Prediction of regulatory targets of alternative isoforms of the epidermal growth factor receptor in a glioblastoma cell line.
Claus WeinholdtHenri WichmannJohanna KotrbaDavid H ArdellMatthias KapplerAlexander W EckertDirk VordermarkIvo GrossePublished in: BMC bioinformatics (2019)
By performing RNAi experiments for three poorly investigated EGFR isoforms, we were able to successfully predict 1140 putative target genes specifically regulated by EGFR isoforms II-IV using the developed Bayesian Gene Selection Criterion (BGSC) approach. This approach is easily utilizable for the analysis of data of other nested experimental designs, and we provide an implementation in R that is easily adaptable to similar data or experimental designs together with all raw datasets used in this study in the BGSC repository, https://github.com/GrosseLab/BGSC .
Keyphrases
- epidermal growth factor receptor
- tyrosine kinase
- advanced non small cell lung cancer
- electronic health record
- genome wide
- big data
- small cell lung cancer
- genome wide identification
- primary care
- healthcare
- transcription factor
- copy number
- machine learning
- gene expression
- dna methylation
- artificial intelligence
- mass spectrometry
- atomic force microscopy
- single cell
- deep learning
- high speed
- bioinformatics analysis