Data-driven learning how oncogenic gene expression locally alters heterocellular networks.

ABSTRACT: Developing drugs increasingly relies on mechanistic modeling and simulation. Models that capture causal relations among genetic drivers of oncogenesis, functional plasticity, and host immunity complement wet experiments. Unfortunately, formulating such mechanistic cell-level models currently relies on hand curation, which can bias how data is interpreted or the priority of drug targets. In modeling molecular-level networks, rules and algorithms are employed to limit a priori biases in formulating mechanistic models. Here we combine digital cytometry with Bayesian network inference to generate causal models of cell-level networks linking an increase in gene expression associated with oncogenesis with alterations in stromal and immune cell subsets from bulk transcriptomic datasets. We predic

SUBMITTER: Klinke DJ 

PROVIDER: S-EPMC9007999 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature