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Given the high prevalence of imported diseases in immigrant populations, it has postulated the need to establish screening programs that allow their early diagnosis and treatment. We present a mathematical model based on machine learning methodologies to contribute to the design of screening programs in this population. We conducted a retrospective cross-sectional screening program of imported diseases in all immigrant patients who attended the Tropical Medicine Unit between January 2009 and December 2016. We designed a mathematical model based on machine learning methodologies to establish the set of most discriminatory prognostic variables to predict the onset of the: HIV infection, malaria, chronic hepatitis B and C, schistosomiasis, and Chagas in immigrant population. We analyzed 759 patients. HIV was predicted with an accuracy of 84.9% and the number of screenings to detect the first HIV-infected person was 26, as in the case of Chagas disease (with a predictive accuracy of 92.9%). For the other diseases the averages were 12 screenings to detect the first case of chronic hepatitis B (85.4%), or schistosomiasis (86.9%), 23 for hepatitis C (85.6%) or malaria (93.3%), and eight for syphilis (79.4%) and strongyloidiasis (88.4%). The use of machine learning methodologies allowed the prediction of the expected disease burden and made it possible to pinpoint with greater precision those immigrants who are likely to benefit from screening programs, thus contributing effectively to their development and design.

The complexity of orphan diseases, which are those that do not have an effective treatment, together with the high dimensionality of the genetic data used for their analysis and the high degree of uncertainty in the understanding of the mechanisms and genetic pathways which are involved in their development, motivate the use of advanced techniques of artificial intelligence and in-depth knowledge of molecular biology, which is crucial in order to find plausible solutions in drug design, including drug repositioning. Particularly, we show that the use of robust deep sampling methodologies of the altered genetics serves to obtain meaningful results and dramatically decreases the cost of research and development in drug design, influencing very positively the use of precision medicine and the outcomes in patients. The target-centric approach and the use of strong prior hypotheses that are not matched against reality (disease genetic data) are undoubtedly the cause of the high number of drug design failures and attrition rates. Sampling and prediction under uncertain conditions cannot be avoided in the development of precision medicine.

In this paper, we present a modified version of the Pereyra–Rosen algorithm to the solution of ill-conditioned linear and nonlinear inverse problems arising in gravimetry. We perform a sensitivity analysis of the solution to the two main tuning parameters of this algorithm, comparing its solution with LSQR and the truncated SVD. First, we show the application to a general-purpose linear system whose system matrix has been created by conditional simulation and whose solution is known and to a synthetic 1D gravimetric problem for two different geological set-ups (smooth Gaussian and blocky geophysical anomalies) in the noise-free and noisy cases. The Pereyra–Rosen algorithm provides very good results using a reduced number of column vectors of the system matrix. We finally show the fast inversion of a real gravity profile in the Atacama Desert (north Chile). The algorithm is well suited for the solution of large-scale ill-conditioned linear problems as the ones encountered in the fine discretization of continuous linear inverse problems in several dimensions and in the iterative linearization of nonlinear inverse problems in several fields of geosciences.

To understand the transcriptomic response to SARS-CoV-2 infection, is of the utmost importance to design diagnostic tools predicting the severity of the infection.We have performed a deep sampling analysis of the viral transcriptomic data oriented towards drug repositioning. Using different samplers, the basic principle of this methodology the biological invariance, which means that the pathways altered by the disease, should be independent on the algorithm used to unravel them.The transcriptomic analysis of the altered pathways, reveals a distinctive inflammatory response and potential side effects of infection. The virus replication causes, in some cases, acute respiratory distress syndrome in the lungs, and affects other organs such as heart, brain, and kidneys. Therefore, the repositioned drugs to fight COVID-19 should, not only target the interferon signalling pathway and the control of the inflammation, but also the altered genetic pathways related to the side effects of infection. We also show via Principal Component Analysis that the transcriptome signatures are different from influenza and RSV. The gene COL1A1, which controls collagen production, seems to play a key/vital role in the regulation of the immune system. Additionally, other small-scale signature genes appear to be involved in the development of other COVID-19 comorbidities.Transcriptome-based drug repositioning offers possible fast-track antiviral therapy for COVID-19 patients. It calls for additional clinical studies using FDA approved drugs for patients with increased susceptibility to infection and with serious medical complications.

Aims It is known that matrix metalloproteinase (MMP)-11 has a role in tumour development and progression, and also that immune cells can influence cancer cells to increase their proliferative and invasive properties. The aim of the present study was to propose the evaluation of MMP11 expression by intratumoral mononuclear inflammatory cells (MICs) as a useful biological marker for breast cancer prognosis. Methods and results This study comprised 246 women with invasive breast carcinoma, and a long follow-up period. Patients were stratified with regard to nodal status and to the development of metastatic disease. The median follow-up period in patients without metastasis was 146 months and in patients with metastatic disease 31 months. MMP11 was determined by immunohistochemistry. For relapse-free survival (RFS) and overall survival (OS) analysis we used the Cox's univariate method. Cox's regression model was used to examine the interactions between different prognostic factors in a multivariate analysis. Conclusions Our results showed that MMP11 expression by stromal cells was significantly associated with prognosis. MMP11 expression by cancer-associated fibroblasts (CAFs) was associated with both shortened RFS and OS, but MMP11 expression by MICs showed a stronger association with both shortened RFS and OS, therefore being the most potent and independent factor to predict RFS and OS.

We have evaluated the uncertainty analysis of the 3D electrical tomography inverse problem using model reduction via singular-value decomposition and performed sampling of the nonlinear equivalence region via an explorative member of the particle swarm optimization (PSO) family. The procedure begins with the local inversion of the observed data to find a good resistivity model located in the nonlinear equivalence region. Then, the dimensionality is reduced via the spectral decomposition of the 3D geophysical model. Finally, the exploration of the uncertainty space is performed via an exploratory version of PSO (RR-PSO). This sampling methodology does not prejudge where the initial model comes from as long as this model has a geologic meaning. The 3D subsurface conductivity distribution is arranged as a 2D matrix by ordering the conductivity values contained in a given earth section as a column array and stacking parallel sections as columns of the matrix. There are three basic modes of ordering: mode 1 and mode 2, by using vertical sections in two perpendicular directions, and mode 3, by using horizontal sections. The spectral decomposition is then performed using these three 2D modes. Using this approach, it is possible to sample the uncertainty space of the 3D electrical resistivity inverse problem very efficiently. This methodology is intrinsically parallelizable and could be run for different initial models simultaneously. We found the application to a synthetic data set that is well-known in the literature related to this subject, obtaining a set of surviving geophysical models located in the nonlinear equivalence region that can be used to approximate numerically the posterior distribution of the geophysical model parameters (frequentist approach). Based on these models, it is possible to perform the probabilistic segmentation of the inverse solution found, meanwhile answering geophysical questions with its corresponding uncertainty assessment. This methodology has a general character could be applied to any other 3D nonlinear inverse problems by implementing their corresponding forward model.

Linear and nonlinear discrete inverse problems are by genesis ill-posed, that is, several solutions exist compatible with the prior information and fit the observed data within the same error bounds (equivalent models). This fact and the presence of noise and modelling errors cause their solution to be uncertain. Uncertainty analysis and solution appraisal is a crucial step in inverse modelling, and it is typically accomplished via random sampling methodologies and Bayesian approaches (provided a good knowledge of the probabilistic distributions of the model and data spaces is at disposal) or via exploratory global optimization algorithms that perform an approximate posterior sampling of the nonlinear equivalence region. This paper demonstrates that a simple method to sample the uncertainty space in linear and nonlinear problems consists in performing different inversions of random data bags. This novel methodology is called data kit inversion and it is inspired in the bootstrapping technique in statistical inference, switching in this case to the existence of other equivalent models, instead of using in estimating the properties of a given estimator. We present its application to different synthetic linear and nonlinear inverse problems and also to the inversion of a real dataset of gravimetric inversion in the Atacama Desert and also to 1D seismic and AVO inversion. We show that using partial information (random data bags) it is possible to sample different models of the equivalence region (uncertainty space) in order to explore the existence of different plausible geophysical scenarios according to the geophysical model that it is adopted. This algorithm has a general character and can be used in different fields of science and technology .

Discrimination of case-control status based on gene expression differences has potential to identify novel pathways relevant to neurodegenerative diseases including Parkinson’s disease (PD). In this paper we applied two different novel algorithms to predict dysregulated pathways of gene expression across several different regions of the brain in PD and controls. The Fisher’s ratio sampler uses the Fisher’s ratio of the most discriminatory genes as prior probability distribution to sample the genetic networks and their likelihood (accuracy) was established via Leave-One-Out-Cross Validation (LOOCV). The holdout sampler finds the minimum-scale signatures corresponding to different random holdouts, establishing their likelihood using the validation dataset in each holdout. Phenotype prediction problems have by genesis a very high underdetermined character. We used both approaches to sample different lists of genes that optimally discriminate PD from controls and subsequently used gene ontology to identify pathways affected by disease. Both algorithms identified common pathways of Insulin signaling, FOXA1 Transcription Factor Network, HIF-1 Signaling, p53 Signaling and Chromatin Regulation/Acetylation. This analysis provides new therapeutic targets to treat PD.

BackgroundChemotherapy-induced peripheral neuropathy (CIPN) is a common toxicity of taxanes for which there is no effective intervention. Genomic CIPN risk determination has yielded promising, but inconsistent results. The present study assessed the utility of a collective SNP cluster identified using novel analytic to describe taxane-associated CIPN risk.MethodsWe analyzed GWAS data derived from ECOG-5103, first identifying SNPs that were most strongly associated with CIPN using Fisher’s ratio. We then ranked ordered those SNPs which discriminated CIPN-positive from CIPN-negative phenotypes based on their discriminatory power and developed the cluster of SNPs which provided the highest predictive accuracy using leave-one-out cross validation (LOOCV).ResultsUsing GWAS aggregate data, we identified a 267 SNP cluster which was associated with a CIPN+ phenotype with an accuracy of 96.1%. ConclusionsIdentification of a 267 SNP cluster could accurately predict CIPN risk. Validation using an independent patient cohort should be performed.

International audience; In this paper GravPSO2D, a Matlab tool for two-dimensional gravity inversion in sedimentary basins using the Particle Swarm Optimization (PSO) algorithm, is presented. The package consists of a collection of functions and scripts that cover the main three parts of the process: (1) the model definition based on the observations, (2) the inversion itself, where the PSO is employed, and (3) the results processing, including best model estimation, uncertainty analysis and plots generation. GravPSO2D is freely available, and represents an effort for providing the scientific community with the first tool based on the PSO algorithm in order perform the inversion and the uncertainty assessment of the sedimentary basin gravity inversion problem, taking into account the gravity regional trend estimation, and vertically and horizontally density contrast variations. Synthetic and real examples are provided in order to show the software capabilities.

Artificial intelligence methods may help in unveiling information that is hidden in high-dimensional oncological data. Flow cytometry studies of haematological malignancies provide quantitative data with the potential to be used for the construction of response biomarkers. Many computational methods from the bioinformatics toolbox can be applied to these data, but they have not been exploited in their full potential in leukaemias, specifically for the case of childhood B-cell Acute Lymphoblastic Leukaemia. In this paper, we analysed flow cytometry data that were obtained at diagnosis from 56 paediatric B-cell Acute Lymphoblastic Leukaemia patients from two local institutions. Our aim was to assess the prognostic potential of immunophenotypical marker expression intensity. We constructed classifiers that are based on the Fisher&rsquo, s Ratio to quantify differences between patients with relapsing and non-relapsing disease. We also correlated this with genetic information. The main result that arises from the data was the association between subexpression of marker CD38 and the probability of relapse.

Nonlinear high-dimensional optimization problems are generally ill-posed and ill-conditioned, with different sets of models located in one or different disconnected valleys of the cost function landscape with similar values. This situation generates uncertainty in the identification of the optimum model parameters that should be translated to the decision that has to be made. Therefore, the analysis of the uncertainty space of this type of problems is required in order to adopt robust decisions. This is done by sampling the cost function topography within the intricate solution set valleys that belong to the nonlinear equivalence region and validating the existence of different scenarios. This is also the case of the portfolio optimization problem, which admits multiple solutions depending on the expected return-risk ratio. As the popular wisdom says, you cannot have the butter and the money of having sold it. This mathematical situation is known as a Pareto front, which aims to show the boundary of the nonlinear equivalence region of the corresponding decision problem. In this chapter, we introduce the concept of uncertainty in high-dimensional problems, proposing the particle swarm optimization family as a parameter free-tuning global algorithm, capable of sampling the nonlinear equivalent region in parallel with the optimization. For that purpose, these algorithms should be exploratory. This feature is related to the automatic tuning of the PSO parameters in the neighborhood of the stochastic second-order stability region of the particle trajectories. These algorithms are faster than Monte Carlo methods. The chapter concludes with the application of PSO to the portfolio optimization problem of the IBEX-35, which is the main stock market index of the Bolsa de Madrid. In this case, the cost function is constructed in a way that the investors seek to maximize the portfolio’s expected return subjected to a given risk. The optimization of the portfolio composition follows a previous selection of the stocks.

The prediction of the dynamics of the COVID-19 outbreak and the corresponding needs of the health care system (COVID-19 patients’ admissions, the number of critically ill patients, need for intensive care units, etc.) is based on the combination of a limited growth model (Verhulst model) and a short-term predictive model that allows predictions to be made for the following day. In both cases, the uncertainty analysis of the prediction is performed, i.e., the set of equivalent models that adjust the historical data with the same accuracy. This set of models provides the posterior distribution of the parameters of the predictive model that adjusts the historical series. It can be extrapolated to the same analyzed time series (e.g., the number of infected individuals per day) or to another time series of interest to which it is correlated and used, e.g., to predict the number of patients admitted to urgent care units, the number of critically ill patients, or the total number of admissions, which are directly related to health needs. These models can be regionalized, that is, the predictions can be made at the local level if data are disaggregated. We show that the Verhulst and the Gompertz models provide similar results and can be also used to monitor and predict new outbreaks. However, the Verhulst model seems to be easier to interpret and to use.

Water flow in the subsoil and pumping tests generate electrical currents measurable at the ground surface and terms spontaneous potential (SP) anomalies that are well correlated with the geometry of the water table. In this paper, we present the application of the Particle Swarm Optimization (PSO) family to estimate the water table elevation from SP measurements at the ground surface. The search is performed in a reduced space that is generated via Principal Component analysis performed in a set of models that are randomly generated taking into account the regularity of the SP anomaly. The PSO members used in this research perform a sampling of the water table coordinates in the PCA space, the electro-kinetic coupling constant and the reference hydraulic head, in a reduced dimensional space. Besides, based on the samples gathered on the low misfit area we are able to compute a fast approximation of the posterior distribution of the SP model parameters with very humble computational resources.

Inverse problems are special kind of optimization problems where observed data (noise included) enters into the optimization cost function, having an important impact in the solution (geophysical model parameters) that has been found. Therefore, the most important step in inversion is uncertainty analysis and model appraisal. We revise the uncertainty concept in linear and nonlinear inverse problems through linear algebra and optimization concepts, relating them to the well-known Bayesian Approaches and maximum likelihood methods. We also provide very practical insights to deal with uncertainty in high-dimensional nonlinear inverse problems through the use of model reduction and forward model approximations. This approach is also valid for classification problems where the forward model is unknown and it has to be constructed. The aim of this paper is not to provide new original results but showing the researchers why uncertainty assessment is a compulsory step in inversion and how to deal with it in a simpler way.