An empirical comparison of several recent epistatic interactions detection methods

ABSTRACT Motivation: Many new methods have recently been proposed for detecting epistatic interactions in GWAS data. There is however no in-depth independent comparison of these methods yet. Results: Five recent methods-TEAM, BOOST, SNPHarvester, SNPRuler, and Screen and Clean (SC)-are evaluated here in terms of power, type-1 error rate, scalability, and completeness. In terms of power, TEAM performs best on data with main effect and BOOST performs best on data without main effect. In terms of type-1 error rate, TEAM and BOOST have higher type-1 error rates than SNPRuler and SNPHarvester. SC does not control type-1 error rate well. In terms of scalability, we tested the five methods using a dataset with 100,000 SNPs on a 64-bit Ubuntu system, with Intel (R) Xeon(R) CPU 2.66GHz, 16G memory. TEAM takes ∼36 days to finish and SNPRuler reports heap allocation problems. BOOST scales up to 100,000 SNPs and the cost is much lower than that of TEAM. SC and SNPHarvester are the most scalable. In terms of completeness, we study how frequently the pruning techniques employed by these methods incorrectly prune away the most significant epistatic interactions. We find that, on average, 20% of datasets without main effect and 60% of datasets with main effect are pruned incorrectly by BOOST, SNPRuler, and SNPHarvester

Towards exploratory hypothesis testing and analysis

10.1109/ICDE.2011.5767907Proceedings - International Conference on Data Engineering745-75

Continuous ECG Monitoring Trial for Outpatient – Patient Receptiveness and Signal Accuracy

10.1109/embc.2019.88573682019 41st Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC

Leveraging open data to reconstruct the Singapore Housing Index and other building-level markers of socioeconomic status for health services research

10.1186/s12939-021-01554-8International journal for equity in health20

Population-centric risk prediction modeling for gestational diabetes mellitus: A machine learning approach

AimsThe heterogeneity in Gestational Diabetes Mellitus (GDM) risk factors among different populations impose challenges in developing a generic prediction model. This study evaluates the predictive ability of existing UK NICE guidelines for assessing GDM risk in Singaporean women, and used machine learning to develop a non-invasive predictive model.MethodsData from 909 pregnancies in Singapore’s most deeply phenotyped mother-offspring cohort study, Growing Up in Singapore Towards healthy Outcomes (GUSTO), was used for predictive modeling. We used a CatBoost gradient boosting algorithm, and the Shapley feature attribution framework for model building and interpretation of GDM risk attributes.ResultsUK NICE guidelines showed poor predictability in Singaporean women [AUC:0.60 (95% CI 0.51, 0.70)]. The non-invasive predictive model comprising of 4 non-invasive factors: mean arterial blood pressure in first trimester, age, ethnicity and previous history of GDM, greatly outperformed [AUC:0.82 (95% CI 0.71, 0.93)] the UK NICE guidelines.ConclusionsThe UK NICE guidelines may be insufficient to assess GDM risk in Asian women. Our non-invasive predictive model outperforms the current state-of-the-art machine learning models to predict GDM, is easily accessible and can be an effective approach to minimize the economic burden of universal testing &amp; GDM associated healthcare in Asian populations.</p

First inter-laboratory study of a Supercritical Fluid Chromatography method for the determination of pharmaceutical impurities

International audienceSupercritical Fluid Chromatography (SFC) has known a strong regain of interest for the last 10 years, especially in the field of pharmaceutical analysis. Besides the development and validation of the SFC method in one individual laboratory, it is also important to demonstrate its applicability and transferability to various laboratories around the world. Therefore, an inter-laboratory study was conducted and published for the first time in SFC, to assess method reproducibility, and evaluate whether this chromatographic technique could become a reference method for quality control (QC) laboratories. This study involved 19 participating laboratories from 4 continents and 9 different countries. It included 5 academic groups, 3 demonstration laboratories at analytical instrument companies, 10 pharmaceutical companies and 1 food company. In the initial analysis of the study results, consistencies within- and between-laboratories were deeply examined. In the subsequent analysis, the method reproducibility was estimated taking into account variances in replicates, between-days and between-laboratories. The results obtained were compared with the literature values for liquid chromatography (LC) in the context of impurities determination. Repeatability and reproducibility variances were found to be similar or better than those described for LC methods, and highlighted the adequacy of the SFC method for QC analyses. The results demonstrated the excellent and robust quantitative performance of SFC. Consequently, this complementary technique is recognized on equal merit to other chromatographic techniques

HIV-Infected Children Have Lower Frequencies of CD8+ Mucosal-Associated Invariant T (MAIT) Cells that Correlate with Innate, Th17 and Th22 Cell Subsets

<div><p>Mucosal-associated invariant T cells (MAIT) are innate T cells restricted by major histocompatibility related molecule 1 (MR1) presenting riboflavin metabolite ligands derived from microbes. Specificity to riboflavin metabolites confers MAIT cells a broad array of host-protective activity against gram-negative and -positive bacteria, mycobacteria, and fungal pathogens. MAIT cells are present at low levels in the peripheral blood of neonates and gradually expand to relatively abundant levels during childhood. Despite no anti-viral activity, MAIT cells are depleted early and irreversibly in HIV infected adults. Such loss or impaired expansion of MAIT cells in HIV-positive children may render them more susceptible to common childhood illnesses and opportunistic infections. In this study we evaluated the frequency of MAIT cells in perinatally HIV-infected children, their response to antiretroviral treatment and their associations with HIV clinical status and related innate and adaptive immune cell subsets with potent antibacterial effector functions. We found HIV+ children between ages 3 to 18 years have significantly decreased CD8+ MAIT cell frequencies compared to uninfected healthy children. Remarkably, CD8 MAIT levels gradually increased with antiretroviral therapy, with greater recovery when treatment is initiated at a young age. Moreover, diminished CD8+ MAIT cell frequencies are associated with low CD4:CD8 ratios and elevated sCD14, suggesting a link with HIV disease progression. Last, CD8+ MAIT cell levels tightly correlate with other antibacterial and mucosa-protective immune subsets, namely, neutrophils, innate-like T cells, and Th17 and Th22 cells. Together these findings suggest that low frequencies of MAIT cells in HIV positive children are part of a concerted disruption to the innate and adaptive immune compartments specialized in sensing and responding to pathogenic or commensal bacteria.</p></div