Integrative multi-omics analysis to gain new insights into COVID-19.

Multidimensional host and viral factors determine the clinical course of COVID-19. While the virology of the disease is well studied, investigating host-related factors, including genome, transcriptome, metabolome, and exposome, can provide valuable insights into the underlying pathophysiology. We conducted integrative omics analyses to explore their intricate interplay in COVID-19.

Meta-Analysis of the Heritability of Childhood Height From 560 000 Pairs of Relatives Born Between 1929 and 2004.

Background: Childhood height is commonly used to measure children's health and nutritional status. It is used to define stunting, where a child is considered stunted if their height is shorter than two standard deviations below the Child Growth Standards median. Studies have shown that genetic and environmental factors contribute to childhood height, so understanding these contributions is important for stunting research.

Circulating Phylloquinone and the Risk of Four Female-Specific Cancers: A Mendelian Randomization Study.

Background: Observational studies have linked vitamin K and cancer, but the causality of this association remains unknown. This Mendelian randomization (MR) study aims to investigate the association between circulating phylloquinone (vitamin K) levels and four female-specific cancers.

Methods: We used four single-nucleotide polymorphisms (SNPs) to instrument phylloquinone, with the reported F-statistic 16.

LLpowershap: logistic loss-based automated Shapley values feature selection method.

Background: Shapley values have been used extensively in machine learning, not only to explain black box machine learning models, but among other tasks, also to conduct model debugging, sensitivity and fairness analyses and to select important features for robust modelling and for further follow-up analyses. Shapley values satisfy certain axioms that promote fairness in distributing contributions of features toward prediction or reducing error, after accounting for non-linear relationships and interactions when complex machine learning models are employed. Recently, feature selection methods using predictive Shapley values and p-values have been introduced, including powershap.