DES - Artículos de Revistas

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Multidimensional adaptive P-splines with application to neurons' activity studies
(2023-09-01) Eilers, Paul H.C.; Durbán Reguera, María Luz; Rodríguez-Alvarez, María Xosé; Lee, Dae-Jin; González, Francisco
The receptive field (RF) of a visual neuron is the region of the space that elicits neuronal responses. It can be mapped using different techniques that allow inferring its spatial and temporal properties. Raw RF maps (RFmaps) are usually noisy, making it difficult to obtain and study important features of the RF. A possible solution is to smooth them using P-splines. Yet, raw RFmaps are characterized by sharp transitions in both space and time. Their analysis thus asks for spatiotemporal adaptive P-spline models, where smoothness can be locally adapted to the data. However, the literature lacks proposals for adaptive P-splines in more than two dimensions. Furthermore, the extra flexibility afforded by adaptive P-spline models is obtained at the cost of a high computational burden, especially in a multidimensional setting. To fill these gaps, this work presents a novel anisotropic locally adaptive P-spline model in two (e.g., space) and three (space and time) dimensions. Estimation is based on the recently proposed SOP (Separation of Overlapping Precision matrices) method, which provides the speed we look for. Besides the spatiotemporal analysis of the neuronal activity data that motivated this work, the practical performance of the proposal is evaluated through simulations, and comparisons with alternative methods are reported.
Derivative curve estimation in longitudinal studies using P-splines
(SAGE Publications, 2023-10-01) Hernández, María Alejandra; Lee, Dae-Jin; Rodríguez Álvarez, María Xosé; Durbán Reguera, María Luz
The estimation of curve derivatives is of interest in many disciplines. It allows the extraction of important characteristics to gain insight about the underlying process. In the context of longitudinal data, the derivative allows the description of biological features of the individuals or finding change regions of interest. Although there are several approaches to estimate subject-specific curves and their derivatives, there are still open problems due to the complicated nature of these time course processes. In this article, we illustrate the use of P-spline models to estimate derivatives in the context of longitudinal data. We also propose a new penalty acting at the population and the subject-specific levels to address under-smoothing and boundary problems in derivative estimation. The practical performance of the proposal is evaluated through simulations, and comparisons with an alternative method are reported. Finally, an application to longitudinal height measurements of 125 football players in a youth professional academy is presented, where the goal is to analyse their growth and maturity patterns over time.
Modeling latent spatio-temporal disease incidence using penalized composite link models
(Indiana University, 2022-03-10) Lee Hwang, Dae-Jin; Durbán Reguera, María Luz; Ayma Anza, Diego Armando; Van De Kassteele, Jan; Agencia Estatal de Investigación (España)
Epidemiological data are frequently recorded at coarse spatio-temporal resolutions to protect confidential information or to summarize it in a compact manner. However, the detailed patterns followed by the source data, which may be of interest to researchers and public health officials, are overlooked. We propose to use the penalized composite link model (Eilers PCH (2007)), combined with spatio-temporal P-splines methodology (Lee D.-J., Durban M (2011)) to estimate the underlying trend within data that have been aggregated not only in space, but also in time. Model estimation is carried out within a generalized linear mixed model framework, and sophisticated algorithms are used to speed up computations that otherwise would be unfeasible. The model is then used to analyze data obtained during the largest outbreak of Q-fever in the Netherlands.
An alternative semiparametric model for spatial panel data
(Springer, 2020-12-01) Minguez, Román; Basile, Roberto; Durbán Reguera, María Luz; Ministerio de Economía y Competitividad (España)
We propose a semiparametric P-Spline model to deal with spatial panel data. This model includes a non-parametric spatio-temporal trend, a spatial lag of the dependent variable, and a time series autoregressive noise. Specifically, we consider a spatio-temporal ANOVA model, disaggregating the trend into spatial and temporal main effects, as well as second- and third-order interactions between them. Algorithms based on spatial anisotropic penalties are used to estimate all the parameters in a closed form without the need for multidimensional optimization. Monte Carlo simulations and an empirical analysis of regional unemployment in Italy show that our model represents a valid alternative to parametric methods aimed at disentangling strong and weak cross-sectional dependence when both spatial and temporal heterogeneity are smoothly distributed.
Multidimensional risk in a nonstationary climate: joint probability of increasingly severe warm and dry conditions
(American Association for the Advancement of Science, 2018-11-28) Sarhadi, Ali; Ausín Olivera, María Concepción; Wiper, Michael Peter; Touma, Danielle; Diffenbaugh, Noah S
We present a framework for quantifying the spatial and temporal co-occurrence of climate stresses in a nonstationary climate. We find that, globally, anthropogenic climate forcing has doubled the joint probability of years that are both warm and dry in the same location (relative to the 1961-1990 baseline). In addition, the joint probability that key crop and pasture regions simultaneously experience severely warm conditions in conjunction with dry years has also increased, including high statistical confidence that human influence has increased the probability of previously unprecedented co-occurring combinations. Further, we find that ambitious emissions mitigation, such as that in the United Nations Paris Agreement, substantially curbs increases in the probability that extremely hot years co-occur with low precipitation simultaneously in multiple regions. Our methodology can be applied to other climate variables, providing critical insight for a number of sectors that are accustomed to deploying resources based on historical probabilities.
A global indicator to track well-being in the silver and golden age
(Springer, 2023-01-01) Guo, Qi; Grané Chávez, Aurea; Albarrán Lozano, Irene; Agencia Estatal de Investigación (España)
In this work, we design a protocol to obtain global indicators of health and well-being from weighted and longitudinal heterogeneous multivariate data. First, we consider a set of thematic sub-indicators of interest observed in several periods. Next, we combine them using the Common Principal Component (CPC) model. For this purpose, we put a new straightforward CPC model to cope with weighted and longitudinal data and develop a new statistic to test the validity of the CPC-longitudinal model, whose distribution is obtained by stratified bootstrap. To illustrate this methodology, we use data from the last three waves of the Survey of Health, Ageing and Retirement in Europe (SHARE), which is the largest cross-European social science panel study data set covering insights into the public health and socio-economic living conditions of European individuals. In particular, we first design four thematic indicators that focus on general health status, dependency situation, self-perceived health, and socio-economic status. We then apply the CPC-longitudinal model to obtain a global indicator to track the well-being in the silver and golden age in the 18 participating European countries from 2015 to 2020. We found that the latest survey wave 8 captures the early reactions of respondents successfully. The pandemic significantly worsens people"s physical health conditions; however, the analysis of their self-perceived health presents a delay. Tracking the performances of our global indicator, we also found that people living in Northern Europe mainly have better health and well-being status than in other participating countries.
WHODAS 2.0 as a measure of severity of illness: results of a FLDA Analysis
(Hindawi, 2018-03-25) Sedano Capdevila, Alba; Aroca, Fuentasana; Barrigon Estevez, Maria Luisa; Baca Garcia, Enrique; Delgado Gómez, David; Peñuelas Calvo, Inmaculada; Fernandez, Carolina; Rodriguez Jover, Alba; Amodeo Escribano, Susana; Gonzalez Granado, Marta; Barahona, Igor
WHODAS 2.0 is the standard measure of disability promoted by World Health Organization whereas Clinical Global Impression (CGI) is a widely used scale for determining severity of mental illness. Although a close relationship between these two scales would be expected, there are no relevant studies on the topic. In this study, we explore if WHODAS 2.0 can be used for identifying severity of illness measured by CGI using the Fisher Linear Discriminant Analysis (FLDA) and for identifying which individual items of WHODAS 2.0 best predict CGI scores given by clinicians. One hundred and twenty-two patients were assessed with WHODAS 2.0 and CGI during three months in outpatient mental health facilities of four hospitals of Madrid, Spain. Compared with the traditional correction of WHODAS 2.0, FLDA improves accuracy in near 15%, and so, with FLDA WHODAS 2.0 classifying correctly 59.0% of the patients. Furthermore, FLDA identifies item 6.6 (illness effect on personal finances) and item 4.5 (damaged sexual life) as the most important items for clinicians to score the severity of illness.
Identification of asymmetric conditional heteroscedasticity in the presence of outliers
(Springer Nature, 2016-03) Carnero, María Ángeles; Pérez, Ana; Ruiz Ortega, Esther; Ministerio de Economía y Competitividad (España)
The identification of asymmetric conditional heteroscedasticity is often based on sample cross-correlations between past and squared observations. In this paper we analyse the effects of outliers on these cross-correlations and, consequently, on the identification of asymmetric volatilities. We show that, as expected, one isolated big outlier biases the sample cross-correlations towards zero and hence could hide true leverage effect. Unlike, the presence of two or more big consecutive outliers could lead to detecting spurious asymmetries or asymmetries of the wrong sign. We also address the problem of robust estimation of the cross-correlations by extending some popular robust estimators of pairwise correlations and autocorrelations. Their finite sample resistance against outliers is compared through Monte Carlo experiments. Situations with isolated and patchy outliers of different sizes are examined. It is shown that a modified Ramsay-weighted estimator of the cross-correlations outperforms other estimators in identifying asymmetric conditionally heteroscedastic models. Finally, the results are illustrated with an empirical application.
Time-varying nonstationary multivariate risk analysis using a dynamic Bayesian copula
(American Geophysical Union, 2016-03) Sarhadi, Ali; Burn, Donald H.; Ausín Olivera, María Concepción; Wiper, Michael Peter
A time-varying risk analysis is proposed for an adaptive design framework in nonstationary conditions arising from climate change. A Bayesian, dynamic conditional copula is developed for modeling the time-varying dependence structure between mixed continuous and discrete multiattributes of multidimensional hydrometeorological phenomena. Joint Bayesian inference is carried out to fit the marginals and copula in an illustrative example using an adaptive, Gibbs Markov Chain Monte Carlo (MCMC) sampler. Posterior mean estimates and credible intervals are provided for the model parameters and the Deviance Information Criterion (DIC) is used to select the model that best captures different forms of nonstationarity over time. This study also introduces a fully Bayesian, time-varying joint return period for multivariate time-dependent risk analysis in nonstationary environments.
A new time-varying concept of risk in a changing climate
(Nature Research, 2016-10-20) Sarhadi, Ali; Ausín Olivera, María Concepción; Wiper, Michael Peter
In a changing climate arising from anthropogenic global warming, the nature of extreme climatic events is changing over time. Existing analytical stationary-based risk methods, however, assume multi-dimensional extreme climate phenomena will not significantly vary over time. To strengthen the reliability of infrastructure designs and the management of water systems in the changing environment, multidimensional stationary risk studies should be replaced with a new adaptive perspective. The results of a comparison indicate that current multi-dimensional stationary risk frameworks are no longer applicable to projecting the changing behaviour of multi-dimensional extreme climate processes. Using static stationary-based multivariate risk methods may lead to undesirable consequences in designing water system infrastructures. The static stationary concept should be replaced with a flexible multi-dimensional time-varying risk framework. The present study introduces a new multi-dimensional time-varying risk concept to be incorporated in updating infrastructure design strategies under changing environments arising from human-induced climate change. The proposed generalized time-varying risk concept can be applied for all stochastic multi-dimensional systems that are under the influence of changing environments.
Electricity price forecasting by averaging dynamic factor models
(MDPI, 2016-08-01) Alonso Fernández, Andrés Modesto; Bastos, Guadalupe; García-Martos, Carolina; Ministerio de Economía y Competitividad (España)
In the context of the liberalization of electricity markets, forecasting prices is essential. With this aim, research has evolved to model the particularities of electricity prices. In particular, dynamic factor models have been quite successful in the task, both in the short and long run. However, specifying a single model for the unobserved factors is difficult, and it cannot be guaranteed that such a model exists. In this paper, model averaging is employed to overcome this difficulty, with the expectation that electricity prices would be better forecast by a combination of models for the factors than by a single model. Although our procedure is applicable in other markets, it is illustrated with an application to forecasting spot prices of the Iberian Market, MIBEL (The Iberian Electricity Market). Three combinations of forecasts are successful in providing improved results for alternative forecasting horizons.
Twenty-Two Years of Inflation Assessment and Forecasting Experience at the Bulletin of EU & US Inflation and Macroeconomic Analysis
(MDPI, 2017-01-01) Espasa, Antoni; Senra, Eva; Ministerio de Economía y Competitividad (España)
The Bulletin of EU & US Inflation and Macroeconomic Analysis (BIAM) is a monthly publication that has been reporting real time analysis and forecasts for inflation and other macroeconomic aggregates for the Euro Area, the US and Spain since 1994. The BIAM inflation forecasting methodology stands on working with useful disaggregation schemes, using leading indicators when possible and applying outlier correction. The paper relates this methodology to corresponding topics in the literature and discusses the design of disaggregation schemes. It concludes that those schemes would be useful if they were formulated according to economic, institutional and statistical criteria aiming to end up with a set of components with very different statistical properties for which valid single-equation models could be built. The BIAM assessment, which derives from a new observation, is based on (a) an evaluation of the forecasting errors (innovations) at the components' level. It provides information on which sectors they come from and allows, when required, for the appropriate correction in the specific models. (b) In updating the path forecast with its corresponding fan chart. Finally, we show that BIAM real time Euro Area inflation forecasts compare successfully with the consensus from the ECB Survey of Professional Forecasters, one and two years ahead.
Microsoft Kinect-based Continuous Performance Test: An Objective Attention Deficit Hyperactivity Disorder Assessment
(JMIR Publications Inc., 2017-03-20) Delgado Gómez, David; Peñuelas Calvo, Inmaculada; Masó Besga, Antonio Eduardo; Vallejo Onate, Silvia; Baltasar Tello, Itziar; Arrua Duarte, Elsa; Vera Varela, Maria Constanza; Carballo, Juan; Baca García, Enrique; Ministerio de Ciencia e Innovación (España)
Background: One of the major challenges in mental medical care is finding out new instruments for an accurate and objective evaluation of the attention deficit hyperactivity disorder (ADHD). Early ADHD identification, severity assessment, and prompt treatment are essential to avoid the negative effects associated with this mental condition. Objective: The aim of our study was to develop a novel ADHD assessment instrument based on Microsoft Kinect, which identifies ADHD cardinal symptoms in order to provide a more accurate evaluation. Methods: A group of 30 children, aged 8-12 years (10.3 [SD 1.4]; male 70% [21/30]), who were referred to the Child and Adolescent Psychiatry Unit of the Department of Psychiatry at Fundación Jiménez Díaz Hospital (Madrid, Spain), were included in this study. Children were required to meet the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) criteria of ADHD diagnosis. One of the parents or guardians of the children filled the Spanish version of the Strengths and Weaknesses of ADHD Symptoms and Normal Behavior (SWAN) rating scale used in clinical practice. Each child conducted a Kinect-based continuous performance test (CPT) in which the reaction time (RT), the commission errors, and the time required to complete the reaction (CT) were calculated. The correlations of the 3 predictors, obtained using Kinect methodology, with respect to the scores of the SWAN scale were calculated. Results: The RT achieved a correlation of -.11, -.29, and -.37 with respect to the inattention, hyperactivity, and impulsivity factors of the SWAN scale. The correlations of the commission error with respect to these 3 factors were -.03, .01, and .24, respectively. Conclusions: Our findings show a relation between the Microsoft Kinect-based version of the CPT and ADHD symptomatology assessed through parental report. Results point out the importance of future research on the development of objective measures for the diagnosis of ADHD among children and adolescents.
Ecological assessment of clinicians antipsychotic prescription habits in psychiatric inpatients: a novel web-and Mobile Phone-based prototype for a dynamic clinical decision support system
(JMIR Publications, 2017-01) Berrouiguet, Sofian; Barrigón Estévez, María Luisa; Brandt, Sara A.; Nitzburg, George C.; Ovejero, Santiago; Álvarez-García, Raquel; Carballo, Juan; Walter, Michel; Billot, Romain; Lenca, Philippe; Delgado Gómez, David; Ropars, Juliette; Calle González, Iván de la; Courtet, Philippe; Baca-García, Enrique
Background: Electronic prescribing devices with clinical decision support systems (CDSSs) hold the potential to significantly improve pharmacological treatment management. Objective: The aim of our study was to develop a novel Web- and mobile phone-based application to provide a dynamic CDSS by monitoring and analyzing practitioners' antipsychotic prescription habits and simultaneously linking these data to inpatients' symptom changes. Methods: We recruited 353 psychiatric inpatients whose symptom levels and prescribed medications were inputted into the MEmind application. We standardized all medications in the MEmind database using the Anatomical Therapeutic Chemical (ATC) classification system and the defined daily dose (DDD). For each patient, MEmind calculated an average for the daily dose prescribed for antipsychotics (using the N05A ATC code), prescribed daily dose (PDD), and the PDD to DDD ratio. Results: MEmind results found that antipsychotics were used by 61.5% (217/353) of inpatients, with the largest proportion being patients with schizophrenia spectrum disorders (33.4%, 118/353). Of the 217 patients, 137 (63.2%, 137/217) were administered pharmacological monotherapy and 80 (36.8%, 80/217) were administered polytherapy. Antipsychotics were used mostly in schizophrenia spectrum and related psychotic disorders, but they were also prescribed in other nonpsychotic diagnoses. Notably, we observed polypharmacy going against current antipsychotics guidelines. Conclusions: MEmind data indicated that antipsychotic polypharmacy and off-label use in inpatient units is commonly practiced. MEmind holds the potential to create a dynamic CDSS that provides real-time tracking of prescription practices and symptom change. Such feedback can help practitioners determine a maximally therapeutic drug treatment while avoiding unproductive overprescription and off-label use.
Bayesian analysis of the stationary MAP2
(International Society for Bayesian Analysis, 2017-12) Ramírez Cobo, Josefa; Lillo Rodríguez, Rosa Elvira; Wiper, Michael Peter; Ministerio de Economía y Competitividad (España)
In this article we describe a method for carrying out Bayesian estimation for the two-state stationary Markov arrival process (MAP(2)), which has been proposed as a versatile model in a number of contexts. The approach is illustrated on both simulated and real data sets, where the performance of the MAP(2) is compared against that of the well-known MMPP2. As an extension of the method, we estimate the queue length and virtual waiting time distributions of a stationary MAP(2)/G/1 queueing system, a matrix generalization of the M/G/1 queue that allows for dependent inter-arrival times. Our procedure is illustrated with applications in Internet traffic analysis.
Testing for voter rigging in small polling stations
(American Association for the Advancement of Science, 2017-06-02) Jiménez Recaredo, Raúl José; Hidalgo Trenado, Manuel; Klimek, Peter; European Commission; Ministerio de Economía y Competitividad (España)
Nowadays, a large number of countries combine formal democratic institutions with authoritarian practices. Althoughin these countries the ruling elites may receive considerable voter support, they often use several manipulation toolsto control election outcomes. A common practice of these regimes is the coercion and mobilization of large numbersof voters. This electoral irregularity is known as voter rigging, distinguishing it from vote rigging, which involves ballotstuffing or stealing. We develop a statistical test to quantify the extent to which the results of a particular electiondisplay traces of voter rigging. Our key hypothesis is that small polling stations are more susceptible to voter riggingbecause it is easier to identify opposing individuals, there are fewer eyewitnesses, and interested parties might reasonablyexpect fewer visits from election observers. We devise a general statistical method for testing whether votingbehavior in small polling stations is significantly different from the behavior in their neighbor stations in a way that isconsistent with the widespread occurrence of voter rigging. On the basis of a comparative analysis, the methodenables third parties to conclude that an explanation other than simple variability is needed to explain geographicheterogeneities in vote preferences. We analyze 21 elections in 10 countries and find significant statistical anomaliescompatible with voter rigging in Russia from 2007 to 2011, in Venezuela from 2006 to 2013, and in Uganda in 2011.Particularly disturbing is the case of Venezuela, where the smallest polling stations were decisive to the outcome of the2013 presidential elections.
The electronic mental wellness tool as a self-administered brief screening instrument for mental disorders in the general Spanish population during the post-COVID-19 era
(MDPI, 2023-02-01) Martinez Nicolas, Ismael; Basaraba, Cale; Delgado Gómez, David; López Fernández, Olatz; Baca García, Enrique; Wainberg, Milton L.
(1) Background: In the 'post-COVID-19 era', there is a need to focus on properly assessing and addressing the extent of its well-established mental health collateral damage. The 'Electronic Mental Wellness Tool' (E-mwTool) is a 13-item validated stepped-care or stratified management instrument that aims at the high-sensitivity captures of individuals with mental health disorders to determine the need for mental health care. This study validated the E-mwTool in a Spanish-speaking population. (2) Methods: It is a cross-sectional validation study using the Mini International Neuropsychiatric Interview as a criterion standard in a sample of 433 participants. (3) Results: About 72% of the sample had a psychiatric disorder, and 67% had a common mental disorder. Severe mental disorders, alcohol use disorders, substance use disorders, and suicide risk had a much lower prevalence rate (6.7%, 6.2%, 3.2%, and 6.2%, respectively). The first three items performed excellently in identifying any mental health disorder with 0.97 sensitivity. Ten additional items classified participants with common mental disorders, severe mental disorders, substance use disorders, and suicide risk. (4) Conclusions: The E-mwTool had high sensitivity in identifying common mental disorders, alcohol and substance use disorders, and suicidal risk. However, the tool's sensitivity in detecting low-prevalence disorders in the sample was low. This Spanish version may be useful to detect patients at risk of mental health burden at the front line of primary and secondary care in facilitating help-seeking and referral by their physicians.
Computer-aided detection and classification of monkeypox and chickenpox lesion in human subjects using deep learning framework
(MDPI, 2023-01-12) Uzun Ozsahin, Dilber; Mustapha, Mubarak Taiwo; Uzun, Berna; Duwa, Basil; Ozsahin, Ilker
Monkeypox is a zoonotic viral disease caused by the monkeypox virus. After its recent outbreak, it has become clear that a rapid, accurate, and reliable diagnosis may help reduce the risk of a future outbreak. The presence of skin lesions is one of the most prominent symptoms of the disease. However, this symptom is also peculiar to chickenpox. The resemblance in skin lesions in the human subject may disrupt effective diagnosis and, as a result, lead to misdiagnosis. Such misdiagnosis can lead to the further spread of the disease as it is a communicable disease and can eventually result in an outbreak. As deep learning (DL) algorithms have recently been regarded as a promising technique in medical fields, we have been attempting to integrate a well-trained DL algorithm to assist in the early detection and classification of skin lesions in human subjects. This study used two open-sourced digital skin images for monkeypox and chickenpox. A two-dimensional convolutional neural network (CNN) consisting of four convolutional layers was applied. Afterward, three MaxPooling layers were used after the second, third, and fourth convolutional layers. Finally, we evaluated the performance of our proposed model with state of the art deep-learning models for skin lesions detection. Our proposed CNN model outperformed all DL models with a test accuracy of 99.60%. In addition, a weighted average precision, recall, F1 score of 99.00% was recorded. Subsequently, Alex Net outperformed other pre-trained models with an accuracy of 98.00%. The VGGNet consisting of VGG16 and VGG19 performed least well with an accuracy of 80.00%. Due to the uniqueness of the proposed model and image augmentation techniques applied, the proposed CNN model is generalized and avoids over-fitting. This model would be helpful for the rapid and accurate detection of monkeypox using digital skin images of patients with suspected monkeypox.
Clinical modelling of RVHF using pre-operative Variables:a direct and inverse feature extraction technique
(MDPI, 2022-12-06) Uzun Ozsahin, Dilber; Balcioglu, Ozlem; Garba Usman, Abdullahi; Ikechukwu Emegano, Declan; Uzun, Berna; Isah Abba, Sani; Ozsahin, Ilker; Ozsahin Ozsahin, Tahir; Engin, Cagatay
Right ventricular heart failure (RVHF) mostly occurs due to the failure of the left-side of the heart. RVHF is a serious disease that leads to swelling of the abdomen, ankles, liver, kidneys, and gastrointestinal (GI) tract. A total of 506 heart-failure subjects from the Faculty of Medicine, Cardiovascular Surgery Department, Ege University, Turkey, who suffered from a severe heart failure and are currently receiving support from a ventricular assistance device, were involved in the current study. Therefore, the current study explored the application of both the direct and inverse modelling approaches, based on the correlation analysis feature extraction performance of various pre-operative variables of the subjects, for the prediction of RVHF. The study equally employs both single and hybrid paradigms for the prediction of RVHF using different pre-operative variables. The visualized and quantitative performance of the direct and inverse modelling approach indicates the robust prediction performance of the hybrid paradigms over the single techniques in both the calibration and validation steps. Whereby, the quantitative performance of the hybrid techniques, based on the Nash;Sutcliffe coefficient (NC) metric, depicts its superiority over the single paradigms by up to 58.7%/75.5% and 80.3%/51% for the calibration/validation phases in the direct and inverse modelling approaches, respectively. Moreover, to the best knowledge of the authors, this is the first study to report the implementation of direct and inverse modelling on clinical data. The findings of the current study indicates the possibility of applying these novel hybridised paradigms for the prediction of RVHF using pre-operative variables.
A Generative Angular Model of Protein Structure Evolution
(Oxford University Press, 2017-08-01) Golden, Michael; García Portugués, Eduardo; Sorensen, Michael; Mardia, Kanti V.; Hamelryck, Thomas; Hein, Jontun; Ministerio de Economía y Competitividad (España)
Recently described stochastic models of protein evolution have demonstrated that the inclusion of structural information in addition to amino acid sequences leads to a more reliable estimation of evolutionary parameters. We present a generative, evolutionary model of protein structure and sequence that is valid on a local length scale. The model concerns the local dependencies between sequence and structure evolution in a pair of homologous proteins. The evolutionary trajectory between the two structures in the protein pair is treated as a random walk in dihedral angle space, which is modeled using a novel angular diffusion process on the two-dimensional torus. Coupling sequence and structure evolution in our model allows for modeling both "smooth" conformational changes and "catastrophic" conformational jumps, conditioned on the amino acid changes. The model has interpretable parameters and is comparatively more realistic than previous stochastic models, providing new insights into the relationship between sequence and structure evolution. For example, using the trained model we were able to identify an apparent sequence-structure evolutionary motif present in a large number of homologous protein pairs. The generative nature of our model enables us to evaluate its validity and its ability to simulate aspects of protein evolution conditioned on an amino acid sequence, a related amino acid sequence, a related structure or any combination thereof.

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