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Book ; 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.
Keywords	Computer Science - Sound ; Electrical Engineering and Systems Science - Audio and Speech Processing ; Statistics - Applications ; Statistics - Machine Learning
Subject code	514
Publishing date	2023-10-10
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: An overview on Approximate Bayesian computation*

Baragatti Meïli / Pudlo Pierre

ESAIM : Proceedings , Vol 44, Pp 291-

2014 Volume 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.
Keywords	Mathematics ; QA1-939 ; Science ; Q
Language	English
Publishing date	2014-01-01T00:00:00Z
Publisher	EDP Sciences
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; 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.
Keywords	Computer Science - Sound ; Computer Science - Machine Learning ; Electrical Engineering and Systems Science - Audio and Speech Processing ; Statistics - Applications
Subject code	006
Publishing date	2023-02-14
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; 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 , Page(s) 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.
Keywords	Bayesian theory ; bioinformatics ; evolution ; human population ; population genetics
Language	English
Dates of publication	2019-0515
Size	p. 1720-1728
Publishing place	Oxford University Press
Document type	Article ; Online
Note	Use and reproduction
ZDB-ID	1422668-6
ISSN	1367-4803
ISSN	1367-4803
DOI	10.1093/bioinformatics/bty867
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Article ; 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 Volume 35, Issue 10, Page(s) 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 term(s)	Bayes Theorem ; Biometry ; Computer Simulation ; Genetics, Population ; Humans ; Likelihood Functions
Language	English
Publishing date	2019-02-09
Publishing country	England
Document type	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
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Article ; 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 Volume 110, Issue 4, Page(s) 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 term(s)	Algorithms ; Bayes Theorem ; Biophysical Phenomena ; Biostatistics ; Genetics, Population/statistics & numerical data ; Likelihood Functions ; Models, Statistical ; Stochastic Processes
Language	English
Publishing date	2013-01-07
Publishing country	United States
Document type	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
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Article ; 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 Volume 32, Issue 6, Page(s) 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 term(s)	Algorithms ; Bayes Theorem ; Computer Simulation ; Genetics, Population ; Models, Genetic
Language	English
Publishing date	2016-03-15
Publishing country	England
Document type	Journal Article
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btv684
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Article ; Online: Sympathetic axonal sprouting induces changes in macrophage populations and protects against pancreatic cancer.

Nature communications

2022 Volume 13, Issue 1, Page(s) 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 term(s)	Animals ; Carcinoma, Pancreatic Ductal ; Macrophages ; Mice ; Pancreatic Neoplasms ; Sympathetic Nervous System/physiology ; Tumor Microenvironment ; Pancreatic Neoplasms
Language	English
Publishing date	2022-04-13
Publishing country	England
Document type	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
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Article: 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 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.
Keywords	Bayesian theory ; algorithms ; artificial intelligence ; bioinformatics ; computer software ; data collection ; models ; population genetics ; prediction ; probability
Language	English
Dates of publication	2016-0315
Size	p. 859-866.
Publishing place	Oxford University Press
Document type	Article
ZDB-ID	1468345-3
ISSN	1460-2059 ; 1367-4803
ISSN (online)	1460-2059
ISSN	1367-4803
DOI	10.1093/bioinformatics/btv684
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Maximum-likelihood inference of population size contractions from microsatellite data.

Leblois, Raphaël / Pudlo, Pierre / Néron, Joseph / Bertaux, François / Reddy Beeravolu, Champak / Vitalis, Renaud / Rousset, François

Molecular biology and evolution

2014 Volume 31, Issue 10, Page(s) 2805–2823

Abstract: Understanding the demographic history of populations and species is a central issue in evolutionary biology and molecular ecology. In this work, we develop a maximum-likelihood method for the inference of past changes in population size from ... ...

Abstract	Understanding the demographic history of populations and species is a central issue in evolutionary biology and molecular ecology. In this work, we develop a maximum-likelihood method for the inference of past changes in population size from microsatellite allelic data. Our method is based on importance sampling of gene genealogies, extended for new mutation models, notably the generalized stepwise mutation model (GSM). Using simulations, we test its performance to detect and characterize past reductions in population size. First, we test the estimation precision and confidence intervals coverage properties under ideal conditions, then we compare the accuracy of the estimation with another available method (MSVAR) and we finally test its robustness to misspecification of the mutational model and population structure. We show that our method is very competitive compared with alternative ones. Moreover, our implementation of a GSM allows more accurate analysis of microsatellite data, as we show that the violations of a single step mutation assumption induce very high bias toward false contraction detection rates. However, our simulation tests also showed some limits, which most importantly are large computation times for strong disequilibrium scenarios and a strong influence of some form of unaccounted population structure. This inference method is available in the latest implementation of the MIGRAINE software package.
MeSH term(s)	Animals ; Computational Biology/methods ; Likelihood Functions ; Microsatellite Repeats ; Models, Genetic ; Mutation ; Pongo/genetics ; Population Density ; Software
Language	English
Publishing date	2014-10
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	998579-7
ISSN	1537-1719 ; 0737-4038
ISSN (online)	1537-1719
ISSN	0737-4038
DOI	10.1093/molbev/msu212
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