English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Toxicogenomics (TGx) is a powerful method to evaluate toxicity and is widely used in both  in vivo  and  in vitro  assays. For  in vivo  TGx, reduction, refinement, and replacement represent the unremitting pursuit of live-animal tests, but  in vitro  assays, as alternatives, usually demonstrate poor correlation with real  in vivo  assays. In living subjects, in addition to drug effects, inner-environmental reactions also affect genetic variation, and these two factors are further jointly reflected in gene abundance. Thus, finding a strategy to factorize inner-environmental factor from  in vivo  assays based on gene expression levels and to further utilize  in vitro  data to better simulate  in vivo  data is needed. We proposed a strategy based on post‐modified non‐negative matrix factorization, which can estimate the gene expression profiles and contents of major factors in samples. The applicability of the strategy was first verified, and the strategy was then utilized to simulate  in vivo  data by correcting  in vitro  data. The similarities between real  in vivo  data and simulated data (single-dose 0.72, repeat-doses 0.75) were higher than those observed when directly comparing real  in vivo  data with  in vitro  data (single-dose 0.56, repeat-doses 0.70). Moreover, by keeping environment-related factor, a simulation can always be generated by using  in vitro  data to provide potential substitutions for  in vivo  TGx and to reduce the launch of live-animal tests.

Narrowing the Gap Between In Vitro and In Vivo Genetic Profiles by Deconvoluting Toxicogenomic Data In Silico