Computational Models for Transplant Biomarker Discovery.

Translational medicine offers a rich promise for improved diagnostics and drug discovery for biomedical research in the field of transplantation, where continued unmet diagnostic and therapeutic needs persist. Current advent of genomics and proteomics profiling called "omics" provides new resources to develop novel biomarkers for clinical routine. Establishing such a marker system heavily depends on appropriate applications of computational algorithms and software, which are basically based on mathematical theories and models. Understanding these theories would help to apply appropriate algorithms to ensure biomarker systems successful. Here, we review the key advances in theories and mathematical models relevant to transplant biomarker developments. Advantages and limitations inherent inside these models are discussed. The principles of key -computational approaches for selecting efficiently the best subset of biomarkers from high--dimensional omics data are highlighted. Prediction models are also introduced, and the integration of multi-microarray data is also discussed. Appreciating these key advances would help to accelerate the development of clinically reliable biomarker systems

Examining Connections between Gendered Dimensions of Inequality and Deforestation in Nepal

The United Nations recognizes empowering women as a key component of achieving numerous development-related goals. Qualitative studies suggest that communities where men and women have equal levels of agency over resource allocation and land tenure sometimes experience decreases in forest degradation and deforestation, all else being equal. However, these patterns are spatially heterogeneous, as are patterns of gender inequality in terms of land tenure and agency. This paper uses data from the Demographic and Health Surveys (DHS) to quantify the relationship between gender inequality and ecosystem degradation using three linear regression models, Empirical Bayesian Kriging, and mapping the intersections between gender inequality and deforestation. Results from LASSO, Ordinary Least Squares, and Stepwise regression models show that there is no linear relationship between gender inequality and deforestation. Additionally, the distributions of gender inequality as it pertains to land tenure and deforestation are highly heterogeneous over space, indicating potential sociocultural and sociodemographic factors not captured in my data. Further work should focus on identifying ways to incorporate complex gender dynamics into environmental planning at multiple levels of forest governance

On Nonregularized Estimation of Psychological Networks.

An important goal for psychological science is developing methods to characterize relationships between variables. Customary approaches use structural equation models to connect latent factors to a number of observed measurements, or test causal hypotheses between observed variables. More recently, regularized partial correlation networks have been proposed as an alternative approach for characterizing relationships among variables through off-diagonal elements in the precision matrix. While the graphical Lasso (glasso) has emerged as the default network estimation method, it was optimized in fields outside of psychology with very different needs, such as high dimensional data where the number of variables (p) exceeds the number of observations (n). In this article, we describe the glasso method in the context of the fields where it was developed, and then we demonstrate that the advantages of regularization diminish in settings where psychological networks are often fitted ( p≪n ). We first show that improved properties of the precision matrix, such as eigenvalue estimation, and predictive accuracy with cross-validation are not always appreciable. We then introduce nonregularized methods based on multiple regression and a nonparametric bootstrap strategy, after which we characterize performance with extensive simulations. Our results demonstrate that the nonregularized methods can be used to reduce the false-positive rate, compared to glasso, and they appear to provide consistent performance across sparsity levels, sample composition (p/n), and partial correlation size. We end by reviewing recent findings in the statistics literature that suggest alternative methods often have superior performance than glasso, as well as suggesting areas for future research in psychology. The nonregularized methods have been implemented in the R package GGMnonreg

A cross sectional analysis of the association between FGF19 tumor expression and serum AFP levels in advanced HCC patients

PURPOSE: HCC is a complicated disease with high mortality rates and limited treatment options. No universal clinical or molecular classification established to inform better treatment options. There has been very limited success in determining a molecular profile that represent valid drivers in HCC patients and thus no targeted agents have obtained marketing approval. However, emerging data suggest the FGF19-pathway as a HCC driver and a potential therapeutic target. This research study aims to investigate whether the HCC prognostic risk factor, serum AFP, is predictive of FGF19 protein expression as assessed by immunohistochemistry in advanced HCC patients. METHODS: A cross-sectional analysis was performed from baseline data collected in a Phase 1 study conducted at various centers across the US, EU, and Asia. Only advanced HCC patients with adequate liver function were eligible for enrollment. Demographic data, detailed history of HCC, and any prior treatments or surgeries were recorded. Baseline laboratory values and prognostic factors including performance status (ECOG), lab values (i.e. bilirubin, albumin), and the number, size and biomarker status of the tumor(s) were collected. Differences between groups were assessed by t test, or Chi-square test, as appropriate. Multivariate logistic stepwise regression analyses were performed including all parameters with highly significant correlations in the multivariate analysis. RESULTS: Only AFP, metastatic disease, and prior surgery met the criteria to be incorporated into the final model. Results indicated that high AFP had a statistically significant (p-value = .01) positive association (Wald chi-square statistic = 6.601) with positive FGF19 IHC status. The odds ratio for being FGF19 IHC+ was 12.216 among the high AFP subjects as compared to low AFP subjects, and also statistically significant but had a very wide 95% confidence interval (1.811, 82.79). CONCLUSIONS: The results indicated that HCC patients with high serum AFP levels have a twelve fold higher chance of having a positive FGF19 IHC status than those with low AFP levels. Further studies are warranted in order to replicate the data in a larger sample size to understand future clinical implications once treatment options become available for FGF19 IHC positive patients

Pathway-Based Genomics Prediction using Generalized Elastic Net.

We present a novel regularization scheme called The Generalized Elastic Net (GELnet) that incorporates gene pathway information into feature selection. The proposed formulation is applicable to a wide variety of problems in which the interpretation of predictive features using known molecular interactions is desired. The method naturally steers solutions toward sets of mechanistically interlinked genes. Using experiments on synthetic data, we demonstrate that pathway-guided results maintain, and often improve, the accuracy of predictors even in cases where the full gene network is unknown. We apply the method to predict the drug response of breast cancer cell lines. GELnet is able to reveal genetic determinants of sensitivity and resistance for several compounds. In particular, for an EGFR/HER2 inhibitor, it finds a possible trans-differentiation resistance mechanism missed by the corresponding pathway agnostic approach