Using Explainable AI to Understand Bond Excess Returns

Recent empirical evidence indicates that bond excess returns can be predicted using machine learning models. While the predictive power of machine learning models is intriguing, they typically lack transparency. We introduce SHapley Additive exPlanations (SHAP), a state-of-the-art explainable artificial technique, to open the black box of these models. Our analysis identifies the key determinants that drive the predictions of bond excess returns in machine learning models and how these determinants are related to bond excess returns. Thereby, our approach facilitates an in-depth interpretation of the predictions of bond excess returns made by machine learning models

Carcinogen metabolism, cigarette smoking, and breast cancer risk: a Bayes model averaging approach

BACKGROUND: Standard logistic regression with or without stepwise selection has the disadvantage of not incorporating model uncertainty and the dependency of estimates on the underlying model into the final inference. We explore the use of a Bayes Model Averaging approach as an alternative to analyze the influence of genetic variants, environmental effects and their interactions on disease. METHODS: Logistic regression with and without stepwise selection and Bayes Model Averaging were applied to a population-based case-control study exploring the association of genetic variants in tobacco smoke-related carcinogen pathways with breast cancer. RESULTS: Both regression and Bayes Model Averaging highlighted a significant effect of NAT1*10 on breast cancer, while regression analysis also suggested a significant effect for packyears and for the interaction of packyears and NAT2. CONCLUSIONS: Bayes Model Averaging allows incorporation of model uncertainty, helps reduce dimensionality and avoids the problem of multiple comparisons. It can be used to incorporate biological information, such as pathway data, into the analysis. As with all Bayesian analysis methods, careful consideration must be given to prior specification

Haplotype-sharing analysis using Mantel statistics for combined genetic effects

We applied a new approach based on Mantel statistics to analyze the Genetic Analysis Workshop 14 simulated data with prior knowledge of the answers. The method was developed in order to improve the power of a haplotype sharing analysis for gene mapping in complex disease. The new statistic correlates genetic similarity and phenotypic similarity across pairs of haplotypes from case-control studies. The genetic similarity is measured as the shared length between haplotype pairs around a genetic marker. The phenotypic similarity is measured as the mean corrected cross-product based on the respective phenotypes. Cases with phenotype P1 and unrelated controls were drawn from the population of Danacaa. Power to detect main effects was compared to the X(2)-test for association based on 3-marker haplotypes and a global permutation test for haplotype association to test for main effects. Power to detect gene × gene interaction was compared to unconditional logistic regression. The results suggest that the Mantel statistics might be more powerful than alternative tests

Impact of genotyping errors on the type I error rate and the power of haplotype-based association methods

<p>Abstract</p> <p>Background</p> <p>We investigated the influence of genotyping errors on the type I error rate and empirical power of two haplotype based association methods applied to candidate regions. We compared the performance of the Mantel Statistic Using Haplotype Sharing and the haplotype frequency based score test with that of the Armitage trend test.</p> <p>Our study is based on 1000 replication of simulated case-control data settings with 500 cases and 500 controls, respectively. One of the examined markers was set to be the disease locus with a simulated odds ratio of 3. Differential and non-differential genotyping errors were introduced following a misclassification model with varying mean error rates per locus in the range of 0.2% to 15.6%.</p> <p>Results</p> <p>We found that the type I error rate of all three test statistics hold the nominal significance level in the presence of nondifferential genotyping errors and low error rates. For high and differential error rates, the type I error rate of all three test statistics was inflated, even when genetic markers not in Hardy-Weinberg Equilibrium were removed. The empirical power of all three association test statistics remained high at around 89% to 94% when genotyping error rates were low, but decreased to 48% to 80% for high and nondifferential genotyping error rates.</p> <p>Conclusion</p> <p>Currently realistic genotyping error rates for candidate gene analysis (mean error rate per locus of 0.2%) pose no significant problem for the type I error rate as well as the power of all three investigated test statistics.</p

Mimicking of the histidine brace structural motif in molecular copper(I) compounds

L-Nτ-methylhistidine methyl ester, MeHisOMe, has been employed as a potential ligand to mimic the histidine brace-type coordination of copper ions in enzymes such as the particulate methane monoxygenase or lytic polysaccharide monooxygenases. MeHisOMe was prepared by double-methylation of histidine methyl ester. Subsequently, its complexation by diphosphine copper(I) precursors [Cu(P^P)(MeCN)2]BF4 was tested, which led to the complexes [Cu(P^P)(MeHisOMe)]BF4 (P^P=dpePhos: 1, P^P=XantPhos: 2, P^P=dppf: 3). 1–3 were fully characterized, also by single crystal X-ray analysis, thus providing first structural data for copper complexes with a synthetic, authentic histidine brace. The complexes proved inert in contact with dioxygen. To improve the biomimetic character attempts were made to formally replace the diphosphine ligands by bis(pyrazolyl)methanes, Bpm. Correspondingly, [BpmCu(NCMe)x]BF4 precursors were synthesized, with different substituents at the 3-positions of the pyrazolyl (i. e. Bpm=di(3-(phenyl)-1H-pyrazol-1-yl)diphenylmethane, di(3-(mesityl)-1H-pyrazol-1-yl)methane and di(3-(tert-butyl)-1H-pyrazol-1-yl)diphenylmethane). Addition of MeHisOMe to these complexes led to products that were so sensitive towards oxidation by the environment that they eluded isolation. One experiment provided blue crystals as a product of such a reaction. They belonged to a salt with a complex cation consisting of a Cu(μ-OH)2Cu core ligated by two MeHisOMe ligands, which dimerises in the solid state to give [Cu4(OH)4(MeHisOMe)4]4+.Peer Reviewe

Parent participation in school governance : a legal analysis of experiences in South Africa and Kentucky

This comparative study on the educational governance systems of South Africa and the Commonwealth of Kentucky examines legal evidence from judicial decisions and administrative law to understand similarities in how school-based governance structures have been developed. We found that although school-level governance structures may provide greater opportunities for community and parental participation, each engenders a number of legal problems that compromise the decentralization of democracy to the school level. Recommendations for policymakers and practitioners are offered that may achieve this goal.https://rowman.com/page/JSPRgv201

TRIM32-dependent transcription in adult neural progenitor cells regulates neuronal differentiation

In the adult mammalian brain, neural stem cells in the subventricular zone continuously generate new neurons for the olfactory bulb. Cell fate commitment in these adult neural stem cells is regulated by cell fate-determining proteins. Here, we show that the cell fate-determinant TRIM32 is upregulated during differentiation of adult neural stem cells into olfactory bulb neurons. We further demonstrate that TRIM32 is necessary for the correct induction of neuronal differentiation in these cells. In the absence of TRIM32, neuroblasts differentiate slower and show gene expression profiles that are characteristic of immature cells. Interestingly, TRIM32 deficiency induces more neural progenitor cell proliferation and less cell death. Both effects accumulate in an overproduction of adult-generated olfactory bulb neurons of TRIM32 knockout mice. These results highlight the function of the cell fate-determinant TRIM32 for a balanced activity of the adult neurogenesis process

Representation of genetic association via attributable familial relative risks in order to identify polymorphisms functionally relevant to rheumatoid arthritis

The results from association studies are usually summarized by a measure of evidence of association (frequentist or Bayesian probability values) that does not directly reflect the impact of the detected signals on familial aggregation. This article investigates the possible advantage of a two-dimensional representation of genetic association in order to identify polymorphisms relevant to disease: a measure of evidence of association (the Bayes factor, BF) combined with the estimated contribution to familiality (the attributable sibling relative risk, λs). Simulation and data from the North American Rheumatoid Consortium (NARAC) were used to assess the possible benefit under several scenarios. Simulation indicated that the allele frequencies to reach the maximum BF and the maximum attributable λs diverged as the size of the genetic effect increased. The representation of BF versus attributable λs for selected regions of NARAC data revealed that SNPs involved in replicated associations clearly departed from the bulk of SNPs in these regions. In the 12 investigated regions, and particularly in the low-recombination major histocompatibility region, the ranking of SNPs according to BF differed from the ranking of SNPs according to attributable λs. The present results should be generalized using more extensive simulations and additional real data, but they suggest that a characterization of genetic association by both BF and attributable λs may result in an improved ranking of variants for further biological analyses