STRING Score Optimization

This repository contains code for the optimization of the String-DB protein interaction prediction score.

Installation

This requires a valid installation of Anaconda or miniconda. Create the Python environment as described below:

cd configs
conda env create -f env.yml 
conda activate string_score_env

Data

The data folder should be copied into your src directory. The data folder contains all raw data required to run the model scripts.

cd src
cp -r /mnt/mnemo1/sum02dean/dean_mnt/projects/STRINGSCORE/src/data .

Label Generation

It is not essential to generate labels since this is pre-provided in the data directory. However if required, labels can be regenerated via running:

cd src/scripts
bash run_label_generation.sh

Data Preparation

To change the default pre-processing arguments please modify the values in run_pre_process.sh file, then run:

cd src/scripts
bash run_pre_process.sh

Running the model

Each model comes with a seperate bash file. Within each bash file there are sets of options to configure. To see the definition of each option, run the corresponding python file followed by --help.

• run_xgboost.sh
• run_bambi.sh
• run_net.sh

Example of calling help for model args:

python xgboost_model.py --help

Model Output

Outputs will be organised as shown below - this is automatically generated by the python model scripts:

    models
    |_ _output_directory
    |   |_ _model_name
    |       |_ _(plots and main results)
    |       |_ _ensemble 
    |           |_ _ (ensemble model data)

Example Results

Running several models on Yeast data-set shows high performance across all models - XGBoost outperforms the other models both for AUC and score correctness.

Hyper-parameter selection

Below are listed some useful parameters to use for initial hyper-parameter optimization, see the /models/ section for model specific documentation. Note that for Bambi - the majority of parameters are determined using NUTS.

XGBoost:

Parameter	Value
n_sampling_runs	3
max_depth	15
eta	0.1
objective	'binary:logistic'
alpha	0.1
lambda	0.01
subsample	0.9
colsample_bynode	0.2

Bambi:

Parameter	Value
n_sampling_runs	1
n_chains	2
n_draws	1000
n_tune	3000
family	bernouli

Neural Network (Pytorch):

Parameter	Value
n_sampling_runs	3
batch_size	50
epochs	100
hidden_size	200
learning_rate	0.001

Models

Models	Resources
XGBoost	XGBoost Documentation
Bambi	Bambi Documentation
Neural Net (Pytorch)	Pytorch Documentation

Potential conflicts

"""Pandas Multiindex deprecation: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead."""

Major dependencies

The conda environemnt provided should contain all of these requirements. If not, you can find them at the following sources.

Dependency	Installation
Bambi	Install with: pip install -U git+https://github.com/bambinos/bambi.git@main
XGBoost	Pypi
Pytorch (cpu, linux)	Pypi
torch-summary	Pypi
R for Python (second option)	Anaconda
Perl for Python (second option)	Pypi
Perl-Json module for Python (second option)	Pypi

IMPORTANT! For installation of R, Perl and Perl-Json install in the order shown. Always use the second option suggested by Pypi installation page.

Combining Plots Manually

Note on combining plots manually: Make sure that file paths inside the master JSON are relative to the src directory.