromatin structure analysis from Hi-C experiments. Compared with other methods presented in the literature, our approach uses a new solution model incorporating sound prior knowledge, and a new reconstruction criterion that does not require an explicit translation of the Hi-C data into Euclidean distances between pairs of genomic loci. This approach also allows us to solve the problem in a multiscale setting, by reconstructing significant fragments separately at high resolution and then putting them together through the same criterion applied to lower-resolution data.Source: Project report, Bandiera InterOmics, 2014
See at:
ISTI Repository | CNR ExploRA
2016
Software
Unknown
Reconstruction of 3D chromatin structure from chromosome conformation capture data (Release 2.0)
Salerno E., Caudai C.
This Python code provides an estimate of the 3D structure of the chromatin fibre in cell nuclei from the contact frequency data produced by a 'Chromosome conformation capture' experiment. The only input required is a text file containing a general real matrix of contact frequencies. The related genomic resolution, along with a few geometric parameters and the parameters for tuning the estimation algorithm must be set in advance in a special section of the source code. The whole fibre is divided in independent segments, whose structures are estimated and modelled as single elements of a lower-resolution fibre that is treated recursively in the same way, until it cannot be divided anymore into independent segments. The full-resolution chain is then reconstructed by another recursive procedure.
Salerno E., Caudai C.
This Python code provides an estimate of the 3D structure of the chromatin fibre in cell nuclei from the contact frequency data produced by a 'Chromosome conformation capture' experiment. The only input required is a text file containing a general real matrix of contact frequencies. The related genomic resolution, along with a few geometric parameters and the parameters for tuning the estimation algorithm must be set in advance in a special section of the source code. The whole fibre is divided in independent segments, whose structures are estimated and modelled as single elements of a lower-resolution fibre that is treated recursively in the same way, until it cannot be divided anymore into independent segments. The full-resolution chain is then reconstructed by another recursive procedure.
See at: CNR ExploRA
2018
Software
Unknown
ChromStruct v4.2 - Reconstruction of 3D chromatin structure from chromosome conformation capture data
Salerno E., Caudai C.
This Python (v.2.7.10) code provides an estimate of the 3D structure of the chromatin fibre in cell nuclei from the contact frequency data produced by a 'Chromosome conformation capture' experiment. The only input required is a text file containing a general real matrix of contact frequencies. The code features a GUI where all the tuneable parameters are made available to the user. The fibre is divided in independent segments whose structures are first estimated separately and then modelled as single elements of a lower-resolution fibre, which is treated iteratively in the same way until it cannot be divided anymore into independent segments. The full-resolution chain is then reconstructed by another iterative procedure. See the Readme file and the cited references for more detail.
Salerno E., Caudai C.
This Python (v.2.7.10) code provides an estimate of the 3D structure of the chromatin fibre in cell nuclei from the contact frequency data produced by a 'Chromosome conformation capture' experiment. The only input required is a text file containing a general real matrix of contact frequencies. The code features a GUI where all the tuneable parameters are made available to the user. The fibre is divided in independent segments whose structures are first estimated separately and then modelled as single elements of a lower-resolution fibre, which is treated iteratively in the same way until it cannot be divided anymore into independent segments. The full-resolution chain is then reconstructed by another iterative procedure. See the Readme file and the cited references for more detail.
See at: CNR ExploRA | www.researchgate.net
2021
Contribution to conference
Unknown
Imaging e radiomica nell'ambito del progetto P.I.N.K.
Caudai C., Colantonio S., Franchini M., Molinaro S., Pascali M. A., Pieroni S., Salvatori M.
La presentazione introduce la linea di sviluppo dedicata alla radiomica nell'ambito dello studio P.I.N.K. Vengono introdotti gli aspetti e le potenzialità di Radiomics and Deep Learning per l'imaging medico , suportatti da alcuni esempi di applicazione. Vengono indicate le linee organizzative per implementare questa linea di sviluppo all'interno dello studio, affrontando gli aspetti tecnologici e modalità di attuazione previste.Source: Terzo Webinar del ciclo Agorà P.I.N.K, 21/6/2021
Caudai C., Colantonio S., Franchini M., Molinaro S., Pascali M. A., Pieroni S., Salvatori M.
La presentazione introduce la linea di sviluppo dedicata alla radiomica nell'ambito dello studio P.I.N.K. Vengono introdotti gli aspetti e le potenzialità di Radiomics and Deep Learning per l'imaging medico , suportatti da alcuni esempi di applicazione. Vengono indicate le linee organizzative per implementare questa linea di sviluppo all'interno dello studio, affrontando gli aspetti tecnologici e modalità di attuazione previste.Source: Terzo Webinar del ciclo Agorà P.I.N.K, 21/6/2021
See at: CNR ExploRA
2023
Conference article
Open Access
Medical waste sorting: a computer vision approach for assisted primary sorting
Bruno A., Caudai C., Leone G. R., 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.Source: IWCIM2023 - 11th International Workshop on Computational Intelligence for Multimedia Understanding, Rhodes Island, Greece, 05/06/2023
DOI: 10.1109/icasspw59220.2023.10193520
Metrics:
Bruno A., Caudai C., Leone G. R., 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.Source: IWCIM2023 - 11th International Workshop on Computational Intelligence for Multimedia Understanding, Rhodes Island, Greece, 05/06/2023
DOI: 10.1109/icasspw59220.2023.10193520
Metrics:
See at:
ISTI Repository | ieeexplore.ieee.org | CNR ExploRA
2015
Report
Open Access
InterOmics - Reconstructing 3D chromatin structure from chromosome conformation capture data.
Caudai C., Salerno E., Zoppè M., Tonazzini A.
Dna is the central repository of information to keep cells and organisms alive. In human cells, the 46 chromosomes amount to a length of about 2 m, with a diameter of 2 nm, and are packed in a way that allows for access by transcription, replication and repair machinery, fitting within a globular nucleus with a radius of 5000 to 10000 nm. Efficiency of packing is obtained by several levels of packing mechanisms (Figure 1), both general (due to general principles, irrespective of Dna sequence) and speci fic, i.e. mediated by proteins that recognize specifi c motives (sequences) and bring in close proximity parts of Dna that may be very distant in the genomic sequence. In both cases, general packing and specifi c aggregation, the underlying mechanisms are not entirely described or understood. The fi rst level, mediated by histon octamers, produces a ber of 11 nm, which in turn is organized into a 30 nm-wide structure. Further packing is at work in cells, and the research community engaged in the study of chromatin conformation is producing increasing knowledge that will finally allow for a clear vision of the nuclear machinery that regulates Dna metabolism.Source: Project report, InterOmics, 2015
Caudai C., Salerno E., Zoppè M., Tonazzini A.
Dna is the central repository of information to keep cells and organisms alive. In human cells, the 46 chromosomes amount to a length of about 2 m, with a diameter of 2 nm, and are packed in a way that allows for access by transcription, replication and repair machinery, fitting within a globular nucleus with a radius of 5000 to 10000 nm. Efficiency of packing is obtained by several levels of packing mechanisms (Figure 1), both general (due to general principles, irrespective of Dna sequence) and speci fic, i.e. mediated by proteins that recognize specifi c motives (sequences) and bring in close proximity parts of Dna that may be very distant in the genomic sequence. In both cases, general packing and specifi c aggregation, the underlying mechanisms are not entirely described or understood. The fi rst level, mediated by histon octamers, produces a ber of 11 nm, which in turn is organized into a 30 nm-wide structure. Further packing is at work in cells, and the research community engaged in the study of chromatin conformation is producing increasing knowledge that will finally allow for a clear vision of the nuclear machinery that regulates Dna metabolism.Source: Project report, InterOmics, 2015
See at:
ISTI Repository | CNR ExploRA
2015
Contribution to book
Open Access
A statistical approach to infer 3D chromatin structure
Caudai C., Salerno E., Zoppé M., Tonazzini A.
We propose a new algorithm to estimate the 3D configuration of a chromatin chain from the contact frequency data provided by HI-C experiments. Since the data originate from a population of cells, we rather aim at obtaining a set of structures that are compatible with both the data and our prior knowledge. Our method overcomes some drawbacks presented by other state-of-the-art methods, including the problems related to the translation of contact frequencies into Euclidean distances. Indeed, such a translation always produces a geometrically inconsistent distance set. Our multiscale chromatin model and our probabilistic solution approach allow us to partition the problem, thus speeding up the solution, to include suitable constraints, and to get multiple feasible structures. Moreover, the density function we use to sample the solution space does not require any translation from contact frequencies into distances.Source: Mathematical Models in Biology. Bringing Mathematics to Life, edited by Valeria Zazzu, Maria Brigida Ferraro, Mario R. Guarracino, pp. 161–171. Berlin Heidelberg: Springer, 2015
DOI: 10.1007/978-3-319-23497-7_12
Metrics:
Caudai C., Salerno E., Zoppé M., Tonazzini A.
We propose a new algorithm to estimate the 3D configuration of a chromatin chain from the contact frequency data provided by HI-C experiments. Since the data originate from a population of cells, we rather aim at obtaining a set of structures that are compatible with both the data and our prior knowledge. Our method overcomes some drawbacks presented by other state-of-the-art methods, including the problems related to the translation of contact frequencies into Euclidean distances. Indeed, such a translation always produces a geometrically inconsistent distance set. Our multiscale chromatin model and our probabilistic solution approach allow us to partition the problem, thus speeding up the solution, to include suitable constraints, and to get multiple feasible structures. Moreover, the density function we use to sample the solution space does not require any translation from contact frequencies into distances.Source: Mathematical Models in Biology. Bringing Mathematics to Life, edited by Valeria Zazzu, Maria Brigida Ferraro, Mario R. Guarracino, pp. 161–171. Berlin Heidelberg: Springer, 2015
DOI: 10.1007/978-3-319-23497-7_12
Metrics:
See at:
ISTI Repository | doi.org | link.springer.com | CNR ExploRA
2016
Report
Open Access
Consistency tests for a recursive multi-scale 3D chromatin structure reconstruction algorithm
Caudai C., Salerno E., Zoppè M., Tonazzini A.
In this report, we test the consistency and coherence of an algorithm obtained as an extension of a technique we proposed in the past. This implements a recursive multi-scale reconstruction of the 3d chromatin structure from Chromosome Conformation Capture data. These data derive from millions of cells, so we cannot expect that they lead to a unique solution; for this reason, we adopt a statistic approach to sample the space of the solutions generated by a suitable objective function, in order to achieve congurations compatible with the input data and the known constraints. The consistency of the algorithm has been tested by producing a large number of results and evaluating the dispersion of the nal values of the objective function. Using the same solutions, synthetic contact matrices have been produced and compared with the input matrix to test the coherence of our solutions with the initial data. Furthermore, we investigated the presence of typical structures in the solutions by hierarchical clustering.Source: Project report, InterOmic, 2016
Caudai C., Salerno E., Zoppè M., Tonazzini A.
In this report, we test the consistency and coherence of an algorithm obtained as an extension of a technique we proposed in the past. This implements a recursive multi-scale reconstruction of the 3d chromatin structure from Chromosome Conformation Capture data. These data derive from millions of cells, so we cannot expect that they lead to a unique solution; for this reason, we adopt a statistic approach to sample the space of the solutions generated by a suitable objective function, in order to achieve congurations compatible with the input data and the known constraints. The consistency of the algorithm has been tested by producing a large number of results and evaluating the dispersion of the nal values of the objective function. Using the same solutions, synthetic contact matrices have been produced and compared with the input matrix to test the coherence of our solutions with the initial data. Furthermore, we investigated the presence of typical structures in the solutions by hierarchical clustering.Source: Project report, InterOmic, 2016
See at:
ISTI Repository | CNR ExploRA
2016
Software
Unknown
Reconstruction of 3D chromatin structure from chromosome conformation capture data (Release 3.1)
Salerno E., Caudai C.
This Python code has a command-line and a GUI versions, and provides an estimate of the 3D structure of the chromatin fibre in cell nuclei from the contact frequency data produced by a 'Chromosome conformation capture' experiment. The only input required is a text file containing a general real matrix of contact frequencies. In the command-line version, the related genomic resolution, along with a few geometric parameters and the parameters for tuning the estimation algorithm must be set in advance in a special section of the source code. In the GUI version, all the tuneable parameters are made available in the user interface. The whole fibre is divided in independent segments, whose structures are estimated and modelled as single elements of a lower-resolution fibre that is treated recursively in the same way, until it cannot be divided anymore into independent segments. The full-resolution chain is then reconstructed by another recursive procedure. See the Readme file and the cited references for more detail.DOI: 10.13140/rg.2.2.35785.13923
Metrics:
Salerno E., Caudai C.
This Python code has a command-line and a GUI versions, and provides an estimate of the 3D structure of the chromatin fibre in cell nuclei from the contact frequency data produced by a 'Chromosome conformation capture' experiment. The only input required is a text file containing a general real matrix of contact frequencies. In the command-line version, the related genomic resolution, along with a few geometric parameters and the parameters for tuning the estimation algorithm must be set in advance in a special section of the source code. In the GUI version, all the tuneable parameters are made available in the user interface. The whole fibre is divided in independent segments, whose structures are estimated and modelled as single elements of a lower-resolution fibre that is treated recursively in the same way, until it cannot be divided anymore into independent segments. The full-resolution chain is then reconstructed by another recursive procedure. See the Readme file and the cited references for more detail.DOI: 10.13140/rg.2.2.35785.13923
Metrics:
See at: CNR ExploRA
2017
Journal article
Open Access
The SENSEable Pisa project: citizen-participation in monitoring acoustic climate of Mediterranean city centres
Vinci B., Tonacci A., Caudai C., De Rosa P., Nencini L., Pratali L.
The concept of urban sustainability and liveability closely depends on multi-level approaches to environmental issues. The ultimate goal in the field of noise management is to involve citizens and facilitate their participation in urban environmental decisions. The SENSEable Pisa project, based on the concept of Real-Time City and Smart City, presents an acoustic urban monitoring system based on a low-cost data acquisition method for a pervasive outdoor noise measurement. The system is based on the use of noise sensors located on private houses in the centre of Pisa, which provide a good model for the current acoustic climate of Mediterranean city centres. In this study, SENSEable acquisitions show a strong anthropogenic component not revealed by public strategic maps. The anthropogenic component, commonly known as movida, becomes increasingly critical in Mediterranean cities, therefore, it is necessary to explore methods highlighting this new source and to adopt strategies for the creation of reliable noise pollution maps.Source: Clean (Weinh., Internet) 45 (2017). doi:10.1002/clen.201600137
DOI: 10.1002/clen.201600137
Metrics:
Vinci B., Tonacci A., Caudai C., De Rosa P., Nencini L., Pratali L.
The concept of urban sustainability and liveability closely depends on multi-level approaches to environmental issues. The ultimate goal in the field of noise management is to involve citizens and facilitate their participation in urban environmental decisions. The SENSEable Pisa project, based on the concept of Real-Time City and Smart City, presents an acoustic urban monitoring system based on a low-cost data acquisition method for a pervasive outdoor noise measurement. The system is based on the use of noise sensors located on private houses in the centre of Pisa, which provide a good model for the current acoustic climate of Mediterranean city centres. In this study, SENSEable acquisitions show a strong anthropogenic component not revealed by public strategic maps. The anthropogenic component, commonly known as movida, becomes increasingly critical in Mediterranean cities, therefore, it is necessary to explore methods highlighting this new source and to adopt strategies for the creation of reliable noise pollution maps.Source: Clean (Weinh., Internet) 45 (2017). doi:10.1002/clen.201600137
DOI: 10.1002/clen.201600137
Metrics:
See at:
ISTI Repository | CLEAN - Soil Air Water | onlinelibrary.wiley.com | CNR ExploRA
2017
Contribution to conference
Open Access
3D Chromatin structure estimation from chromosome conformation capture data
Caudai C., Salerno E., Zoppè M., Tonazzini A.
In this communication we describe ChromStruct4, a method to reconstruct a set of plausible chromatin configurations starting from contact data obtained through Chromosome Conformation Capture techniques. Chromating fibre is modeled as a kinematic chain made of consecutive and partially penetrable beads whose properties (bead size, elasticity, curvature, etc.) can be suitably constrained. The chain can be divided in segments corresonding to Topological Association Domains. We do not search for a unique consensus configuration, because the experimental data are not derived from a single cell, but from millions of cells. We use a coarse-grained recoursive approach, based on a Simulated Annealing algorithm in order to sample the solution space. As opposed to most popular methods, we do not translate contact frequencies deterministically into distances, since this often produces structures that are not consistent with the Euclidean geometry, but adopt the assumption that loci with very high contact frequencies are actually close, but loci with low contact frequencies are not necessarily far away. ChromStruct4 is tested against real Hi-C data and compared with other methods for the 3-dimesional reconstruction fo Chromatin structure starting from Chromosome Conformation Capture data.Source: BITS 2017 - 14th Annual Meeting of the Bioinformatics Italian Society, Cagliari, Italy, 5-7 July 2017
Caudai C., Salerno E., Zoppè M., Tonazzini A.
In this communication we describe ChromStruct4, a method to reconstruct a set of plausible chromatin configurations starting from contact data obtained through Chromosome Conformation Capture techniques. Chromating fibre is modeled as a kinematic chain made of consecutive and partially penetrable beads whose properties (bead size, elasticity, curvature, etc.) can be suitably constrained. The chain can be divided in segments corresonding to Topological Association Domains. We do not search for a unique consensus configuration, because the experimental data are not derived from a single cell, but from millions of cells. We use a coarse-grained recoursive approach, based on a Simulated Annealing algorithm in order to sample the solution space. As opposed to most popular methods, we do not translate contact frequencies deterministically into distances, since this often produces structures that are not consistent with the Euclidean geometry, but adopt the assumption that loci with very high contact frequencies are actually close, but loci with low contact frequencies are not necessarily far away. ChromStruct4 is tested against real Hi-C data and compared with other methods for the 3-dimesional reconstruction fo Chromatin structure starting from Chromosome Conformation Capture data.Source: BITS 2017 - 14th Annual Meeting of the Bioinformatics Italian Society, Cagliari, Italy, 5-7 July 2017
See at:
ISTI Repository | CNR ExploRA
2019
Journal article
Open Access
Estimation of the spatial chromatin structure based on a multiresolution bead-chain model
Caudai C., Salerno E., Zoppe M., Tonazzini A.
We present a method to infer 3D chromatin configurations from Chromosome Conformation Capture data. Quite a few methods have been proposed to estimate the structure of the nuclear DNA in homogeneous populations of cells from this kind of data. Many of them transform contact frequencies into Euclidean distances between pairs of chromatin fragments, and then reconstruct the structure by solving a distance-to-geometry problem. To avoid inconsistencies, our method is based on a score function that does not require any frequency-to-distance translation. We propose a multiscale chromatin model where the chromatin fibre is suitably partitioned at each scale. The partial structures are estimated independently, and connected to rebuild the whole fibre. Our score function consists in a data-fit part and a penalty part, balanced automatically at each scale and each subchain. The penalty part enforces "soft" geometric constraints. As many different structures can fit the data, our sampling strategy produces a set of solutions with similar scores. The procedure contains a few parameters, independent of both the scale and the genomic segment treated. The partition of the fibre, along with intrinsically parallel parts, make this method computationally efficient. Results from human genome data support the biological plausibility of our solutions.Source: IEEE/ACM transactions on computational biology and bioinformatics (Print) 16 (2019): 550–559. doi:10.1109/TCBB.2018.2791439
DOI: 10.1109/tcbb.2018.2791439
Metrics:
Caudai C., Salerno E., Zoppe M., Tonazzini A.
We present a method to infer 3D chromatin configurations from Chromosome Conformation Capture data. Quite a few methods have been proposed to estimate the structure of the nuclear DNA in homogeneous populations of cells from this kind of data. Many of them transform contact frequencies into Euclidean distances between pairs of chromatin fragments, and then reconstruct the structure by solving a distance-to-geometry problem. To avoid inconsistencies, our method is based on a score function that does not require any frequency-to-distance translation. We propose a multiscale chromatin model where the chromatin fibre is suitably partitioned at each scale. The partial structures are estimated independently, and connected to rebuild the whole fibre. Our score function consists in a data-fit part and a penalty part, balanced automatically at each scale and each subchain. The penalty part enforces "soft" geometric constraints. As many different structures can fit the data, our sampling strategy produces a set of solutions with similar scores. The procedure contains a few parameters, independent of both the scale and the genomic segment treated. The partition of the fibre, along with intrinsically parallel parts, make this method computationally efficient. Results from human genome data support the biological plausibility of our solutions.Source: IEEE/ACM transactions on computational biology and bioinformatics (Print) 16 (2019): 550–559. doi:10.1109/TCBB.2018.2791439
DOI: 10.1109/tcbb.2018.2791439
Metrics:
See at:
ISTI Repository | IEEE/ACM Transactions on Computational Biology and Bioinformatics | ieeexplore.ieee.org | CNR ExploRA
2019
Journal article
Open Access
ChromStruct 4: a Python code to estimate the chromatin structure from Hi-C data
Caudai C., Salerno E., Zoppè M., Merelli I., Tonazzini A.
A method and a stand-alone Python(TM) code to estimate the 3D chromatin structure from chromosome conformation capture data are presented. The method is based on a multiresolution, modified-bead-chain chromatin model, evolved through quaternion operators in a Monte Carlo sampling. The solution space to be sampled is generated by a score function with a data-fit part and a constraint part where the available prior knowledge is implicitly coded. The final solution is a set of 3D configurations that are compatible with both the data and the prior knowledge. The iterative code, provided here as additional material, is equipped with a graphical user interface and stores its results in standard-format files for 3D visualization. We describe the mathematical-computational aspects of the method and explain the details of the code. Some experimental results are reported, with a demonstration of their fit to the data.Source: IEEE/ACM transactions on computational biology and bioinformatics (Online) 16 (2019): 1867–1878. doi:10.1109/TCBB.2018.2838669
DOI: 10.1109/tcbb.2018.2838669
Metrics:
Caudai C., Salerno E., Zoppè M., Merelli I., Tonazzini A.
A method and a stand-alone Python(TM) code to estimate the 3D chromatin structure from chromosome conformation capture data are presented. The method is based on a multiresolution, modified-bead-chain chromatin model, evolved through quaternion operators in a Monte Carlo sampling. The solution space to be sampled is generated by a score function with a data-fit part and a constraint part where the available prior knowledge is implicitly coded. The final solution is a set of 3D configurations that are compatible with both the data and the prior knowledge. The iterative code, provided here as additional material, is equipped with a graphical user interface and stores its results in standard-format files for 3D visualization. We describe the mathematical-computational aspects of the method and explain the details of the code. Some experimental results are reported, with a demonstration of their fit to the data.Source: IEEE/ACM transactions on computational biology and bioinformatics (Online) 16 (2019): 1867–1878. doi:10.1109/TCBB.2018.2838669
DOI: 10.1109/tcbb.2018.2838669
Metrics:
See at:
ISTI Repository | IEEE/ACM Transactions on Computational Biology and Bioinformatics | ieeexplore.ieee.org | CNR ExploRA
2018
Conference article
Open Access
Parallelizable strategy for the estimation of the 3D structure of biological macromolecules
Caudai C., Zoppè M., Salerno E., Merelli I., Tonazzini A.
We present a parallelizzable, multilevel algorithm for the study of three-dimensional structure of biological macromolecules, applied to two fundamental topics: the 3D reconstruction of Chromatin and the elaboration of motion of proteins. For Chromatin, starting from contact data obtained through Chromosome Conformation Capture techniques, our method first subdivides the data matrix in biologically relevant blocks, and then treats them separately, at several levels, depending on the initial data resolution. The result is a family of configurations for the entire fiber, each one compatible with both experimental data and prior knowledge about specific genomes. For Proteins, the method is conceived as a solution for the problem of identifying motion and alternative conformations to the deposited structures. The algorithm, using quaternions, processes the main chain and the aminoacid side chains independently; it then exploits a Monte Carlo method for selection of biologically acceptable conformations, based on energy evaluation, and finally returns a family of conformations and of trajectories at single atom resolution.Source: PDP 2018 - 26th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP), pp. 134–137, Cambridge, UK, 21-23 March 2018
DOI: 10.1109/pdp2018.2018.00026
Metrics:
Caudai C., Zoppè M., Salerno E., Merelli I., Tonazzini A.
We present a parallelizzable, multilevel algorithm for the study of three-dimensional structure of biological macromolecules, applied to two fundamental topics: the 3D reconstruction of Chromatin and the elaboration of motion of proteins. For Chromatin, starting from contact data obtained through Chromosome Conformation Capture techniques, our method first subdivides the data matrix in biologically relevant blocks, and then treats them separately, at several levels, depending on the initial data resolution. The result is a family of configurations for the entire fiber, each one compatible with both experimental data and prior knowledge about specific genomes. For Proteins, the method is conceived as a solution for the problem of identifying motion and alternative conformations to the deposited structures. The algorithm, using quaternions, processes the main chain and the aminoacid side chains independently; it then exploits a Monte Carlo method for selection of biologically acceptable conformations, based on energy evaluation, and finally returns a family of conformations and of trajectories at single atom resolution.Source: PDP 2018 - 26th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP), pp. 134–137, Cambridge, UK, 21-23 March 2018
DOI: 10.1109/pdp2018.2018.00026
Metrics:
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ISTI Repository | doi.org | ieeexplore.ieee.org | CNR ExploRA
2019
Conference article
Open Access
La radiomica come elemento fondante della medicina di precisione in ambito oncologico
Colantonio S., Carlini E., Caudai C., Germanese D., Manghi P., Pascali M. A., Barucci A., Farnesi D., Zoppetti N., Colcelli V., Pini R., Carpi R., Esposito M., Neri E., Romei C., Occhipinti M.
Questo documento introduce e inquadra le attività che un gruppo interdisciplinare di ricercatori e clinici sta portando avanti grazie a tecniche di analisi di immagini, machine learning e intelligenza artificiale, a supporto della medicina di precisione in ambito oncologico. Partendo dalla comprensione del fenomeno fisico e dalla caratterizzazione dei processi biologici che sottendono alla formazione delle immagini biomedicali, attraverso tecniche di analisi radiomica dei dati radiologici e di mining di dati complessi, terogenei e multisorgente, le soluzioni studiate mirano a supportare i clinici nel continuum dei processi diagnostici, prognostici e terapeutici in ambito oncologico.Source: Ital-IA: primo Convegno Nazionale CINI sull'Intelligenza Artificiale, Roma, Italy, 18-19 marzo 2019
Colantonio S., Carlini E., Caudai C., Germanese D., Manghi P., Pascali M. A., Barucci A., Farnesi D., Zoppetti N., Colcelli V., Pini R., Carpi R., Esposito M., Neri E., Romei C., Occhipinti M.
Questo documento introduce e inquadra le attività che un gruppo interdisciplinare di ricercatori e clinici sta portando avanti grazie a tecniche di analisi di immagini, machine learning e intelligenza artificiale, a supporto della medicina di precisione in ambito oncologico. Partendo dalla comprensione del fenomeno fisico e dalla caratterizzazione dei processi biologici che sottendono alla formazione delle immagini biomedicali, attraverso tecniche di analisi radiomica dei dati radiologici e di mining di dati complessi, terogenei e multisorgente, le soluzioni studiate mirano a supportare i clinici nel continuum dei processi diagnostici, prognostici e terapeutici in ambito oncologico.Source: Ital-IA: primo Convegno Nazionale CINI sull'Intelligenza Artificiale, Roma, Italy, 18-19 marzo 2019
See at:
ISTI Repository | www.ital-ia.it | CNR ExploRA