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Subjective Networks: Perspectives and Challenges [chapter]

Magdalena Ivanovska, Audun Jøsang, Lance Kaplan, Francesco Sambo
2015 Lecture Notes in Computer Science  
possible uncertainty value compatible with Eq.(29) and adjusts it using the average uncertainty of the input conditionals ω Y |X and the irrelevance of X to the value y, for details see [Jøsang and Sambo  ...  Inference in a Two-node Network In this section we briefly summarize the operations of deduction and abduction defined in [Jøsang, 2008; Jøsang and Sambo, 2014] for conditional reasoning with two variables  ... 
doi:10.1007/978-3-319-28702-7_7 fatcat:eojneac6vrg2nlix6tuq6ezfzq

Qualitative Reasoning on Systematic Gene Perturbation Experiments [chapter]

Francesco Sambo, Barbara Di Camillo
2011 Lecture Notes in Computer Science  
Observations of systematic gene perturbation experiments have been proven the most informative for the identification of regulatory relations between genes. For this purpose, we present a novel Qualitative Reasoning approach, based on a qualitative abstraction of DNA-microarray data and on a set of IF-THEN inference rules. Our algorithm exhibits an extremely low rate of false positives, competitive with the state-of-the-art, on both noise-free and noisy simulated data. This, together with the
more » ... lynomial running time, makes our algorithm an useful tool for systematic gene perturbation experiments, able to identify a subset of the oriented regulatory relations with high reliability and to provide valuable insights on the amount of information conveyed by a set of experiments.
doi:10.1007/978-3-642-21946-7_11 fatcat:najkctvwyveknj7gjouekbl4ky

bnstruct: an R package for Bayesian Network structure learning in the presence of missing data

Alberto Franzin, Francesco Sambo, Barbara Di Camillo
2016 Bioinformatics  
Motivation: A Bayesian Network is a probabilistic graphical model that encodes probabilistic dependencies between a set of random variables. We introduce bnstruct, an open source R package to (i) learn the structure and the parameters of a Bayesian Network from data in the presence of missing values and (ii) perform reasoning and inference on the learned Bayesian Networks. To the best of our knowledge, there is no other open source software that provides methods for all of these tasks,
more » ... rly the manipulation of missing data, which is a common situation in practice.
doi:10.1093/bioinformatics/btw807 pmid:28003263 fatcat:vkycag4nx5df7mxhvuf7s3bbfq

A Survey on the Path Computation Element (PCE) Architecture

Francesco Paolucci, Filippo Cugini, Alessio Giorgetti, Nicola Sambo, Piero Castoldi
2013 IEEE Communications Surveys and Tutorials  
Sambo and P. Castoldi are with TeCIP Institute, Scuola Superiore Sant'Anna, Via Moruzzi 1, 56124 Pisa, Italy (email: F.  ...  Nicola Sambo received his Laurea degree cum laude in telecommunication engineering from the University of Pisa, Italy, in 2004 and his Ph.D. degree from Scuola Superiore Sant'Anna, Pisa, Italy, in 2009  ... 
doi:10.1109/surv.2013.011413.00087 fatcat:ncgex62e2vhyvcnbxpjjr3bynq

Experimental Demonstration of Impairment-Aware PCE for Multi-Bit-Rate WSONs

Francesco Paolucci, Nicola Sambo, Filippo Cugini, Alessio Giorgetti, Piero Castoldi
2011 Journal of Optical Communications and Networking  
In emerging multi-bit-rate wavelength switched optical networks (WSONs), the coexistence of lightpaths operating at different bit-rates and modulation formats (e.g., based on amplitude and phase modulation) induces relevant traffic dependent detrimental effects that need to be considered during impairment-aware routing and wavelength assignment (IA-RWA). The considerable complexity of IA-RWA computation has driven the Internet Engineering Task Force (IETF) to propose specific path computation
more » ... ement (PCE) architectures in support of IA-RWA for WSONs. In this paper, following the IETF indications, we expand two PCE architectures and experimentally evaluate five different PCE architectural solutions, performing either combined or separated impairment estimation and RWA, with on-line and off-line computation of impairment validated paths, and with the possible utilization of a novel PCE Protocol (PCEP) extension. Results in terms of traffic engineering performance, path computation delivery time and amount of exchanged PCEP messages are reported and discussed to highlight the benefits and application scenarios of the considered PCE architectures.
doi:10.1364/jocn.3.000610 fatcat:in6ryk344vgs7kkk2zow53mjxy

A multi-objective coordinate-exchange two-phase local search algorithm for multi-stratum experiments

Matteo Borrotti, Francesco Sambo, Kalliopi Mylona, Steven Gilmour
2016 Statistics and computing  
In Sambo et al. (2014) , the authors have empirically demonstrated that 1000 scalarizations are sufficient to achieve a good approximation of the Pareto front.  ...  In Sambo et al. (2014) , the Pareto approach is applied to the construction of split-plot designs: the newly introduced Coordinate Exchange -Two Phase Local Search (CE-TPLS) algorithm extends the Goos  ... 
doi:10.1007/s11222-016-9633-6 fatcat:pn4n6hsnsfaunglyq2mqilov3u

Optimizing PCR primers targeting the bacterial 16S ribosomal RNA gene

Francesco Sambo, Francesca Finotello, Enrico Lavezzo, Giacomo Baruzzo, Giulia Masi, Elektra Peta, Marco Falda, Stefano Toppo, Luisa Barzon, Barbara Di Camillo
2018 BMC Bioinformatics  
[23] and effectively exploited in Sambo et al. [24] and Borrotti et al. [25] for the optimal multi-objective design of experiments.  ... 
doi:10.1186/s12859-018-2360-6 fatcat:6wmelkzp7vfdvfa2wfvq4wjyjy

Bag of Na�ve Bayes: biomarker selection and classification from genome-wide SNP data

Francesco Sambo, Emanuele Trifoglio, Barbara Di Camillo, Gianna M Toffolo, Claudio Cobelli
2012 BMC Bioinformatics  
Multifactorial diseases arise from complex patterns of interaction between a set of genetic traits and the environment. To fully capture the genetic biomarkers that jointly explain the heritability component of a disease, thus, all SNPs from a genome-wide association study should be analyzed simultaneously. Results: In this paper, we present Bag of Naïve Bayes (BoNB), an algorithm for genetic biomarker selection and subjects classification from the simultaneous analysis of genome-wide SNP data.
more » ... BoNB is based on the Naïve Bayes classification framework, enriched by three main features: bootstrap aggregating of an ensemble of Naïve Bayes classifiers, a novel strategy for ranking and selecting the attributes used by each classifier in the ensemble and a permutation-based procedure for selecting significant biomarkers, based on their marginal utility in the classification process. BoNB is tested on the Wellcome Trust Case-Control study on Type 1 Diabetes and its performance is compared with the ones of both a standard Naïve Bayes algorithm and HyperLASSO, a penalized logistic regression algorithm from the state-of-the-art in simultaneous genome-wide data analysis. Conclusions: The significantly higher classification accuracy obtained by BoNB, together with the significance of the biomarkers identified from the Type 1 Diabetes dataset, prove the effectiveness of BoNB as an algorithm for both classification and biomarker selection from genome-wide SNP data. Availability: Source code of the BoNB algorithm is released under the GNU General Public Licence and is available at
doi:10.1186/1471-2105-13-s14-s2 pmid:23095127 pmcid:PMC3439675 fatcat:xwotnqhjzfhgzl3tnuf3azole4

Code-adaptive transmission accounting for filtering effects in EON

Gianluca Meloni, Luca Potı, Nicola Sambo, Francesco Fresi, Fabio Cavaliere
2015 2015 International Conference on Optical Network Design and Modeling (ONDM)  
• TFP [a] is faster than Nyquist • Sub-carriers are filtered beyond the Nyquist limit, thus introducing inter-symbol interference (ISI) [a] Sambo, N.; Meloni, G.; Paolucci, F.; Cugini, F.; Secondini  ...  more: → more code → higher N → higher B One hop more: → same code is fine + enlarge filters to avoid filtering code ➞ number of carriers ➞ super-channel bandwidth ➞ ITU-T m [a] is faster than Nyquist[a] Sambo  ... 
doi:10.1109/ondm.2015.7127302 dblp:conf/ondm/MeloniPSFC15 fatcat:7my7nmu255fmrdodvwfumlnnwe

BER Degradation Detection and Failure Identification in Elastic Optical Networks

Alba P. Vela, Marc Ruiz, Francesco Fresi, Nicola Sambo, Filippo Cugini, Gianluca Meloni, Luca Poti, Luis Velasco, Piero Castoldi
2017 Journal of Lightwave Technology  
Vela, Marc Ruiz, Francesco Fresi, Nicola Sambo, Filippo Cugini, Gianluca Meloni, Luca Potì, Luis Velasco and Piero Castoldi S evaluated the related induced penalties.  ... 
doi:10.1109/jlt.2017.2747223 fatcat:ulqslb5hfvg7rhfwgapqt6g3we

Parsimonious Description of Glucose Variability in Type 2 Diabetes by Sparse Principal Component Analysis

Chiara Fabris, Andrea Facchinetti, Giuseppe Fico, Francesco Sambo, Maria Teresa Arredondo, Claudio Cobelli
2015 Journal of Diabetes Science and Technology  
Abnormal glucose variability (GV) is a risk factor for diabetes complications, and tens of indices for its quantification from continuous glucose monitoring (CGM) time series have been proposed. However, the information carried by these indices is redundant, and a parsimonious description of GV can be obtained through sparse principal component analysis (SPCA). We have recently shown that a set of 10 metrics selected by SPCA is able to describe more than 60% of the variance of 25 GV indicators
more » ... n type 1 diabetes (T1D). Here, we want to extend the application of SPCA to type 2 diabetes (T2D). Methods: A data set of CGM time series collected in 13 T2D subjects was considered. The 25 GV indices considered for T1D were evaluated. SPCA was used to select a subset of indices able to describe the majority of the original variance. Results: A subset of 10 indicators was selected and allowed to describe 83% of the variance of the original pool of 25 indices. Four metrics sufficient to describe 67% of the original variance turned out to be shared by the parsimonious sets of indices in T1D and T2D. Conclusions: Starting from a pool of 25 indices assessed from CGM time series in T2D subjects, reduced subsets of metrics virtually providing the same information content can be determined by SPCA. The fact that these indices also appear in the parsimonious description of GV in T1D may indicate that they could be particularly informative of GV in diabetes, regardless of the specific type of disease.
doi:10.1177/1932296815596173 pmid:26232371 pmcid:PMC4738208 fatcat:ww6gucrqavfu3ayyvuny2dquve

ABACUS: an entropy-based cumulative bivariate statistic robust to rare variants and different direction of genotype effect

Barbara Di Camillo, Francesco Sambo, Gianna Toffolo, Claudio Cobelli
2013 Computer applications in the biosciences : CABIOS  
Motivation: In the past years, both sequencing and microarray have been widely used to search for relations between genetic variations and predisposition to complex pathologies such as diabetes or neurological disorders. These studies, however, have been able to explain only a small fraction of disease heritability, possibly because complex pathologies cannot be referred to few dysfunctional genes, but are rather heterogeneous and multicausal, as a result of a combination of rare and common
more » ... ants possibly impairing multiple regulatory pathways. Rare variants, though, are difficult to detect, especially when the effects of causal variants are in different directions, i.e. with protective and detrimental effects. Results: Here, we propose ABACUS, an Algorithm based on a BivAriate CUmulative Statistic to identify single nucleotide polymorphisms (SNPs) significantly associated with a disease within predefined sets of SNPs such as pathways or genomic regions. ABACUS is robust to the concurrent presence of SNPs with protective and detrimental effects and of common and rare variants; moreover, it is powerful even when few SNPs in the SNP-set are associated with the phenotype. We assessed ABACUS performance on simulated and real data and compared it with three state-of-the-art methods. When ABACUS was applied to type 1 and 2 diabetes data, besides observing a wide overlap with already known associations, we found a number of biologically sound pathways, which might shed light on diabetes mechanism and etiology. Availability and implementation: ABACUS is available at
doi:10.1093/bioinformatics/btt697 pmid:24292361 fatcat:b2gfpjsui5auhly2sgpratbbdm

On the Difficulty of Inferring Gene Regulatory Networks: A Study of the Fitness Landscape Generated by Relative Squared Error [chapter]

Francesco Sambo, Marco A. Montes de Oca, Barbara Di Camillo, Thomas Stützle
2010 Lecture Notes in Computer Science  
Inferring gene regulatory networks from expression profiles is a challenging problem that has been tackled using many different approaches. When posed as an optimization problem, the typical goal is to minimize the value of an error measure, such as the relative squared error, between the real profiles and those generated with a model whose parameters are to be optimized. In this paper, we use recurrent neural networks to model regulatory interactions and study systematically the "fitness
more » ... ape" that results from measuring the relative squared error. Although the results of the study indicate that the generated landscapes have a positive fitness-distance correlation, the error values span several orders of magnitude over very short distance variations. This suggests that the fitness landscape has extremely deep valleys, which can make general-purpose state-of-the-art continuous optimization algorithms exhibit a very poor performance. Further results obtained from an analysis based on perturbations of the optimal network topology support approaches in which the spaces of network topologies and of network parameters are decoupled.
doi:10.1007/978-3-642-14156-0_7 fatcat:u4lb64urinao7j6y43v5axjnu4

Effects of Drought on Yield and Nutraceutical Properties of Beans (Phaseolus spp.) Traditionally Cultivated in Veneto, Italy

Pietro Sica, Aline Galvao, Francesco Scariolo, Carmelo Maucieri, Carlo Nicoletto, Cristiane Pilon, Paolo Sambo, Gianni Barcaccia, Maurizio Borin, Miguel Cabrera, Dorcas Franklin
2021 Horticulturae  
Beans are often grown in regions with climates that are susceptible to drought during the cultivation period. Consequently, it is important to identify bean accessions tolerant to drought conditions and assess the effect of drought on seeds' nutraceutical properties. This study evaluated the effect of drought during different development stages (NES = never stressed; ALS = always stressed; SBF = stressed before flowering; SAF = stressed after flowering) on the yield and nutraceutical properties
more » ... of six local bean varieties: Fasolo del Diavolo, Gialet, Posenati, Secle, D'oro, and Maron. Analysis of variance indicated that Gialet was not significantly affected by drought treatments, and Posenati under SBF and NES treatments had greater yields than under ALS and SAF treatments, whereas Secle under SBF produced 80% more seeds than under NES. Total phenols, antioxidant capacity, and calcium content were significantly different among the local varieties. Yield was significantly and positively correlated with seed calcium content and significantly and negatively correlated with protein, total phenols, and antioxidant capacity. The interaction between local varieties and treatment significantly affected seeds' Zn content. Gialet and Maron seeds' Zn contents were about 60 mg kg−1, almost double the average of commercial varieties. In summary, this study paves the way to the identification of potential bean varieties resistant to drought. Further molecular studies will help support these findings.
doi:10.3390/horticulturae7020017 fatcat:nrvwzvrxpbfanphwdfy4cmxhyu

A Dynamic Bayesian Network model for long-term simulation of clinical complications in type 1 diabetes

Simone Marini, Emanuele Trifoglio, Nicola Barbarini, Francesco Sambo, Barbara Di Camillo, Alberto Malovini, Marco Manfrini, Claudio Cobelli, Riccardo Bellazzi
2015 Journal of Biomedical Informatics  
The increasing prevalence of diabetes and its related complications is raising the need for effective methods to predict patient evolution and for stratifying cohorts in terms of risk of developing diabetes-related complications. In this paper, we present a novel approach to the simulation of a type 1 diabetes population, based on Dynamic Bayesian Networks, which combines literature knowledge with data mining of a rich longitudinal cohort of type 1 diabetes patients, the DCCT/EDIC study. In
more » ... icular, in our approach we simulate the patient health state and complications through discretized variables. Two types of models are presented, one entirely learned from the data and the other partially driven by literature derived knowledge. The whole cohort is simulated for fifteen years, and the simulation error (i.e. for each variable, the percentage of patients predicted in the wrong state) is calculated every year on independent test data. For each variable, the population predicted in the wrong state is below 10% on both models over time. Furthermore, the distributions of real vs. simulated patients greatly overlap. Thus, the proposed models are viable tools to support decision making in type 1 diabetes.
doi:10.1016/j.jbi.2015.08.021 pmid:26325295 fatcat:qnbmypt6m5eedevfwpipidd5bm
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