Chronic Disease Prediction Using the Common Data Model: Development Study.

Background: Chronic disease management is a major health issue worldwide. With the paradigm shift to preventive medicine, disease prediction modeling using machine learning is gaining importance for precise and accurate medical judgement.

Objective: This study aimed to develop high-performance prediction models for 4 chronic diseases using the common data model (CDM) and machine learning and to confirm the possibility for the extension of the proposed models.

Causal network inference from gene transcriptional time-series response to glucocorticoids.

Gene regulatory network inference is essential to uncover complex relationships among gene pathways and inform downstream experiments, ultimately enabling regulatory network re-engineering. Network inference from transcriptional time-series data requires accurate, interpretable, and efficient determination of causal relationships among thousands of genes. Here, we develop Bootstrap Elastic net regression from Time Series (BETS), a statistical framework based on Granger causality for the recovery of a directed gene network from transcriptional time-series data.

ACE inhibition and cardiometabolic risk factors, lung and gene expression, and plasma ACE2 levels: a Mendelian randomization study.

Angiotensin-converting enzyme 2 (ACE2) and serine protease TMPRSS2 have been implicated in cell entry for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus responsible for coronavirus disease 2019 (COVID-19). The expression of and in the lung epithelium might have implications for the risk of SARS-CoV-2 infection and severity of COVID-19. We use human genetic variants that proxy angiotensin-converting enzyme (ACE) inhibitor drug effects and cardiovascular risk factors to investigate whether these exposures affect lung and gene expression and circulating ACE2 levels.

Co-expression networks reveal the tissue-specific regulation of transcription and splicing.

Gene co-expression networks capture biologically important patterns in gene expression data, enabling functional analyses of genes, discovery of biomarkers, and interpretation of genetic variants. Most network analyses to date have been limited to assessing correlation between total gene expression levels in a single tissue or small sets of tissues. Here, we built networks that additionally capture the regulation of relative isoform abundance and splicing, along with tissue-specific connections unique to each of a diverse set of tissues.

Rewiring of human lung cell lineage and mitotic networks in lung adenocarcinomas.

Analysis of gene expression patterns in normal tissues and their perturbations in tumours can help to identify the functional roles of oncogenes or tumour suppressors and identify potential new therapeutic targets. Here, gene expression correlation networks were derived from 92 normal human lung samples and patient-matched adenocarcinomas. The networks from normal lung show that NKX2-1 is linked to the alveolar type 2 lineage, and identify PEBP4 as a novel marker expressed in alveolar type 2 cells.

Optimizing solute-water van der Waals interactions to reproduce solvation free energies.

An accurate representation of solute-water interactions is necessary for molecular dynamics simulations of biomolecules that reside in aqueous environments. Modern force fields and advanced water models describe solute-solute and water-water interactions reasonably accurately but have known shortcomings in describing solute-water interactions, demonstrated by the large differences between calculated and experimental solvation free energies across a range of peptide and drug chemistries. In this work, we introduce a method for optimizing solute-water van der Waals interactions to reproduce experimental solvation free energy data and apply it to the optimization of a fixed charge force field (AMBER ff99SB/GAFF) and advanced water model (TIP4P-Ew).