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Martín Arevalillo, Jorge

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0000-0003-1944-3699
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Martín Arevalillo
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Jorge
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2018 - 201922020 - 202412
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Mostrando 1 - 10 de 14
  • Miniatura
    Publicación
    A novel approach to triplenegative breast cancer molecular classification reveals a luminal immune-positive subgroup with good prognoses
    (Springer nature, 2019-02-07) Prado Vázquez, Guillermo; Gámez Pozo, Ángelo; Trilla Fuertes, Lucía; Zapater Moros, Andrea; Ferrer Gómez, María; Díaz Almirón, Mariana; López Vacas, Rocío; Maín, Paloma; Feliu Batlle, Jaime; Zamora Auñón, Pilar; Espinosa, Enrique; Fresno Vara, Juan Ángel; Martín Arevalillo, Jorge; Navarro Veguillas, Hilario
    Triple-negative breast cancer is a heterogeneous disease characterized by a lack of hormonal receptors and HER2 overexpression. It is the only breast cancer subgroup that does not benefit from targeted therapies, and its prognosis is poor. Several studies have developed specific molecular classifications for triple-negative breast cancer. However, these molecular subtypes have had little impact in the clinical setting. Gene expression data and clinical information from 494 triple-negative breast tumors were obtained from public databases. First, a probabilistic graphical model approach to associate gene expression profiles was performed. Then, sparse k-means was used to establish a new molecular classification. Results were then verified in a second database including 153 triple-negative breast tumors treated with neoadjuvant chemotherapy. Clinical and gene expression data from 494 triple-negative breast tumors were analyzed. Tumors in the dataset were divided into four subgroups (luminal-androgen receptor expressing, basal, claudin-low and claudin-high), using the cancer stem cell hypothesis as reference. These four subgroups were defined and characterized through hierarchical clustering and probabilistic graphical models and compared with previously defined classifications. In addition, two subgroups related to immune activity were defined. This immune activity showed prognostic value in the whole cohort and in the luminal subgroup. The claudin-high subgroup showed poor response to neoadjuvant chemotherapy. Through a novel analytical approach we proved that there are at least two independent sources of biological information: cellular and immune. Thus, we developed two different and overlapping triple-negative breast cancer classifications and showed that the luminal immune-positive subgroup had better prognoses than the luminal immune-negative. Finally, this work paves the way for using the defined classifications as predictive features in the neoadjuvant scenario.
  • Miniatura
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    On the empirical approximation to quantiles from Lugannani–Rice saddlepoint formula
    (ELSEVIER, 2024) Martín Arevalillo, Jorge
    Lugannani–Rice saddlepoint formula approximates the tail probability and the cumulative distribution function of the sample mean of independent and equally distributed variables. This note revisits Lugannani–Rice formula with a proposal for inverting it to approximate the quantile of the distribution empirically. The asymptotic behavior of the empirical approximation is assessed theoretically and its numerical accuracy for finite samples is studied and compared with the normal approximation and the second order Cornish-Fisher expansion by means of a simulation study. The outcomes of the simulation experiment shed light on the limitations of the empirical inversions of saddlepoint formulae to approximate quantiles
  • Miniatura
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    Data projections by skewness maximization under scale mixtures of skew-normal vectors
    (Springer, 2020-02-22) Martín Arevalillo, Jorge; Navarro Veguillas, Hilario
    Multivariate scale mixtures of skew-normal distributions are flexible models that account for the non-normality of data by means of a tail weight parameter and a shape vector representing the asymmetry of the model in a directional fashion. Its stochastic representation involves a skew-normal vector and a non negative mixing scalar variable, independent of the skew-normal vector, that injects tail weight behavior into the model. In this paper we look into the problem of finding the projection that maximizes skewness for vectors that follow a scale mixture of skew-normal distribution; when a simple condition on the moments of the mixing variable is fulfilled, it can be shown that the direction yielding the maximal skewness is proportional to the shape vector. This finding stresses the directional nature of the shape vector to regulate the asymmetry; it also provides the theoretical foundations motivating the skewness based projection pursuit problem in this class of distributions. Some examples that illustrate the application of our results are also given; they include a simulation experiment with artificial data, which sheds light on the usefulness and implications of our results, and the application to real data.
  • Miniatura
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    A stochastic ordering based on the canonical transformation of skew-normal vectors
    (Springer, 2018-04-25) Martín Arevalillo, Jorge; Navarro Veguillas, Hilario
    In this paper, we define a new skewness ordering that enables stochastic comparisons for vectors that follow a multivariate skew-normal distribution. The new ordering is based on the canonical transformation associated with the multivariate skew-normal distribution and on the well-known convex transform order applied to the only skewed component of such canonical transformation. We examine the connection between the proposed ordering and the multivariate convex transform order studied by Belzunce et al. (TEST 24(4):813–834, 2015). Several standard skewness measures like Mardia’s and Malkovich–Afifi’s indices are revisited and interpreted in connection with the new ordering; we also study its relationship with the J-divergence between skew-normal and normal random vectors and with the Negentropy. Some artificial data are used in simulation experiments to illustrate the theoretical discussion; a real data application is provided as well.
  • Miniatura
    Publicación
    New Insights on the Multivariate Skew Exponential Power Distribution
    (De Gruyter, 2023) Martín Arevalillo, Jorge; Navarro Veguillas, Hilario
    The multivariate exponential power is a useful distribution for modeling departures from normality in data by means of a tail weight scalar parameter that regulates the non-normality of the model. The incorporation of a shape asymmetry vector into the model serves to account for potential asymmetries and gives rise to the multivariate skew exponential power distribution. This work is aimed at revisiting the skew exponential power distribution taking as a starting point its formulation as a scale mixture of skew-normal distributions. The paper provides some highlights and theoretical insights on the role played by its parameters to assess two complementary aspects of the multivariate non-normality such as directional asymmetry and tail weight behavior regardless of the asymmetry. The intuition behind both issues relies on well-known mathematical ideas about skewness maximization and convex transform stochastic orderings.
  • Miniatura
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    Patterns of differential expression by association in omic data using a new measure based on ensemble learning
    (De Gruyter, 2023-11-23) Martín Arevalillo, Jorge; Martín Arevalillo, Raquel; https://orcid.org/0000-0003-0674-0053
    The ongoing development of high-throughput technologies is allowing the simultaneous monitoring of the expression levels for hundreds or thousands of biological inputs with the proliferation of what has been coined as omic data sources. One relevant issue when analyzing such data sources is concerned with the detection of differential expression across two experimental conditions, clinical status or two classes of a biological outcome. While a great deal of univariate data analysis approaches have been developed to address the issue, strategies for assessing interaction patterns of differential expression are scarce in the literature and have been limited to ad hoc solutions. This paper contributes to the problem by exploiting the facilities of an ensemble learning algorithm like random forests to propose a measure that assesses the differential expression explained by the interaction of the omic variables so subtle biological patterns may be uncovered as a result. The out of bag error rate, which is an estimate of the predictive accuracy of a random forests classifier, is used as a by-product to propose a new measure that assesses interaction patterns of differential expression. Its performance is studied in synthetic scenarios and it is also applied to real studies on SARS-CoV-2 and colon cancer data where it uncovers associations that remain undetected by other methods. Our proposal is aimed at providing a novel approach that may help the experts in biomedical and life sciences to unravel insightful interaction patterns that may decipher the molecular mechanisms underlying biological and clinical outcomes.
  • Miniatura
    Publicación
    Skewness-Based Projection Pursuit as an Eigenvector Problem in Scale Mixtures of Skew-Normal Distributions
    (MDPI, 2021-06-03) Martín Arevalillo, Jorge; Navarro Veguillas, Hilario
    This paper addresses the projection pursuit problem assuming that the distribution of the input vector belongs to the flexible and wide family of multivariate scale mixtures of skew normal distributions. Under this assumption, skewness-based projection pursuit is set out as an eigenvector problem, described in terms of the third order cumulant matrix, as well as an eigenvector problem that involves the simultaneous diagonalization of the scatter matrices of the model. Both approaches lead to dominant eigenvectors proportional to the shape parametric vector, which accounts for the multivariate asymmetry of the model; they also shed light on the parametric interpretability of the invariant coordinate selection method and point out some alternatives for estimating the projection pursuit direction. The theoretical findings are further investigated through a simulation study whose results provide insights about the usefulness of skewness model-based projection pursuit in the statistical practice.
  • Miniatura
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    Assessment of extreme records in environmental data through the study of stochastic orders for scale mixtures of skew normal vectors
    (Springer, 2024-02-18) Martín Arevalillo, Jorge; Navarro, Jorge
    Scale mixtures of skew normal distributions are flexible models well-suited to handle departures from multivariate normality. This paper is concerned with the stochastic comparison of vectors that belong to the family of scale mixtures of skew normal distributions. The paper revisits some of their properties with a proposal that allows to carry out tail weight stochastic comparisons. The connections of the proposed stochastic orders with the non-normality parameters of the multivariate model are also studied for some popular distributions within the family. The role played by these parameters to tackle the non-normality of multivariate data is enhanced as a result. This work is motivated by the analysis of multivariate data in environmental studies which usually collect maximum or minimum values exhibiting departures from normality. The implications of our theoretical results in addressing the stochastic comparison of extreme environmental records is illustrated with an application to a real data study on maximum temperatures in the Iberian Peninsula throughout the last century. The resulting findings may elucidate whether extreme temperatures are evolving for such a long period.
  • Miniatura
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    A New Separation Index and Classification Techniques Based on Shannon Entropy
    (Springer, 2023-09-22) Navarro, Jorge; Buono, Francesco; Martín Arevalillo, Jorge; https://orcid.org/0000-0003-2822-915X; https://orcid.org/0000-0002-3569-4052
    The purpose is to use Shannon entropy measures to develop classification techniques and an index which estimates the separation of the groups in a finite mixture model. These measures can be applied to machine learning techniques such as discriminant analysis, cluster analysis, exploratory data analysis, etc. If we know the number of groups and we have training samples from each group (supervised learning) the index is used to measure the separation of the groups. Here some entropy measures are used to classify new individuals in one of these groups. If we are not sure about the number of groups (unsupervised learning), the index can be used to determine the optimal number of groups from an entropy (information/uncertainty) criterion. It can also be used to determine the best variables in order to separate the groups. In all the cases we assume that we have absolutely continuous random variables and we use the Shannon entropy based on the probability density function. Theoretical, parametric and non-parametric techniques are proposed to get approximations of these entropy measures in practice. An application to gene selection in a colon cancer discrimination study with a lot of variables is provided as well.
  • Miniatura
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    Bayesian networks established functional differences between breast cancer subtypes
    (PLOS, 2020-06-11) Trilla Fuertes, Lucía; Gámez Pozo, Ángelo; López Vacas, Rocío; López Camacho, Elena; Prado Vázquez, Guillermo; Zapater Moros, Andrea; Díaz Almirón, Mariana; Ferrer Gómez, María; Nanni, Paolo; Zamora Auñón, Pilar; Espinosa, Enrique; Maín, Paloma; Fresno Vara, Juan Ángel; Martín Arevalillo, Jorge; Navarro Veguillas, Hilario
    Breast cancer is a heterogeneous disease. In clinical practice, tumors are classified as hormonal receptor positive, Her2 positive and triple negative tumors. In previous works, our group defined a new hormonal receptor positive subgroup, the TN-like subtype, which had a prognosis and a molecular profile more similar to triple negative tumors. In this study, proteomics and Bayesian networks were used to characterize protein relationships in 96 breast tumor samples. Components obtained by these methods had a clear functional structure. The analysis of these components suggested differences in processes such as mitochondrial function or extracellular matrix between breast cancer subtypes, including our new defined subtype TN-like. In addition, one of the components, mainly related with extracellular matrix processes, had prognostic value in this cohort. Functional approaches allow to build hypotheses about regulatory mechanisms and to establish new relationships among proteins in the breast cancer context.