Evaluating deep learning for predicting epigenomic profiles

In this post I will briefly introduce GOPHER – GenOmic Profile-model compreHensive EvaluatoR (yes, anything can be used in an abbreviation). I together with a class/labmate – Amber and my PI worked on developing this framework for analysing which training strategies to use for sequence-to-function deep learning models in epigenetics.

We conducted a systematic analysis of various factors and design choices involved in training such models and spent some time thinking how we can fairly evaluate them as well.

Evaluating deep learning for predicting epigenomic profiles

A very brief summary of the paper conclusions would be that quantitative models (predicting normalized read counts in a regression task) seem to outperform binary models (classifying each sequence to active or not active categories) in terms of performance, out-of-distribution generalization and interpretability (filter matches to JASPAR and how well they capture motif interactions).

You can read the full preprint here.

Leave a Reply

Your email address will not be published. Required fields are marked *