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  1. Buch ; Online: Topological data analysis of human vowels

    Bonafos, Guillem / Freyermuth, Jean-Marc / Pudlo, Pierre / Tronçon, Samuel / Rey, Arnaud

    Persistent homologies across representation spaces

    2023  

    Abstract: Topological Data Analysis (TDA) has been successfully used for various tasks in signal/image processing, from visualization to supervised/unsupervised classification. Often, topological characteristics are obtained from persistent homology theory. The ... ...

    Abstract Topological Data Analysis (TDA) has been successfully used for various tasks in signal/image processing, from visualization to supervised/unsupervised classification. Often, topological characteristics are obtained from persistent homology theory. The standard TDA pipeline starts from the raw signal data or a representation of it. Then, it consists in building a multiscale topological structure on the top of the data using a pre-specified filtration, and finally to compute the topological signature to be further exploited. The commonly used topological signature is a persistent diagram (or transformations of it). Current research discusses the consequences of the many ways to exploit topological signatures, much less often the choice of the filtration, but to the best of our knowledge, the choice of the representation of a signal has not been the subject of any study yet. This paper attempts to provide some answers on the latter problem. To this end, we collected real audio data and built a comparative study to assess the quality of the discriminant information of the topological signatures extracted from three different representation spaces. Each audio signal is represented as i) an embedding of observed data in a higher dimensional space using Taken's representation, ii) a spectrogram viewed as a surface in a 3D ambient space, iii) the set of spectrogram's zeroes. From vowel audio recordings, we use topological signature for three prediction problems: speaker gender, vowel type, and individual. We show that topologically-augmented random forest improves the Out-of-Bag Error (OOB) over solely based Mel-Frequency Cepstral Coefficients (MFCC) for the last two problems. Our results also suggest that the topological information extracted from different signal representations is complementary, and that spectrogram's zeros offers the best improvement for gender prediction.
    Schlagwörter Computer Science - Sound ; Electrical Engineering and Systems Science - Audio and Speech Processing ; Statistics - Applications ; Statistics - Machine Learning
    Thema/Rubrik (Code) 514
    Erscheinungsdatum 2023-10-10
    Erscheinungsland us
    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  2. Artikel ; Online: An overview on Approximate Bayesian computation*

    Baragatti Meïli / Pudlo Pierre

    ESAIM : Proceedings , Vol 44, Pp 291-

    2014  Band 299

    Abstract: Approximate Bayesian computation techniques, also called likelihood-free methods, are one of the most satisfactory approach to intractable likelihood problems. This overview presents recent results since its introduction about ten years ago in population ...

    Abstract Approximate Bayesian computation techniques, also called likelihood-free methods, are one of the most satisfactory approach to intractable likelihood problems. This overview presents recent results since its introduction about ten years ago in population genetics.
    Schlagwörter Mathematics ; QA1-939 ; Science ; Q
    Sprache Englisch
    Erscheinungsdatum 2014-01-01T00:00:00Z
    Verlag EDP Sciences
    Dokumenttyp Artikel ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  3. Buch ; Online: Detecting human and non-human vocal productions in large scale audio recordings

    Bonafos, Guillem / Pudlo, Pierre / Freyermuth, Jean-Marc / Legou, Thierry / Fagot, Joël / Tronçon, Samuel / Rey, Arnaud

    2023  

    Abstract: We propose an automatic data processing pipeline to extract vocal productions from large-scale natural audio recordings. Through a series of computational steps (windowing, creation of a noise class, data augmentation, re-sampling, transfer learning, ... ...

    Abstract We propose an automatic data processing pipeline to extract vocal productions from large-scale natural audio recordings. Through a series of computational steps (windowing, creation of a noise class, data augmentation, re-sampling, transfer learning, Bayesian optimisation), it automatically trains a neural network for detecting various types of natural vocal productions in a noisy data stream without requiring a large sample of labeled data. We test it on two different data sets, one from a group of Guinea baboons recorded from a primate research center and one from human babies recorded at home. The pipeline trains a model on 72 and 77 minutes of labeled audio recordings, with an accuracy of 94.58% and 99.76%. It is then used to process 443 and 174 hours of natural continuous recordings and it creates two new databases of 38.8 and 35.2 hours, respectively. We discuss the strengths and limitations of this approach that can be applied to any massive audio recording.
    Schlagwörter Computer Science - Sound ; Computer Science - Machine Learning ; Electrical Engineering and Systems Science - Audio and Speech Processing ; Statistics - Applications
    Thema/Rubrik (Code) 006
    Erscheinungsdatum 2023-02-14
    Erscheinungsland us
    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  4. Artikel ; Online: ABC random forests for Bayesian parameter inference

    Raynal, Louis / Marin, Jean-Michel / Pudlo, Pierre / Ribatet, Mathieu / Robert, Christian P. / Estoup, Arnaud

    Bioinformatics 2019 May 15, v. 35, no. 10, p. 1720-1728

    2019  , Seite(n) 1720–1728

    Abstract: Motivation Approximate Bayesian computation (ABC) has grown into a standard methodology that manages Bayesian inference for models associated with intractable likelihood functions. Most ABC implementations require the preliminary selection of a vector of ...

    Abstract Motivation Approximate Bayesian computation (ABC) has grown into a standard methodology that manages Bayesian inference for models associated with intractable likelihood functions. Most ABC implementations require the preliminary selection of a vector of informative statistics summarizing raw data. Furthermore, in almost all existing implementations, the tolerance level that separates acceptance from rejection of simulated parameter values needs to be calibrated. Results We propose to conduct likelihood-free Bayesian inferences about parameters with no prior selection of the relevant components of the summary statistics and bypassing the derivation of the associated tolerance level. The approach relies on the random forest (RF) methodology of Breiman (2001) applied in a (non-parametric) regression setting. We advocate the derivation of a new RF for each component of the parameter vector of interest. When compared with earlier ABC solutions, this method offers significant gains in terms of robustness to the choice of the summary statistics, does not depend on any type of tolerance level, and is a good trade-off in term of quality of point estimator precision and credible interval estimations for a given computing time. We illustrate the performance of our methodological proposal and compare it with earlier ABC methods on a Normal toy example and a population genetics example dealing with human population evolution.
    Schlagwörter Bayesian theory ; bioinformatics ; evolution ; human population ; population genetics
    Sprache Englisch
    Erscheinungsverlauf 2019-0515
    Umfang p. 1720-1728
    Erscheinungsort Oxford University Press
    Dokumenttyp Artikel ; Online
    Anmerkung Use and reproduction
    ZDB-ID 1422668-6
    ISSN 1367-4803
    ISSN 1367-4803
    DOI 10.1093/bioinformatics/bty867
    Datenquelle NAL Katalog (AGRICOLA)

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  5. Artikel ; Online: ABC random forests for Bayesian parameter inference.

    Raynal, Louis / Marin, Jean-Michel / Pudlo, Pierre / Ribatet, Mathieu / Robert, Christian P / Estoup, Arnaud

    Bioinformatics (Oxford, England)

    2019  Band 35, Heft 10, Seite(n) 1720–1728

    Abstract: Motivation: Approximate Bayesian computation (ABC) has grown into a standard methodology that manages Bayesian inference for models associated with intractable likelihood functions. Most ABC implementations require the preliminary selection of a vector ... ...

    Abstract Motivation: Approximate Bayesian computation (ABC) has grown into a standard methodology that manages Bayesian inference for models associated with intractable likelihood functions. Most ABC implementations require the preliminary selection of a vector of informative statistics summarizing raw data. Furthermore, in almost all existing implementations, the tolerance level that separates acceptance from rejection of simulated parameter values needs to be calibrated.
    Results: We propose to conduct likelihood-free Bayesian inferences about parameters with no prior selection of the relevant components of the summary statistics and bypassing the derivation of the associated tolerance level. The approach relies on the random forest (RF) methodology of Breiman (2001) applied in a (non-parametric) regression setting. We advocate the derivation of a new RF for each component of the parameter vector of interest. When compared with earlier ABC solutions, this method offers significant gains in terms of robustness to the choice of the summary statistics, does not depend on any type of tolerance level, and is a good trade-off in term of quality of point estimator precision and credible interval estimations for a given computing time. We illustrate the performance of our methodological proposal and compare it with earlier ABC methods on a Normal toy example and a population genetics example dealing with human population evolution.
    Availability and implementation: All methods designed here have been incorporated in the R package abcrf (version 1.7.1) available on CRAN.
    Supplementary information: Supplementary data are available at Bioinformatics online.
    Mesh-Begriff(e) Bayes Theorem ; Biometry ; Computer Simulation ; Genetics, Population ; Humans ; Likelihood Functions
    Sprache Englisch
    Erscheinungsdatum 2019-02-09
    Erscheinungsland England
    Dokumenttyp Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 1422668-6
    ISSN 1367-4811 ; 1367-4803
    ISSN (online) 1367-4811
    ISSN 1367-4803
    DOI 10.1093/bioinformatics/bty867
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  6. Artikel ; Online: Bayesian computation via empirical likelihood.

    Mengersen, Kerrie L / Pudlo, Pierre / Robert, Christian P

    Proceedings of the National Academy of Sciences of the United States of America

    2013  Band 110, Heft 4, Seite(n) 1321–1326

    Abstract: Approximate Bayesian computation has become an essential tool for the analysis of complex stochastic models when the likelihood function is numerically unavailable. However, the well-established statistical method of empirical likelihood provides another ...

    Abstract Approximate Bayesian computation has become an essential tool for the analysis of complex stochastic models when the likelihood function is numerically unavailable. However, the well-established statistical method of empirical likelihood provides another route to such settings that bypasses simulations from the model and the choices of the approximate Bayesian computation parameters (summary statistics, distance, tolerance), while being convergent in the number of observations. Furthermore, bypassing model simulations may lead to significant time savings in complex models, for instance those found in population genetics. The Bayesian computation with empirical likelihood algorithm we develop in this paper also provides an evaluation of its own performance through an associated effective sample size. The method is illustrated using several examples, including estimation of standard distributions, time series, and population genetics models.
    Mesh-Begriff(e) Algorithms ; Bayes Theorem ; Biophysical Phenomena ; Biostatistics ; Genetics, Population/statistics & numerical data ; Likelihood Functions ; Models, Statistical ; Stochastic Processes
    Sprache Englisch
    Erscheinungsdatum 2013-01-07
    Erscheinungsland United States
    Dokumenttyp Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 209104-5
    ISSN 1091-6490 ; 0027-8424
    ISSN (online) 1091-6490
    ISSN 0027-8424
    DOI 10.1073/pnas.1208827110
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  7. Artikel ; Online: Reliable ABC model choice via random forests.

    Pudlo, Pierre / Marin, Jean-Michel / Estoup, Arnaud / Cornuet, Jean-Marie / Gautier, Mathieu / Robert, Christian P

    Bioinformatics (Oxford, England)

    2016  Band 32, Heft 6, Seite(n) 859–866

    Abstract: Motivation: Approximate Bayesian computation (ABC) methods provide an elaborate approach to Bayesian inference on complex models, including model choice. Both theoretical arguments and simulation experiments indicate, however, that model posterior ... ...

    Abstract Motivation: Approximate Bayesian computation (ABC) methods provide an elaborate approach to Bayesian inference on complex models, including model choice. Both theoretical arguments and simulation experiments indicate, however, that model posterior probabilities may be poorly evaluated by standard ABC techniques.
    Results: We propose a novel approach based on a machine learning tool named random forests (RF) to conduct selection among the highly complex models covered by ABC algorithms. We thus modify the way Bayesian model selection is both understood and operated, in that we rephrase the inferential goal as a classification problem, first predicting the model that best fits the data with RF and postponing the approximation of the posterior probability of the selected model for a second stage also relying on RF. Compared with earlier implementations of ABC model choice, the ABC RF approach offers several potential improvements: (i) it often has a larger discriminative power among the competing models, (ii) it is more robust against the number and choice of statistics summarizing the data, (iii) the computing effort is drastically reduced (with a gain in computation efficiency of at least 50) and (iv) it includes an approximation of the posterior probability of the selected model. The call to RF will undoubtedly extend the range of size of datasets and complexity of models that ABC can handle. We illustrate the power of this novel methodology by analyzing controlled experiments as well as genuine population genetics datasets.
    Availability and implementation: The proposed methodology is implemented in the R package abcrf available on the CRAN.
    Contact: jean-michel.marin@umontpellier.fr
    Supplementary information: Supplementary data are available at Bioinformatics online.
    Mesh-Begriff(e) Algorithms ; Bayes Theorem ; Computer Simulation ; Genetics, Population ; Models, Genetic
    Sprache Englisch
    Erscheinungsdatum 2016-03-15
    Erscheinungsland England
    Dokumenttyp Journal Article
    ZDB-ID 1422668-6
    ISSN 1367-4811 ; 1367-4803
    ISSN (online) 1367-4811
    ISSN 1367-4803
    DOI 10.1093/bioinformatics/btv684
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  8. Artikel: Reliable ABC model choice via random forests

    Pudlo, Pierre / Marin, Jean-Michel / Estoup, Arnaud / Cornuet, Jean-Marie / Gautier, Mathieu / Robert, Christian P

    Bioinformatics. 2016 Mar. 15, v. 32, no. 6

    2016  

    Abstract: Motivation: Approximate Bayesian computation (ABC) methods provide an elaborate approach to Bayesian inference on complex models, including model choice. Both theoretical arguments and simulation experiments indicate, however, that model posterior ... ...

    Abstract Motivation: Approximate Bayesian computation (ABC) methods provide an elaborate approach to Bayesian inference on complex models, including model choice. Both theoretical arguments and simulation experiments indicate, however, that model posterior probabilities may be poorly evaluated by standard ABC techniques. Results: We propose a novel approach based on a machine learning tool named random forests (RF) to conduct selection among the highly complex models covered by ABC algorithms. We thus modify the way Bayesian model selection is both understood and operated, in that we rephrase the inferential goal as a classification problem, first predicting the model that best fits the data with RF and postponing the approximation of the posterior probability of the selected model for a second stage also relying on RF. Compared with earlier implementations of ABC model choice, the ABC RF approach offers several potential improvements: (i) it often has a larger discriminative power among the competing models, (ii) it is more robust against the number and choice of statistics summarizing the data, (iii) the computing effort is drastically reduced (with a gain in computation efficiency of at least 50) and (iv) it includes an approximation of the posterior probability of the selected model. The call to RF will undoubtedly extend the range of size of datasets and complexity of models that ABC can handle. We illustrate the power of this novel methodology by analyzing controlled experiments as well as genuine population genetics datasets. Availability and implementation: The proposed methodology is implemented in the R package abcrf available on the CRAN. Contact: jean-michel.marin@umontpellier.fr Supplementary information: Supplementary data are available at Bioinformatics online.
    Schlagwörter Bayesian theory ; algorithms ; artificial intelligence ; bioinformatics ; computer software ; data collection ; models ; population genetics ; prediction ; probability
    Sprache Englisch
    Erscheinungsverlauf 2016-0315
    Umfang p. 859-866.
    Erscheinungsort Oxford University Press
    Dokumenttyp Artikel
    ZDB-ID 1468345-3
    ISSN 1460-2059 ; 1367-4803
    ISSN (online) 1460-2059
    ISSN 1367-4803
    DOI 10.1093/bioinformatics/btv684
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  9. Artikel ; Online: Sympathetic axonal sprouting induces changes in macrophage populations and protects against pancreatic cancer.

    Guillot, Jérémy / Dominici, Chloé / Lucchesi, Adrien / Nguyen, Huyen Thi Trang / Puget, Angélique / Hocine, Mélanie / Rangel-Sosa, Martha M / Simic, Milesa / Nigri, Jérémy / Guillaumond, Fabienne / Bigonnet, Martin / Dusetti, Nelson / Perrot, Jimmy / Lopez, Jonathan / Etzerodt, Anders / Lawrence, Toby / Pudlo, Pierre / Hubert, Florence / Scoazec, Jean-Yves /
    van de Pavert, Serge A / Tomasini, Richard / Chauvet, Sophie / Mann, Fanny

    Nature communications

    2022  Band 13, Heft 1, Seite(n) 1985

    Abstract: Neuronal nerve processes in the tumor microenvironment were highlighted recently. However, the origin of intra-tumoral nerves remains poorly known, in part because of technical difficulties in tracing nerve fibers via conventional histological ... ...

    Abstract Neuronal nerve processes in the tumor microenvironment were highlighted recently. However, the origin of intra-tumoral nerves remains poorly known, in part because of technical difficulties in tracing nerve fibers via conventional histological preparations. Here, we employ three-dimensional (3D) imaging of cleared tissues for a comprehensive analysis of sympathetic innervation in a murine model of pancreatic ductal adenocarcinoma (PDAC). Our results support two independent, but coexisting, mechanisms: passive engulfment of pre-existing sympathetic nerves within tumors plus an active, localized sprouting of axon terminals into non-neoplastic lesions and tumor periphery. Ablation of the innervating sympathetic nerves increases tumor growth and spread. This effect is explained by the observation that sympathectomy increases intratumoral CD163
    Mesh-Begriff(e) Animals ; Carcinoma, Pancreatic Ductal ; Macrophages ; Mice ; Pancreatic Neoplasms ; Sympathetic Nervous System/physiology ; Tumor Microenvironment ; Pancreatic Neoplasms
    Sprache Englisch
    Erscheinungsdatum 2022-04-13
    Erscheinungsland England
    Dokumenttyp Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2553671-0
    ISSN 2041-1723 ; 2041-1723
    ISSN (online) 2041-1723
    ISSN 2041-1723
    DOI 10.1038/s41467-022-29659-w
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  10. Artikel ; Online: DIYABC v2.0: a software to make approximate Bayesian computation inferences about population history using single nucleotide polymorphism, DNA sequence and microsatellite data.

    Cornuet, Jean-Marie / Pudlo, Pierre / Veyssier, Julien / Dehne-Garcia, Alexandre / Gautier, Mathieu / Leblois, Raphaël / Marin, Jean-Michel / Estoup, Arnaud

    Bioinformatics (Oxford, England)

    2014  Band 30, Heft 8, Seite(n) 1187–1189

    Abstract: Motivation: DIYABC is a software package for a comprehensive analysis of population history using approximate Bayesian computation on DNA polymorphism data. Version 2.0 implements a number of new features and analytical methods. It allows (i) the ... ...

    Abstract Motivation: DIYABC is a software package for a comprehensive analysis of population history using approximate Bayesian computation on DNA polymorphism data. Version 2.0 implements a number of new features and analytical methods. It allows (i) the analysis of single nucleotide polymorphism data at large number of loci, apart from microsatellite and DNA sequence data, (ii) efficient Bayesian model choice using linear discriminant analysis on summary statistics and (iii) the serial launching of multiple post-processing analyses. DIYABC v2.0 also includes a user-friendly graphical interface with various new options. It can be run on three operating systems: GNU/Linux, Microsoft Windows and Apple Os X.
    Availability: Freely available with a detailed notice document and example projects to academic users at http://www1.montpellier.inra.fr/CBGP/diyabc CONTACT: estoup@supagro.inra.fr Supplementary information: Supplementary data are available at Bioinformatics online.
    Mesh-Begriff(e) Bayes Theorem ; Computational Biology ; Genetics, Population/methods ; Humans ; Microsatellite Repeats ; Polymorphism, Single Nucleotide ; Sequence Analysis, DNA ; Software
    Sprache Englisch
    Erscheinungsdatum 2014-04-15
    Erscheinungsland England
    Dokumenttyp Journal Article
    ZDB-ID 1422668-6
    ISSN 1367-4811 ; 1367-4803
    ISSN (online) 1367-4811
    ISSN 1367-4803
    DOI 10.1093/bioinformatics/btt763
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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