Deep learning use case in genomics
==================================

Objective
---------

-  Choose a use case where machine learning (ML) technique is used to
   solve a genomics problem.

   -  AlphaMissense - implements a deep learning model to predict the
      pathogenicity of DNA changes that alter protein sequence.

-  Present a genomics scientist’s perspective of the ML workflow
   implemented in the use-case. i.e., genomics interpretation of the
   AlphaMissense deep learning workflow.
-  Main focus is to highlight the importance of ML in solving genomics
   problems and not to promote AlphaMissense as a clinically viable
   product.

Content
-------

-  `Genomics context of missense
   variants <AlphaMissense/01_genomics_context.html>`__

   -  Challenges in the current clinical setting
   -  Current methods to predict the pathogenicity of missense variants
   -  Ensembl Variation pathogenicity predictions
   -  Limitations in current methods
   -  Deep learning based solution for pathogenicity predictions

-  `Using deep learning models to predict
   pathogenicity <AlphaMissense/02_deep_learning.html>`__

   -  Why use deep learning?
   -  Improving the accuracy of model predictions

-  `AlphaMissense <AlphaMissense/03_AlphaMissense.html>`__

   -  Studying genetic variations and disease outcomes
   -  AlphaMissense deep learning workflow
   -  Genomics context of each step deep learning workflow

-  `Data preparation <AlphaMissense/04_Data_prep.html>`__

   -  Data Preparation workflow
   -  Dataset Splitting

-  `Feature engineering <AlphaMissense/05_Feature_eng.html>`__
-  `Model training and
   validation <AlphaMissense/06_Model_training.html>`__
-  `Model evaluation <AlphaMissense/07_Model_eval.html>`__
-  `AlphaMissense community
   resource <AlphaMissense/08_Community_resource.html>`__
-  `Hands-on experiment <AlphaMissense/09_Handson.html>`__

Author/Instructor
-----------------
- Pubudu Saneth Samarakoon