Intrinsically Multivariate Predictive Genes

Canalizing genes possess such broad regulatory power, and their action sweeps across a such a wide swath of processes that the full set of affected genes are not highly correlated under normal conditions. When not active, the controlling gene will not be predictable to any significant degree by its subject genes, either alone or in groups, since their behavior will be highly varied relative to the inactive controlling gene. When the controlling gene is active, its behavior is not well predicted by any one of its targets, but can be very well predicted by groups of genes under its control. To investigate this question, we introduce in this paper the concept of intrinsically multivariate predictive (IMP) genes, and present a mathematical study of IMP in the context of binary genes with respect to the coefficient of determination (CoD), which measures the predictive power of a set of genes with respect to a target gene. A set of predictor genes is said to be IMP for a target gene if all properly contained subsets of the predictor set are bad predictors of the target but the full predictor set predicts the target with great accuracy. We show that logic of prediction, predictive power, covariance between predictors, and the entropy of the joint probability distribution of the predictors jointly affect the appearance of IMP genes. In particular, we show that high-predictive power, small covariance among predictors, a large entropy of the joint probability distribution of predictors, and certain logics, such as XOR in the 2-predictor case, are factors that favor the appearance of IMP. The IMP concept is applied to characterize the behavior of the gene DUSP1, which exhibits control over a central, process-integrating signaling pathway, thereby providing preliminary evidence that IMP can be used as a criterion for discovery of canalizing genes.

Signal propagation in Bayesian networks and its relationship with intrinsically multivariate predictive variables

A set of predictor variables is said to be intrinsically multivariate predictive (IMP) for a target variable if all properly contained subsets of the predictor set are poor predictors of the. target but the full set predicts the target with great accuracy. In a previous article, the main properties of IMP Boolean variables have been analytically described, including the introduction of the IMP score, a metric based on the coefficient of determination (CoD) as a measure of predictiveness with respect to the target variable. It was shown that the IMP score depends on four main properties: logic of connection, predictive power, covariance between predictors and marginal predictor probabilities (biases). This paper extends that work to a broader context, in an attempt to characterize properties of discrete Bayesian networks that contribute to the presence of variables (network nodes) with high IMP scores. We have found that there is a relationship between the IMP score of a node and its territory size, i.e., its position along a pathway with one source: nodes far from the source display larger IMP scores than those closer to the source, and longer pathways display larger maximum IMP scores. This appears to be a consequence of the fact that nodes with small territory have larger probability of having highly covariate predictors, which leads to smaller IMP scores. In addition, a larger number of XOR and NXOR predictive logic relationships has positive influence over the maximum IMP score found in the pathway. This work presents analytical results based on a simple structure network and an analysis involving random networks constructed by computational simulations. Finally, results from a real Bayesian network application are provided. (C) 2012 Elsevier Inc. All rights reserved.

Multivariate prediction of clarified butter composition using Raman spectroscopy

Raman spectroscopy has been used to predict the abundance of the FA in clarified butterfat that was obtained from dairy cows fed a range of levels of rapeseed oil in their diet. Partial least squares regression of the Raman spectra against FA compositions obtained by GC showed good prediction for the five major (abundance >5%) FA with R-2=0.74-0.92 and a root mean SE of prediction (RMSEP) that was 5-7% of the mean. In general, the prediction accuracy fell with decreasing abundance in the sample, but the RMSEP was 1.25%. The Raman method has the best prediction ability for unsaturated FA (R-2=0.85-0.92), and in particular trans unsaturated FA (best-predicted FA was 18:1 tDelta9). This enhancement was attributed to the isolation of the unsaturated modes from the saturated modes and the significantly higher spectral response of unsaturated bonds compared with saturated bonds. Raman spectra of the melted butter samples could also be used to predict bulk parameters calculated from standard analyzes, such as iodine value (R-2=0.80) and solid fat content at low temperature (R-2=0.87). For solid fat contents determined at higher temperatures, the prediction ability was significantly reduced (R-2=0.42), and this decrease in performance was attributed to the smaller range of values in solid fat content at the higher temperatures. Finally, although the prediction errors for the abundances of each of the FA in a given sample are much larger with Raman than with full GC analysis, the accuracy is acceptably high for quality control applications. This, combined with the fact that Raman spectra can be obtained with no sample preparation and with 60-s data collection times, means that high-throughput, on-line Raman analysis of butter samples should be possible.

Application of the Multivariate Prediction Method to Time Series

* This work was financially supported by RFBR-04-01-00858.

Variable definitions: implications for the prediction of pulmonary complications after adult cardiac surgery

Aim: The aim of this paper was to review the implications that variable definitions have for the prediction of post-operative pulmonary complications after cardiac surgery.

Method: A review of the literature from 1980 to 2002. Selected studies demonstrated an original attempt to examine multivariate associations between pre, intra or post-operative antecedents and pulmonary outcomes in patients undergoing coronary artery bypass grafting (CABG). Reports that described the validation of established clinical prediction rules, testing interventions or research conducted in non-human cohorts were excluded from this review.

Results: Consistently, variable factor and outcome definitions are combined for the development of multivariate prediction models that subsequently have limited clinical value. Despite being prevalent there are very few attempts to examine post-operative pulmonary complications (PPC) as endpoints in isolation. The trajectory of pulmonary dysfunction that precedes complications in the post-operative context is not clear. As such there is little knowledge of post-operative antecedents to PPC that are invariably excluded from model development.

Conclusion: Multivariate clinical prediction rules that incorporate antecedent patient and process factors from the continuum of cardiovascular care for specific pulmonary outcomes are recommended. Models such as these would be useful for practice, policy and quality improvement.

Expressed emotion as predictor of relapse in patients with comorbid psychoses and substance use disorder

Objective: Expressed emotion (EE) and substance use disorder predict relapse in psychosis, but there is little research on EE in comorbid samples. The current study addressed this issue. Method: Sixty inpatients with a DSM-IV psychosis and substance use disorder were recruited and underwent diagnostic and substance use assessment. Key relatives were administered the Camberwell Family Interview. Results: Patients were assessed on the initial symptoms and recent substance use, and 58 completed the assessment over the following 9 months. High EE was observed in 62% of households. Expressed emotion was the strongest predictor of relapse during follow up and its predictive effect remained in participants with early psychosis. A multivariate prediction of a shorter time to relapse entered EE, substance use during follow up Q1 and (surprisingly) an absence of childhood attention deficit hyperactivity disorder. Conclusions: Since high EE is a common and important risk factor for people with comorbid psychosis and substance misuse, approaches to address it should be considered by treating clinicians.

Factores asociados a complicaciones post quirúrgicas en pacientes adultos con obstrucción intestinal mecánica en Bogotá, Colombia, período 2008 - 2014

Introducción: La obstrucción intestinal es una patología de alta prevalencia e impacto en los servicios de cirugía general a nivel mundial. El manejo de esta entidad puede ser médico o quirúrgico. Cuando se requiere intervención quirúrgica, se busca evitar el desarrollo de isquemia intestinal y resecciones intestinales; durante el postoperatorio, pueden existir complicaciones. El objetivo de este estudio es identificar los factores asociados al desarrollo de complicaciones post operatorias en un grupo de pacientes con obstrucción intestinal mecánica llevados a manejo quirúrgico. Metodología: Estudio analítico tipo casos y controles en un grupo de pacientes con diagnóstico de obstrucción intestinal mecánica llevados a manejo quirúrgico de su patología. Los casos corresponden a los pacientes con complicaciones postoperatorias y los controles aquellos que no presentaron complicaciones. Se identificaron factores asociados a complicación post operatoria mediante modelos estadísticos bivariados y multivariados de regresión logística para factores como edad, sexo, antecedente quirúrgico, presentación clínica, paraclínica y diagnóstico postoperatorio de malignidad, entre otras. Resultados: Se identificaron un total de 138 pacientes (54 casos y 129 controles). Los rangos de edad entre 55-66 años y mayor de 66 años fueron asociados con complicaciones postoperatorias (OR 3,87 IC95% 1,58-9,50 y OR 3,62 IC95% 1,45-9,08 respectivamente). El déficit de base inferior a 5 mEq/litro se relaciona con complicaciones postoperatorias (OR 2,64 IC95% 1.33-5,25) Otras pruebas de laboratorio, características radiológicas, hallazgos de malignidad en el postoperatorio y la evolución de los pacientes no fueron asociados con complicaciones. Conclusiones: Las disminución de las complicaciones durante el manejo quirúrgico de obstrucción intestinal mecánica continúa siendo un reto para la cirugía general. Factores no modificables como edad avanzada y modificables como el equilibrio ácido base deben ser tenidos en cuenta dada su correlación en el desarrollo de complicaciones postoperatorias.

Factores asociados a tratamiento quirúrgico en pacientes con obstrucción intestinal por adherencias

El manejo de la obstrucción intestinal por adherencias es un reto para cualquier especialista en Cirugía debido a que existe controversia sobre el alcance del manejo médico y el momento adecuado para llevar el paciente a cirugía para la resolución del cuadro clínico. En el presente trabajo se pretende, identificar los factores asociados a tratamiento quirúrgico en pacientes con obstrucción intestinal por adherencias. Metodología: Se realizó un estudio de casos y controles en una relación de 1:1, con una recolección de muestra estadística de 48 pacientes en cada grupo, entre mayo 2012 y mayo 2014 en el Hospital Universitario Mayor Mederi y en Barrios Unidos. Se consideraron casos los pacientes intervenidos quirúrgicamente por obstrucción intestinal por bridas y controles los pacientes manejados con tratamiento médico. Se evaluaron factores como edad, antecedentes personales patológicos y quirúrgicos, tiempo de evolución del cuadro clínico, hallazgos en imágenes y laboratorio entre otros. Resultados: Se recolectaron un total de 158 pacientes, (78 casos, 80 controles). Ambas poblaciones fueron comparables (p=0.13). Los factores asociados a tratamiento quirúrgico estadísticamente significativos fueron género masculino, presencia de fiebre al ingreso, el hallazgo de engrosamiento de la pared intestinal y de obstrucción de asa cerrada en imágenes diagnósticas (p<0,05). Discusión: Los principales factores asociados para que un paciente con obstrucción intestinal por bridas requiera de manejo quirúrgico son consistentes con literatura. Se requiere la socialización de los resultados para disminuir la morbimortalidad de nuestros pacientes.

Prediction of breeding values for average fruit weight in mango using a multivariate individual mixed model

Mango is an important horticultural fruit crop and breeding is a key strategy to improve ongoing sustainability. Knowledge of breeding values of potential parents is important for maximising progress from breeding. This study successfully employed a mixed linear model methods incorporating a pedigree to predict breeding values for average fruit weight from highly unbalanced data for genotypes planted over three field trials and assessed over several harvest seasons. Average fruit weight was found to be under strong additive genetic control. There was high correlation between hybrids propagated as seedlings and hybrids propagated as scions grafted onto rootstocks. Estimates of additive genetic correlation among trials ranged from 0.69 to 0.88 with correlations among harvest seasons within trials greater than 0.96. These results suggest that progress from selection for broad adaptation can be achieved, particularly as no repeatable environmental factor that could be used to predict G x E could be identified. Predicted breeding values for 35 known cultivars are presented for use in ongoing breeding programs.

High-throughput prediction of eucalypt lignin syringyl/guaiacyl content using multivariate analysis: a comparison between mid-infrared, near-infrared, and Raman spectroscopies for model development

BACKGROUND: In order to rapidly and efficiently screen potential biofuel feedstock candidates for quintessential traits, robust high-throughput analytical techniques must be developed and honed. The traditional methods of measuring lignin syringyl/guaiacyl (S/G) ratio can be laborious, involve hazardous reagents, and/or be destructive. Vibrational spectroscopy can furnish high-throughput instrumentation without the limitations of the traditional techniques. Spectral data from mid-infrared, near-infrared, and Raman spectroscopies was combined with S/G ratios, obtained using pyrolysis molecular beam mass spectrometry, from 245 different eucalypt and Acacia trees across 17 species. Iterations of spectral processing allowed the assembly of robust predictive models using partial least squares (PLS). RESULTS: The PLS models were rigorously evaluated using three different randomly generated calibration and validation sets for each spectral processing approach. Root mean standard errors of prediction for validation sets were lowest for models comprised of Raman (0.13 to 0.16) and mid-infrared (0.13 to 0.15) spectral data, while near-infrared spectroscopy led to more erroneous predictions (0.18 to 0.21). Correlation coefficients (r) for the validation sets followed a similar pattern: Raman (0.89 to 0.91), mid-infrared (0.87 to 0.91), and near-infrared (0.79 to 0.82). These statistics signify that Raman and mid-infrared spectroscopy led to the most accurate predictions of S/G ratio in a diverse consortium of feedstocks. CONCLUSION: Eucalypts present an attractive option for biofuel and biochemical production. Given the assortment of over 900 different species of Eucalyptus and Corymbia, in addition to various species of Acacia, it is necessary to isolate those possessing ideal biofuel traits. This research has demonstrated the validity of vibrational spectroscopy to efficiently partition different potential biofuel feedstocks according to lignin S/G ratio, significantly reducing experiment and analysis time and expense while providing non-destructive, accurate, global, predictive models encompassing a diverse array of feedstocks.

Multivariate nonlinear ensemble prediction of daily chaotic rainfall with climate inputs

The basic characteristic of a chaotic system is its sensitivity to the infinitesimal changes in its initial conditions. A limit to predictability in chaotic system arises mainly due to this sensitivity and also due to the ineffectiveness of the model to reveal the underlying dynamics of the system. In the present study, an attempt is made to quantify these uncertainties involved and thereby improve the predictability by adopting a multivariate nonlinear ensemble prediction. Daily rainfall data of Malaprabha basin, India for the period 1955-2000 is used for the study. It is found to exhibit a low dimensional chaotic nature with the dimension varying from 5 to 7. A multivariate phase space is generated, considering a climate data set of 16 variables. The chaotic nature of each of these variables is confirmed using false nearest neighbor method. The redundancy, if any, of this atmospheric data set is further removed by employing principal component analysis (PCA) method and thereby reducing it to eight principal components (PCs). This multivariate series (rainfall along with eight PCs) is found to exhibit a low dimensional chaotic nature with dimension 10. Nonlinear prediction employing local approximation method is done using univariate series (rainfall alone) and multivariate series for different combinations of embedding dimensions and delay times. The uncertainty in initial conditions is thus addressed by reconstructing the phase space using different combinations of parameters. The ensembles generated from multivariate predictions are found to be better than those from univariate predictions. The uncertainty in predictions is decreased or in other words predictability is increased by adopting multivariate nonlinear ensemble prediction. The restriction on predictability of a chaotic series can thus be altered by quantifying the uncertainty in the initial conditions and also by including other possible variables, which may influence the system. (C) 2011 Elsevier B.V. All rights reserved.

Slow Release Drug Dissolution Profile Prediction in Pharmaceutical Manufacturing: a Multivariate and Machine Learning Approach

Slow release drugs must be manufactured to meet target specifications with respect to dissolution curve profiles. In this paper we consider the problem of identifying the drivers of dissolution curve variability of a drug from historical manufacturing data. Several data sources are considered: raw material parameters, coating data, loss on drying and pellet size statistics. The methodology employed is to develop predictive models using LASSO, a powerful machine learning algorithm for regression with high-dimensional datasets. LASSO provides sparse solutions facilitating the identification of the most important causes of variability in the drug fabrication process. The proposed methodology is illustrated using manufacturing data for a slow release drug.

Carbon Nuclear Magnetic Resonance Spectroscopic Profiles coupled to Partial Least-Squares Multivariate Regression for Prediction of Several Physicochemical Parameters of Brazilian Commercial Gasoline

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Multivariate calibrations on H-1 NMR profiles for prediction of physicochemical parameters of Brazilian commercial gasoline

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)