Modeling crispr-cas13d on-target and off-target effects using machine learning approaches

HIGHLIGHTS

SUMMARY

Cas13d proliferation screen and a convolutional recurrent neural_network for efficiency prediction To systematically investigate the efficiency and specificity of Cas13d, the authors conducted a two-vector CRISPR/Cas13d proliferation screening experiment (Fig 1a; see "Methods" for details). Features from sgRNA sequences and structures are extracted through convolutional recurrent neural_networks (CRNN), which is commonly used to extract features in both spatial and temporal dimensions. The ends of the whiskers represent the minimum and maximum values in the data set. connected layer in neural_network for prediction (see "Methods" for more details). DeepCas13 is different from machine_learning . . .

 



If you want to have access to all the content you need to log in!

Thanks :)

Username or Email

Password

Remember me

Lost your password?

If you don't have an account, you can create one here.