Descriptions

Multitask learning (secondary structure prediction, b-values prediction, solvent-accessibility prediction) can improve the prediction accuracy of protein secondary structure.

• We have to face with the class imbalance problem
• "foldername_cv": 5 fold cross validation
• Distribution of outputs:

Data

The copyright belongs to http://rostlab.org/. It can not be public.

Data representation

Using Protvec (3-gram) and follow the vector addition rule. For example:

TNCDE = UTN + TNC + NCD + CDE + DEU

Multitask learning model

Results

3 states protein secondary structure)

Multi-task learning (3 tasks, 3 states):

• Secondary Structure accuracy (3 states): 69.0%

• Solvent Accessibility accuracy (3 states): 54.6%

• B-values accuracy (3 states): 59.1%

8 states protein secondary structure

Multi-task learning (3 tasks, 8 states):

• Secondary Structure accuracy (8 states): 0.476

• Solvent Accessibility accuracy (3 states): 0.548

• B-values accuracy (3 states): 0.598

• Secondary structure

• Solvent accessibility

• b-values

Prerequisites

• python 2.7
• tensorflow 1.4.0
• ProtVec

How to run

Go into each subfolder and run the code following:

• python lstm.py

Author

Binh Do

License

This project is licensed under the MIT License