Biology subjects

Abedi, V.

Publications and source records attributed to Abedi, V..

2 recordsLinked to original sources

bioRxiv2019

Identification of regulatory genes through global gene expression analysis of a Helicobacter pylori co-culture system

Helicobacter pylori is a gram-negative bacterium that establishes life-long infections by inducing immunoregulatory responses. We have developed a novel ex vivo H. pylori co-culture system to identify new regulatory genes based on expression kinetics overlapping with that of genes with known regulatory functions. Using this novel experimental platform, in combination with global transcriptomic analysis, we have identified five lead candidates, validated them using mouse models of H. pylori infection and in vitro co-cultures under pro-inflammatory conditions. Plexin domain containing 2 (Plxdc2) was selected as the top lead immunoregulatory target. Gene silencing and ligand-induced activation studies confirmed its predicted regulatory function. Our integrated bioinformatics analyses and experimental validation platform has enabled the discovery of new immunoregulatory genes. This pipeline can be used for the identification of genes with therapeutic applications for treating infectious, inflammatory, and autoimmune diseases.

immunology↗

bioRxiv2018

Latent-Based Imputation of Laboratory Measures from Electronic Health Records: Case for Complex Diseases

Imputation is a key step in Electronic Health Records-mining as it can significantly affect the conclusions derived from the downstream analysis. There are three main categories that explain the missingness in clinical settings-incompleteness, inconsistency, and inaccuracy-and these can capture a variety of situations: the patient did not seek treatment, the health care provider did not enter the information, etc. We used EHR data from patients diagnosed with Inflammatory Bowel Disease from Geisinger Health System to design a novel imputation that focuses on a complex phenotype. Our approach is based on latent-based analysis integrated with clustering to group patients based on their comorbidities before imputation. IBD is a chronic illness of unclear etiology and without a complete cure. We have taken advantage of the complexity of IBD to pre-process the EHR data of 10,498 IBD patients and show that imputation can be improved using shared latent comorbidities. The R code and sample simulated input data will be available at a future time.

bioinformatics↗