Drug Resistance Prediction in Escherichia coli Bacteria Using Machine Learning

Author: Mehdi Foroozandeh Shahraki

Abstract

Drug resistance is a growing public health threat with catastrophic medical and economic impacts thus, powerful methodologies are required urgently to tackle this crisis. Recent advancements in high-throughput sequencing technologies as well as the broad arsenal of various machine-learning techniques enable us to approach drug resistance, a traditionally clinical problem, with robust computational tools aiming to understand it and even make phenotypical predictions based on genomic data. In this study, several machine-learning methods have been applied to predict drug-resistance in a familiar cause of a diverse range of infectious diseases Escherichia coli. The results of this project highlight the potential ability of machine-learning in deciphering complex biological problems with significant universal influence.

Below is a list of files in the "./Data and Codes" directory alongside with a short description of what each of them actually do.

CODES:

1. snp2gene.py:

   This module contains several pre-processing functions which are responsible for the following: 1.1 generate a .csv gene map from .gff genome annotation files 2.2 convert SNP-based data to gene-based data.

2. BackBone.py:

   This module contains: 2.1 a class of hyper-parameter space for all ML algorithms that are used in this study. 2.2 a function to read and load .csv gene-based dataset and scale/normalize the loaded data

3. nn_models.py:

   This module contains several functions which are responsible for the following: 3.1 building Dense, CNN, LSTM neural networks from an input of parameters dictionary. 3.2 Build LRCN, CNN, feed-forward, and LSTM classifiers 3.3 masked loss function and masked accuracy function to ignore the isolates with missing labels during the training

4. BO_ML.py:

   This module is responsible for the following: 4.1 performing the nested CV evaluation and Bayesian Hyperparameter optimization for RF, SVM, LR, KNN, GNB, GBC, and XGB algorithms 4.2 training and saving different machine learning models

5. BO_DL.py:

   This module is responsible for the following: 5.1 performing the nested CV evaluation and Bayesian Hyperparameter optimization for LRCN, CNN, feed-forward, LSTM neural network architectures 5.2 training and saving different NN-based machine learning models

DATA:

1. Escherichia_coli_str_k_12_substr_mg1655.ASM584v2.48.chromosome.Chromosome.gff3:

   This file contains the genome annotation of E coli str_k_12_substr_mg1655

2. gene_based_DS.csv:

   This file contains the gene-based dataset

3. Drug_labels.csv:

   This file contains the labels for the gene-based dataset

Thank you for reading!