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

Franzin, Alberto; Sambo, Francesco; Camillo, Barbara Di

doi:10.1093/bioinformatics/btw807

Cited by 81 publications

(59 citation statements)

References 6 publications

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“…One interesting model for T2DM detection, which is not based on the aforementioned regressions, is the MOSAIC model [ 40 ], which is open source and available for research ( (last accessed 15 December 2017)). This model is based on a Bayesian network to impute unknown parameters.…”

Section: Methodsmentioning

confidence: 99%

Integration of Distributed Services and Hybrid Models Based on Process Choreography to Predict and Detect Type 2 Diabetes

Martínez-Millana

Bayo-Monton

Argente

et al. 2017

Sensors

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Life expectancy is increasing and, so, the years that patients have to live with chronic diseases and co-morbidities. Type 2 diabetes is one of the most prevalent chronic diseases, specifically linked to being overweight and ages over sixty. Recent studies have demonstrated the effectiveness of new strategies to delay and even prevent the onset of type 2 diabetes by a combination of active and healthy lifestyle on cohorts of mid to high risk subjects. Prospective research has been driven on large groups of the population to build risk scores that aim to obtain a rule for the classification of patients according to the odds for developing the disease. Currently, there are more than two hundred models and risk scores for doing this, but a few have been properly evaluated in external groups and integrated into a clinical application for decision support. In this paper, we present a novel system architecture based on service choreography and hybrid modeling, which enables a distributed integration of clinical databases, statistical and mathematical engines and web interfaces to be deployed in a clinical setting. The system was assessed during an eight-week continuous period with eight endocrinologists of a hospital who evaluated up to 8080 patients with seven different type 2 diabetes risk models implemented in two mathematical engines. Throughput was assessed as a matter of technical key performance indicators, confirming the reliability and efficiency of the proposed architecture to integrate hybrid artificial intelligence tools into daily clinical routine to identify high risk subjects.

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Section: Methodsmentioning

confidence: 99%

Integration of Distributed Services and Hybrid Models Based on Process Choreography to Predict and Detect Type 2 Diabetes

Martínez-Millana

Bayo-Monton

Argente

et al. 2017

Sensors

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“…We used IM-PET 23,106 EP interaction pairs between 5311 CpG probes and 344 genes as positive control and tested the precision of EP prediction from patient data by four methods: MICMIC; ELMER; BNstruct (Bayesian Network Structure Learning) [ 40 ]; and NEO2 (Network Edge Orienting (NEO) Software) [ 41 ]. All the methods were applied on the expression and methylation data from the same patient cohort of TCGA liver cancer.…”

Section: Methodsmentioning

confidence: 99%

MICMIC: identification of DNA methylation of distal regulatory regions with causal effects on tumorigenesis

et al. 2018

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Aberrant promoter methylation is a common mechanism for tumor suppressor inactivation in cancer. We develop a set of tools to identify genome-wide DNA methylation in distal regions with causal effect on tumorigenesis called MICMIC. Many predictions are directly validated by dCas9-based epigenetic editing to support the accuracy and efficiency of our tool. Oncogenic and lineage-specific transcription factors are shown to aberrantly shape the methylation landscape by modifying tumor-subtype core regulatory circuitry. Notably, the gene regulatory networks orchestrated by enhancer methylation across different cancer types are seen to converge on a common architecture. MICMIC is available on https://github.com/ZhangJlab/MICMIC.Electronic supplementary materialThe online version of this article (10.1186/s13059-018-1442-0) contains supplementary material, which is available to authorized users.

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“…All analysis were done in R 2.14.0. The bnlearn package [58] was used for Bayesian Network estimation, the bestNormalize package [52] for variables' normalization and the bnstruct package [19] for handling missing values by means of the KNN algorithm. For the purpose of satisfactorily answering the above inquiries, we deal with static, hybrid BNs that also incorporate geographic information and suggest two different strategies to encode prior information in the learning algorithm.…”

Section: Reliability Of the Estimated Networkmentioning

confidence: 99%

Modeling “Equitable and Sustainable Well-being” (BES) Using Bayesian Networks: A Case Study of the Italian Regions

Onori

Lasinio

2020

Soc Indic Res

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Measurement of well-being has been a highly debated topic since the end of the last century. While some specific aspects are still open issues, a multidimensional approach as well as the construction of shared and well-rooted systems of indicators are now accepted as the main route to measure this complex phenomenon. A meaningful effort, in this direction, is that of the Italian "Equitable and Sustainable Well-being" (BES) system of indicators, developed by the Italian National Institute of Statistics (ISTAT) and the National Council for Economics and Labour (CNEL). The BES framework comprises a number of atomic indicators measured yearly at regional level and reflecting the different domains of well-being (e.g. Health, Education, Work & Life Balance, Environment,...). In this work we aim at dealing with the multidimensionality of the BES system of indicators and try to answer three main research questions: I) What is the structure of the relationships among the BES atomic indicators; II) What is the structure of the relationships among the BES domains; III) To what extent the structure of the relationships reflects the current BES theoretical framework. We address these questions by implementing Bayesian Networks (BNs), a widely accepted class of multivariate statistical models, particularly suitable for handling reasoning with uncertainty. Implementation of a BN results in a set of nodes and a set of conditional independence statements that provide an effective tool to explore associations in a system of variables. In this work, we also suggest two strategies for encoding prior knowledge in the BN estimating algorithm so that the BES theoretical framework can be represented into the network.

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

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 81 publications

References 6 publications

Integration of Distributed Services and Hybrid Models Based on Process Choreography to Predict and Detect Type 2 Diabetes

Integration of Distributed Services and Hybrid Models Based on Process Choreography to Predict and Detect Type 2 Diabetes

MICMIC: identification of DNA methylation of distal regulatory regions with causal effects on tumorigenesis

Modeling “Equitable and Sustainable Well-being” (BES) Using Bayesian Networks: A Case Study of the Italian Regions

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