https://github.com/abhi18av/ljmu_masters_dissertation

https://github.com/abhi18av/ljmu_masters_dissertation

drug-resistance-prediction h2oai machine-learning python tuberculosis

Last synced: about 1 month ago
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• Host: GitHub
• URL: https://github.com/abhi18av/ljmu_masters_dissertation
• Owner: abhi18av
• License: bsd-3-clause
• Created: 2020-05-07T15:01:45.000Z (about 6 years ago)
• Default Branch: master
• Last Pushed: 2020-11-28T16:48:01.000Z (over 5 years ago)
• Last Synced: 2025-01-05T10:08:10.530Z (over 1 year ago)
• Topics: drug-resistance-prediction, h2oai, machine-learning, python, tuberculosis
• Language: Jupyter Notebook
• Homepage: https://zenodo.org/badge/latestdoi/262081640
• Size: 19.6 MB
• Stars: 1
• Watchers: 2
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
Application of ML for DRP using WGS data on MTB genomes.

==============================

This repository contains the code for my masters dissertation. 

[![DOI](https://zenodo.org/badge/262081640.svg)](https://zenodo.org/badge/latestdoi/262081640)

To execute the code, the following execution environments are recommended.

1. AWS/Azure Batch for genomic pre-processing.

2. Azure ML Studio for notebooks, with a decent server.

The rest of the instructions  are embedded within the `notebooks/FINAL/*ipynb` notebooks.

Project Organization

------------

    ├── LICENSE

    ├── README.md

    │

    ├── conda_enviroment.yml <- The minimal conda file needed to recreate the environment.

    ├── azure_enviroment.yml <- The conda file for the Azure ML studio.

    │

    ├── data

    │   ├── interim        <- Intermediate data that has been transformed.

    │   ├── processed      <- The final, canonical data sets for modeling.

    │   └── raw            <- The original, immutable data dump.

    │

    ├── models             

    │      ├── ALL_FEATURES   <- Models trained on All features.

    │      │      ├── FINAL   

    │      │

    │      └── PCA300         <- Models trained on PCA300 features.

    │

    ├── notebooks          

    │   ├── FINAL          <- The final jupyter notebooks, named as per their execution order.

    │      └── 001_feature_engineering.ipynb

    │      └── 002_choose_limited_tbportals_genomes.ipynb <- Contains the SRA IDs of genomes, can be downloaded through download.nf

    │      └── 003_eda_mono_resistance.ipynb

    │      └── 004_model_grids.ipynb

    │      └── 005_stacked_ensemble.ipynb

    │      └── 006_pca_based_ml.ipynb

    │      └── 007_model_inspection_with_without_pca.ipynb

    │

    ├── src                

    │   ├── genomic_preprocessing           <- Scripts for genomic pre-processing

    │      └── nyu_gatk.sh

    │      └── download.nf

    │      └── bwa.nf

    │      └── fastqc.nf

    │      └── gatk.nf

    │      └── picard.nf

    │      └── samtools.nf

    │      └── tb_profiler.nf

    │      └── trimmomatic.nf

    │   

    │   

    │   ├── features       <- Scripts to turn raw VCF data into tabular data for modeling

    │       └── 01_tbprofiler.py

    │       └── 02_vcf_drop_cols.py

    │       └── 03_filter_unique_snps.py

    │       └── 04_binarize_vcf.py

    │       └── 05_final_snp_df.py

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