Clustered, information-dense transcription factor binding sites identify genes with similar tissue-wide expression profiles.  BioRxiv, 2018.

doi: https://doi.org/10.1101/283267

On behalf of the Scientific Programme Committee of the European Conference of Human Genetics 2018 taking place in Milan, Italy from June 16 to June 19, 2018, we are pleased to inform you that the abstract entitled:

‘Comprehensive prediction of responses to chemotherapies by biochemically-inspired machine learning’

(Control No. 2018-A-2095-ESHG)

was among the best scored papers accepted for a poster presentation. Best Poster session takes place on Sunday, June 17, 2018 13:00 hrs, and consists of a 3 minute presentation followed by discussion at your electronic poster.

We have published a new approach to devise gene signatures to detect radiation exposure (human, murine), and to quantify levels of exposure (murine):

Zhao JZL, Mucaki EJ and Rogan PK. Predicting ionizing radiation exposure using biochemically-inspired genomic machine learning. F1000Research 2018, 7:233 (doi: 10.12688/f1000research.14048.1)