Deep Learning and CRISPR Off-Target Effect Prediction — A Walkthrough

A Bit About CRISPR

The central dogma of molecular genetics. Weird two-stranded molecule, to another weird single-stranded molecule, to a weird blob, to, finally, a weird ball of strings

Guide RNA Design and Problems

Off-target effects take place because of small mismatches between the gRNA and DNA that the CRISPR system accepts

The Alternative

The Benefit of Deep Learning

The Data

This is how the OR operator combines both sequences to create a collated sequence that can communicate the mismatches between the complementary guide RNA and the target DNA

The Feedforward Neural Network

The basic structure of an FNN

The Convolutional Neural Network: How It Works

A CNN in action, classifying people, buses, and cars on a busy street (somewhere in Europe I think?). Pretty neat 😎 huh?
The convolutional layer at work, using a preset filter to create a feature map with all the important information in a more concise form

The Results

The performance of all the different models in the experiment, with the best performance for the standard CNN (CNN_std) and the FNN with 3 hidden layers (FNN_3layer)

Next Steps

18 y/o innovator working on reversing ageing and researching cancer vaccines.

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