AI Article Synopsis
- We aim to predict transcription factor (TF) binding events using gene expression and epigenetic modifications.
- Using data from the Encode project and The Cancer Genome Atlas, we applied the random forest method to analyze TF-binding events.
- Our predictive models demonstrated high performance in various cell lines and revealed a significant association between top TFs and cancer-related processes, allowing us to apply our findings to other cell lines and tissues.
Article Abstract
We aim to predict transcription factor (TF) binding events from knowledge of gene expression and epigenetic modifications. TF-binding events based on the Encode project and The Cancer Genome Atlas data were analyzed by the random forest method. We showed the high performance of TF-binding predictive models in GM12878, HeLa, HepG2 and K562 cell lines and applied them to other cell lines and tissues. The genes bound by the top TFs ( and ) were significantly associated with cancer-related processes such as cell proliferation and DNA repair. We successfully constructed TF-binding predictive models in cell lines and applied them in tissues.
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| http://dx.doi.org/10.2217/epi-2019-0321 | DOI Listing |
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