Abstract

MicroRNAs are small non-coding RNAs that play a central role in many molecular processes, but the exact rules of their activity are not known. One of the processes is gene regulation, pairing with the Ago protein and, as a pair, binds to mRNA.

The common techniques used in this field are manual feature selection followed by a classical ML method. However, these methods are greatly dependent on short sequence patterns called a seed. As a result, they work well on conventional binding caused by the seed, however, they lack in less frequent cases of unconventional binging. We build an explainable CNN model for the binding of miRNA and a subsequence of mRNA. [1]

Subsequently, we use this model to scan the whole mRNA sequence (transcript) and produce a signal of SHAP values. [2] This scanning method creates a signal sample for each transcript. We try to correlate the signal with a fold change in gene expression the miRNA would cause if introduced in large quantities to the environment. We build a CNN + RNN regression to predict the fold change based on the signal.

We hypothesize that using the signal could help to shield from the model overfitting on simple sequence patterns and help with cases where the conventional seed pattern is not strongly present.
[1] https://www.mdpi.com/2079-7737/12/3/369
[2] https://github.com/shap/shap

Slides

Lecture Recordings

Readings

1. Determinants of Functional MicroRNA Targeting
   https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9880601/
   
2. miRBind: A Deep Learning Method for miRNA Binding Classification
   https://www.mdpi.com/2073-4425/13/12/2323
3. Using Attribution Sequence Alignment to Interpret Deep Learning Models for
   miRNA Binding Site Prediction https://www.mdpi.com/2079-7737/12/3/369
   

Catering

Cukroví, camembert a hrozny.