# Multi-Label Classification for Predicting Antimicrobial Resistance on _E. coli_
## 1 Introduction  
According to the World Health Organization (WHO) and Centers for Disease Control and Prevention (CDC), at least 2 million cases of antibiotic-resistant infections are recorded annually worldwide [1, 2]. By 2050, the annual death toll caused by bacteria resistant to antibiotics is estimated to reach 10 million, leading to a global economic burden in tackling the issue [3]. Antimicrobial resistance is defined as the capability of microorganisms, such as bacteria, fungi, viruses, and parasites, to resist the effects of antimicrobial drugs. The emergence of antimicrobial resistance is attributed mainly to the overuse and abuse of antimicrobial agents [4]. Consequently, microorganisms resistant to common and widely used antibiotics are becoming increasingly common [5]. Antimicrobial resistance in _Escherichia coli_ (_E. coli_) is a significant concern due to its potential impact on both the veterinary and human health sectors. Understanding and predicting antimicrobial resistance to develop effective prevention and control strategies is of the utmost importance. _Escherichia coli_ is a type of bacteria that is often present in the intestines of both humans and animals. This bacterium is commonly used as a marker for monitoring antimicrobial resistance, making it essential for research on the development of resistance in the fields of veterinary and human health on a global scale [6]. There is a need for alternative strategies to fight antimicrobial resistance and prevent the spreading of drug-resistant _E. coli_ in humans and animals. Advances in the field of medicinal chemistry have facilitated the syntheses of antimicrobial compounds that aim to combat resistant _E. coli_ strains. Evidently, utilizing synthesized compounds in high-throughput screening assays against various _E. coli_ strains enables the identification of compounds that are effective against resistant strains. In addition, through the integration of the synthesized compounds, predictive models using machine learning (ML) algorithms are employed to analyze patterns and identify the factors contributing to AMR in _E. coli_, allowing for early identification and intervention [7]. Multi-label classification, a field in ML, aids in achieving this goal. Multilabel classification is a supervised classification method where each instance in a dataset can be assigned multiple labels simultaneously from a predefined set of possible labels [8]. Unlike the traditional single-label classification, multiple overlapping labels can be assigned to instances based on features in multi-label classification [9]. The current methods used to predict AMR often rely on the manual interpretation of laboratory tests, which can be time consuming and subject to human error. Moreover, with the increasing variety of antimicrobial agents available, it becomes challenging to predict the resistance patterns for individual microorganisms accurately [7]. ML algorithms have shown promise in predicting antimicrobial resistance in _E. coli_. Studies that have utilized ML algorithms and mass spectrometry have been successful in predicting antimicrobial susceptibility, differentiating closely related species, serotyping, and clonal lineages of significant clinical pathogens such as methicillin-resistant _Staphylococcus aureus_[10]. Moreover, ML models have been utilized to predict peptides' antimicrobial activity against gram-negative and gram-positive bacteria, highlighting their potential in combating AMR [11]. The researchers in Refs. [12; 13] analyzed AMR by utilizing various ML algorithms such as support vector machine (SVM), logistic regression (LR), and random forest (RF) on data from whole-genome sequencing. The researchers then attained a high accuracy in predicting AMR. ML algorithms have also been employed to predict linear antimicrobial peptides active against _E. coli_, allowing for the identification of potential treatments for drug-resistant strains [14]. Furthermore, the researchers in Ref.