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  1. Book ; Online: The YODO algorithm

    Ballester-Ripoll, Rafael / Leonelli, Manuele

    An efficient computational framework for sensitivity analysis in Bayesian networks

    2023  

    Abstract: Sensitivity analysis measures the influence of a Bayesian network's parameters on a quantity of interest defined by the network, such as the probability of a variable taking a specific value. Various sensitivity measures have been defined to quantify ... ...

    Abstract Sensitivity analysis measures the influence of a Bayesian network's parameters on a quantity of interest defined by the network, such as the probability of a variable taking a specific value. Various sensitivity measures have been defined to quantify such influence, most commonly some function of the quantity of interest's partial derivative with respect to the network's conditional probabilities. However, computing these measures in large networks with thousands of parameters can become computationally very expensive. We propose an algorithm combining automatic differentiation and exact inference to efficiently calculate the sensitivity measures in a single pass. It first marginalizes the whole network once, using e.g. variable elimination, and then backpropagates this operation to obtain the gradient with respect to all input parameters. Our method can be used for one-way and multi-way sensitivity analysis and the derivation of admissible regions. Simulation studies highlight the efficiency of our algorithm by scaling it to massive networks with up to 100'000 parameters and investigate the feasibility of generic multi-way analyses. Our routines are also showcased over two medium-sized Bayesian networks: the first modeling the country-risks of a humanitarian crisis, the second studying the relationship between the use of technology and the psychological effects of forced social isolation during the COVID-19 pandemic. An implementation of the methods using the popular machine learning library PyTorch is freely available.

    Comment: arXiv admin note: substantial text overlap with arXiv:2206.08687
    Keywords Statistics - Methodology ; Computer Science - Artificial Intelligence
    Subject code 006
    Publishing date 2023-02-01
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Book ; Online: Learning and interpreting asymmetry-labeled DAGs

    Leonelli, Manuele / Varando, Gherardo

    a case study on COVID-19 fear

    2023  

    Abstract: Bayesian networks are widely used to learn and reason about the dependence structure of discrete variables. However, they are only capable of formally encoding symmetric conditional independence, which in practice is often too strict to hold. Asymmetry- ... ...

    Abstract Bayesian networks are widely used to learn and reason about the dependence structure of discrete variables. However, they are only capable of formally encoding symmetric conditional independence, which in practice is often too strict to hold. Asymmetry-labeled DAGs have been recently proposed to both extend the class of Bayesian networks by relaxing the symmetric assumption of independence and denote the type of dependence existing between the variables of interest. Here, we introduce novel structural learning algorithms for this class of models which, whilst being efficient, allow for a straightforward interpretation of the underlying dependence structure. A comprehensive computational study highlights the efficiency of the algorithms. A real-world data application using data from the Fear of COVID-19 Scale collected in Italy showcases their use in practice.
    Keywords Computer Science - Artificial Intelligence ; Statistics - Machine Learning
    Subject code 006
    Publishing date 2023-01-02
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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    Inter-library loan at ZB MED

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  3. Book ; Online: AI and the creative realm

    Crimaldi, Fabio / Leonelli, Manuele

    A short review of current and future applications

    2023  

    Abstract: This study explores the concept of creativity and artificial intelligence (AI) and their recent integration. While AI has traditionally been perceived as incapable of generating new ideas or creating art, the development of more sophisticated AI models ... ...

    Abstract This study explores the concept of creativity and artificial intelligence (AI) and their recent integration. While AI has traditionally been perceived as incapable of generating new ideas or creating art, the development of more sophisticated AI models and the proliferation of human-computer interaction tools have opened up new possibilities for AI in artistic creation. This study investigates the various applications of AI in a creative context, differentiating between the type of art, language, and algorithms used. It also considers the philosophical implications of AI and creativity, questioning whether consciousness can be researched in machines and AI's potential interests and decision-making capabilities. Overall, we aim to stimulate a reflection on AI's use and ethical implications in creative contexts.
    Keywords Computer Science - Artificial Intelligence ; Computer Science - Human-Computer Interaction
    Publishing date 2023-06-01
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Article ; Online: Wide complex tachycardia with atrioventricular dissociation? Not so fast buddy.

    Leonelli, Fabio M / Bagliani, Giuseppe / Williams, Jeffrey / Scheinman, Melvin M

    Heart rhythm

    2023  Volume 20, Issue 11, Page(s) 1591–1592

    MeSH term(s) Humans ; Heart Block ; Tachycardia ; Electrocardiography
    Language English
    Publishing date 2023-11-01
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2229357-7
    ISSN 1556-3871 ; 1547-5271
    ISSN (online) 1556-3871
    ISSN 1547-5271
    DOI 10.1016/j.hrthm.2023.05.014
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 6278: Show issues Location:
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    bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular
    ab Jg. 2022: Lesesaal (EG)

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  5. Article ; Online: Atrial Flutter and Precision Electrocardiology: An Indissoluble Symbiosis.

    Bagliani, Giuseppe / Leonelli, Fabio M / De Ponti, Roberto

    Cardiac electrophysiology clinics

    2022  Volume 14, Issue 3, Page(s) xiii–xiv

    MeSH term(s) Atrial Fibrillation ; Atrial Flutter ; Catheter Ablation ; Electrocardiography ; Humans ; Symbiosis
    Language English
    Publishing date 2022-08-25
    Publishing country United States
    Document type Editorial
    ISSN 1877-9190
    ISSN (online) 1877-9190
    DOI 10.1016/j.ccep.2022.07.005
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article ; Online: Interpretation of Typical and Atypical Atrial Flutters by Precision Electrocardiology Based on Intracardiac Recording.

    Leonelli, Fabio M / Ponti, Roberto De / Bagliani, Giuseppe

    Cardiac electrophysiology clinics

    2022  Volume 14, Issue 3, Page(s) 435–458

    Abstract: Atrial flutter is a term encompassing multiple clinical entities. Clinical manifestations of these arrhythmias range from typical isthmus-dependent flutter to post-ablation microreentries. Twelve-lead electrocardiogram (ECG) is a diagnostic tool in ... ...

    Abstract Atrial flutter is a term encompassing multiple clinical entities. Clinical manifestations of these arrhythmias range from typical isthmus-dependent flutter to post-ablation microreentries. Twelve-lead electrocardiogram (ECG) is a diagnostic tool in typical flutter, but it is often unable to clearly localize atrial flutters maintained by more complex reentrant circuits. Electrophysiology study and mapping are able to characterize in fine details all the components of the circuit and determine their electrophysiological properties. Combining these 2 techniques can greatly help in understanding the vectors determining the ECG morphology of the flutter waveforms, increasing the diagnostic usefulness of this tool.
    MeSH term(s) Atrial Flutter ; Catheter Ablation/methods ; Electrocardiography ; Heart ; Humans
    Language English
    Publishing date 2022-08-25
    Publishing country United States
    Document type Journal Article ; Review
    ISSN 1877-9190
    ISSN (online) 1877-9190
    DOI 10.1016/j.ccep.2022.05.004
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Book ; Online: Structural Learning of Simple Staged Trees

    Leonelli, Manuele / Varando, Gherardo

    2022  

    Abstract: Bayesian networks faithfully represent the symmetric conditional independences existing between the components of a random vector. Staged trees are an extension of Bayesian networks for categorical random vectors whose graph represents non-symmetric ... ...

    Abstract Bayesian networks faithfully represent the symmetric conditional independences existing between the components of a random vector. Staged trees are an extension of Bayesian networks for categorical random vectors whose graph represents non-symmetric conditional independences via vertex coloring. However, since they are based on a tree representation of the sample space, the underlying graph becomes cluttered and difficult to visualize as the number of variables increases. Here we introduce the first structural learning algorithms for the class of simple staged trees, entertaining a compact coalescence of the underlying tree from which non-symmetric independences can be easily read. We show that data-learned simple staged trees often outperform Bayesian networks in model fit and illustrate how the coalesced graph is used to identify non-symmetric conditional independences.
    Keywords Statistics - Machine Learning ; Computer Science - Machine Learning
    Publishing date 2022-03-08
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Book ; Online: Highly Efficient Structural Learning of Sparse Staged Trees

    Leonelli, Manuele / Varando, Gherardo

    2022  

    Abstract: Several structural learning algorithms for staged tree models, an asymmetric extension of Bayesian networks, have been defined. However, they do not scale efficiently as the number of variables considered increases. Here we introduce the first scalable ... ...

    Abstract Several structural learning algorithms for staged tree models, an asymmetric extension of Bayesian networks, have been defined. However, they do not scale efficiently as the number of variables considered increases. Here we introduce the first scalable structural learning algorithm for staged trees, which searches over a space of models where only a small number of dependencies can be imposed. A simulation study as well as a real-world application illustrate our routines and the practical use of such data-learned staged trees.

    Comment: arXiv admin note: text overlap with arXiv:2203.04390
    Keywords Statistics - Machine Learning ; Computer Science - Machine Learning
    Publishing date 2022-06-14
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  9. Book ; Online: You Only Derive Once (YODO)

    Ballester-Ripoll, Rafael / Leonelli, Manuele

    Automatic Differentiation for Efficient Sensitivity Analysis in Bayesian Networks

    2022  

    Abstract: Sensitivity analysis measures the influence of a Bayesian network's parameters on a quantity of interest defined by the network, such as the probability of a variable taking a specific value. In particular, the so-called sensitivity value measures the ... ...

    Abstract Sensitivity analysis measures the influence of a Bayesian network's parameters on a quantity of interest defined by the network, such as the probability of a variable taking a specific value. In particular, the so-called sensitivity value measures the quantity of interest's partial derivative with respect to the network's conditional probabilities. However, finding such values in large networks with thousands of parameters can become computationally very expensive. We propose to use automatic differentiation combined with exact inference to obtain all sensitivity values in a single pass. Our method first marginalizes the whole network once using e.g. variable elimination and then backpropagates this operation to obtain the gradient with respect to all input parameters. We demonstrate our routines by ranking all parameters by importance on a Bayesian network modeling humanitarian crises and disasters, and then show the method's efficiency by scaling it to huge networks with up to 100'000 parameters. An implementation of the methods using the popular machine learning library PyTorch is freely available.
    Keywords Computer Science - Artificial Intelligence
    Subject code 006
    Publishing date 2022-06-17
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  10. Article ; Online: DamID-seq: A Genome-Wide DNA Methylation Method that Captures Both Transient and Stable TF-DNA Interactions in Plant Cells.

    Alvarez, José M / Hinckley, Will E / Leonelli, Lauriebeth / Brooks, Matthew D / Coruzzi, Gloria M

    Methods in molecular biology (Clifton, N.J.)

    2023  Volume 2698, Page(s) 87–107

    Abstract: Capturing the dynamic and transient interactions of a transcription factor (TF) with its genome-wide targets whose regulation leads to plants' adaptation to their changing environment is a major technical challenge. This is a widespread problem with ... ...

    Abstract Capturing the dynamic and transient interactions of a transcription factor (TF) with its genome-wide targets whose regulation leads to plants' adaptation to their changing environment is a major technical challenge. This is a widespread problem with biochemical methods such as chromatin immunoprecipitation-sequencing (ChIP-seq) which are biased towards capturing stable TF-target gene interactions. Herein, we describe how DNA adenine methyltransferase identification and sequencing (DamID-seq) can be used to capture both transient and stable TF-target interactions by DNA methylation. The DamID technique uses a TF protein fused to a DNA adenine methyltransferase (Dam) from E. coli. When expressed in a plant cell, the Dam-TF fusion protein will methylate adenine (A) bases near the sites of TF-DNA interactions. In this way, DamID results in a permanent, stable DNA methylation mark on TF-target gene promoters, even if the target gene is only transiently "touched" by the Dam-TF fusion protein. Here we provide a step-by-step protocol to perform DamID-seq experiments in isolated plant cells for any Dam-TF fusion protein of interest. We also provide information that will enable researchers to analyze DamID-seq data to identify TF-binding sites in the genome. Our protocol includes instructions for vector cloning of the Dam-TF fusion proteins, plant cell protoplast transfections, DamID preps, library preparation, and sequencing data analysis. The protocol outlined in this chapter is performed in Arabidopsis thaliana, however, the DamID-seq workflow developed in this guide is broadly applicable to other plants and organisms.
    MeSH term(s) DNA Methylation ; Plant Cells ; Escherichia coli ; DNA ; Transcription Factors ; Adenine ; Arabidopsis/genetics ; Factor VII ; Methyltransferases
    Chemical Substances DNA (9007-49-2) ; Transcription Factors ; Adenine (JAC85A2161) ; Factor VII (9001-25-6) ; Methyltransferases (EC 2.1.1.-)
    Language English
    Publishing date 2023-08-22
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Extramural ; Research Support, U.S. Gov't, Non-P.H.S.
    ISSN 1940-6029
    ISSN (online) 1940-6029
    DOI 10.1007/978-1-0716-3354-0_7
    Database MEDical Literature Analysis and Retrieval System OnLINE

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