




































Highlights in BioScience
ISSN:2682-4043
DOI:10.36462/H.BioSci.202304

Review Article

Open Access

1 Department of Microbiology, Central University

of Tamil Nadu, Tamil Nadu, India.

* To whom correspondence should be
addressed: rkaushik@cutn.ac.in

Editor: Alsamman M. Alsamman, International
Center for Agricultural Research in the Dry Areas
(ICARDA), Giza, Egypt.

Reviewer(s):
Suleiman Aminu, Department of Biochemistry,
Ahmadu Bello University, Zaria, Nigeria.

AbdulAziz Ascandari, Chemical and Biochemical
Sciences-Green Process Engineering, University
Mohammed VI Polytechnic, BenGuerir, Morocco.

Received: October 10, 2023

Accepted: December 24, 2023

Published: December 30, 2023

Citation: Mary AS, Patil MM, Kundu G,
Rajaram K. Machine Learning Mediated Advanced
Phage and Antimicrobial Therapy - A Futuristic
Approach. 2023 Dec 30;6:bs202304

Copyright: © 2023 Mary et al.. This is an open
access article distributed under the terms of the Cre-
ative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in
any medium, provided the original author and source
are credited.
Data Availability Statement: All relevant data are
within the paper and supplementary materials.
Funding: The authors have no support or funding to
report.
Competing interests: The authors declare that they
have no competing interests.

Machine Learning Mediated Advanced Phage and Antimicrobial
Therapy - A Futuristic Approach

Aarcha Shanmugha Mary 1
>< , Manali Manik Patil 1

>< , Godhuli Kundu 1
>< , Kaushik

Rajaram *,1
><�

Abstract

The emergence of antimicrobial resistance (AMR) has overwhelmed the contemporary
curatives and have turned into one of the major challenges in the biomedical sector.
With increasing deaths being associated with AMR every year; early detection of
pathogens and development of novel drugs and alternative therapies, have all become
ad hoc in diagnosis, prognosis and patient survival. Bacteriophage therapy remains a
viable strategy to counteract AMR, yet unduly restrained by phage resistance. Phage
infection is a natural phenomenon and can be widely manipulated in vitro using
advanced techniques including the CRISPR/Cas systems which renders phage therapy
an upper hand in comparison to conventional drugs. Phage identification, host range
detection, determination of phage-receptor binding efficiency, adsorption rate, phage
genome analysis are crucial stages in phage selection and phage cocktail preparation
and moreover pivotal in flourishing phage therapy. The ascent of translational research
and omics has allowed the development of quick, reliable and precise strategies for
phage-based diagnosis and treatment techniques. However, in vitro evaluation of
AMR and phage factors as well as storing, processing and analyzing large laboratory
data outputs are expensive, time-consuming and labor-intensive. Machine learning
(ML) is a utilitarian strategy to organize, store, analyze data sets and more importantly
allows prediction of certain features by recognizing patterns in the data sets. With
the huge number of research been carried out around the globe and enormous data
sets being published and stored in databases, ML can utilize the available data to
perform and guide in developing alternative therapeutics. Several ML based tools
have been developed to predict resistance in host, phage grouping for cocktail
preparation, resistance and lysogenic genes detection, phage genomic evaluation
and to understand phage-host interactions. ML also allows the in silico analysis of
large samples (drug/phage) and reduces sample size for in vitro evaluation thereby
reducing overall costs, time and labor. The present review summarizes the available
ML algorithms and corresponding databases used in AMR and phage research. It
also emphasizes the status quo of antimicrobial and phage resistance in the healthcare
sector and analyses the role of ML in analyzing biological databases in order to predict
possible phage/drug-host interaction patterns, phage susceptibility, suitability of phage
strains for therapy and recommends the most efficient drug combinations and treatment
strategies.

Keywords: Antimicrobial, AMR, Bacteriophage, Resistance, Machine learning, Database,

Phage, Bacteria, Pathogen, Infection

Background
An alarming hike in morbidity and mortality due to bacterial infections have been reported

worldwide in the past decade [1]. The Centre for Disease Control and Prevention and the World

Health Organisation have arbitrated in this global health threat, and have identified and listed the

priority pathogens entitled with the acronym ESKAPE, and have implemented strategies to combat

pan drug-resistant (PDR), multidrug-resistant (MDR) and extensively drug-resistant (XDR) bacteria

[2; 3]. The Clinical and Laboratory Standards Institute (CLSI) has disclosed the ineffectiveness and

failure of antibiotics against ESKAPE pathogens overtime [4].

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https://creativecommons.org/licenses/by/4.0/
https://creativecommons.org/licenses/by/4.0/
mailto:aarchaachu3939@gmail.com
mailto:manalipatil85801@gmail.com
mailto:gksonai03@gmail.com
mailto:rkaushik@cutn.ac.in
http://orcid.org/0000-0002-1830-4083
http://bioscience.highlightsin.org/


Mary et al., 2023 Machine Learning Mediated Advanced Phage and Antimicrobial Therapy - A Futuristic Approach

Development of alternative drugs is on the horizon and in
2019, Mulani et al., catalogued the emerging approaches like
antimicrobial peptides, antibiotic combination therapies, photo-
dynamic light therapy, bacteriophage therapy, nanoparticles and
phytochemicals to combat antimicrobial resistance (AMR) [4].
Additionally, state-of-the-art treatment strategies like CRISPR/
Cas9 and bacterial vaccines have also been surfaced and are under
exploratory research and development [5].

Bacteriophage are ubiquitous and are present alongside the
host and are pivotal in regulating bacterial populations; thereby
maintaining microbial balance. Phage therapy has been largely
neglected during the antibiotic era; however, with the emergence
of drug-resistant bacteria it has regained its lost glory. On one
hand, bacteriophage remains a logical and sustainable remedy
for AMR, whereas bacterial evolution and fitness in presence
of phage allows the persistent cells/mutants to grow and prop-
agate [6]. Bacteriophage resistance can also be associated to
bacterial abortive mechanisms, receptor mutations, Restriction-
Modification (RM) systems and CRISPR-Cas systems [7]. While
bacteriophage resistance is a backlash for phage based treat-
ment regimen, certain clinical implications incurred due to phage
resistance are of therapeutic importance. In a clinical setting,
phage resistance does not only result in treatment failure but can
also aid cross-resistance across other phage and antibiotics [7].
In 2018, Wright et al., demonstrated the modular network of
cross-resistance in spontaneous mutants of P. aeruginosa against
27 distinct phage; and emphasized the role of cross-resistance
network based prediction of mutation frequency and phage com-
binations for successful therapy [8]. Screening and identifying
putative phage for effective host inhibition is yet another major
challenge where conventional methods can turn out to be unreli-
able, time-consuming and labour-intensive. Melo et al., in 2022,
evinced the use of Flow Cytometry for fast, reliable and high-
throughput phage screening [9]. Genomic analysis associated to
receptor synthesis of P. aeruginosa phage (K8) resistant-mutants
by Pan et al., in 2016, revealed the absence of O-antigen (serve
as receptor) in mutants [10]. Similarly, innumerable research has
documented the genes associated to the receptors modification
in host [11; 12; 13], receptor binding proteins (RBP) [14; 15],
antimicrobial genes (ARGs) [16; 17], cross-resistance network
[8], phage-host interaction [18; 19], endotoxins [20; 21], lysins
[22; 23; 24; 25], pharmacokinetics [26; 27], whole genome data
[28; 29; 30], gene expression data [31; 32] and structural infor-
mation [33; 34] all of which are paramount information in estab-
lishing phage therapy. The advancements in research strategies,
subsequent outputs and innumerable data has been largely con-
trolled by computational intervention and developments in bioin-
formatics [35]. Even when bioinformatics provides large range of
data storage, indexing, analysis and data retrieval; recent develop-
ments in machine learning including deep learning and artificial
neural network (ANN) have allowed the unprecedented predic-
tion by analysing massive data sets [36]. Biological databases
or repositories are key prerequisites in machine learning and are

classified into primary, secondary and composite, and typically
reserve molecular sequences (GenBank, DDBJ, PIR), structures
(PDB), metabolic pathways (KEGG, MetaCyc), enzyme structure
and interactions (BRENDA), molecules (PubChem), microarray
gene expression data (GEO), taxonomic data (Catalogue of life),
disease data (OMIM), model organisms (RGD, Flybase) and
bibliographic data (PubMed) [37]. Machine learning algorithms
learns from these data sets by analysing and identifying patterns;
and this is particularly useful in prediction, classification, fea-
ture identification/selection and clustering [38]. ML algorithms
have remarkable applications in biomedical research including
disease diagnosis, drug discovery and development, personal-
ized medicine, medical image analysis, electronic health record
analysis and supreme role in analysing raw data in genomics
and proteomics [39]. Several concerns arise in using machine
learning and deep learning for biological data analysis, including
ethical integrity and analysis of noise data along with selected
features [40]. This review, elaborates the use of Machine learn-
ing in devising efficient therapeutic strategies to combat AMR as
well as its applications in exploratory and basic research. It also
briefs the potential of phage therapy and corresponding databases
and ML algorithms to aid in the development of phage based
curatives and strategies to overcome phage resistance by pre-
dicting bacterial susceptibility, phage host range and infectivity.
This review also summarizes currently used ML algorithms and
databases for research and medical purposes.

Phage therapy
The antibiotic pipeline has shown seldom growth with very

few novel compounds being commercialized in the recent years
[41]. It is safe to say that the ’post-antibiotic era’ is approaching,
and the development of effective alternative therapies is essential.
The discovery of the mighty penicillin and the subsequent devel-
opment of other broad-spectrum antibiotics marked the beginning
of the golden age of antibiotics, and as a result, the then-used
alternatives like phage and other therapeutics have been side-
lined since the 1940s [42]. Lytic phages are potential bactericidal
agents and have been successfully used in clinical practices. The
phage-bacterial interactions are commonly observed to be ob-
ligate lytic (virulent), pseudolysogenic, lysogenic, and chronic
[43]. Lytic phages are bacterial viruses that replicate immedi-
ately after entering the host cell and release the progenies; on
the other hand, lysogenic phages are viruses that integrate the
phage genome with the bacterial genome for generations and can
resume the lytic life cycle [44]. Pseudolysogeny occurs when
the host cells are deprived of nutrients, and the phage genome
neither enters the lytic nor lysogenic cycle but stays inactive
[45]. Chronic life cycle is also known as the carrier state where
the progeny is released through the host cell membrane without
rupturing or damaging the cell, resulting in a long-term infec-
tion [44]. While lytic phages are the only bacterial viruses that
could be translated for therapy, lysogenic conversion of phage
in bacteria has allowed the acquisition of undesirable genes. For

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Mary et al., 2023 Machine Learning Mediated Advanced Phage and Antimicrobial Therapy - A Futuristic Approach

instance, Corynebacterium diphtheria and enterohaemorrhagic
Escherichia coli acquired toxin-producing genes - diphtheria
toxin (siphovirus β-phage) and Shiga toxin (lambdoid phage)
respectively from the integrated phage genome [46; 47]. Besides
the type of phage life cycle, the optimal bactericidal efficacy of
phage is also dependent on factors like adsorption rate, latency
period, multiplicity of infection (MOI), and burst size. However,
bacterial features including the host outer membrane (LPS, cap-
sule, peptidoglycan), receptors, presence or absence of flagella
or pili can alter the fate of phage infectivity. Xuan et al., through
their experiment in 2022, proved that quorum sensing upregu-
lated the synthesis of lipopolysaccharides (typically for biofilm
production) which are receptors for phage adsorption, thereby
promoting phage infection [42]. Phage-encoded enzymes like
depolymerases are capable of degrading the glycan protective
layer in bacteria to establish infection [48]. At a clinical setting,
the use of phage cocktails and lysins are also potential strategies
to ensure bacterial growth suppression.

In 2019, Aslam et al., reported the use of bacteriophage
cocktails for treatment against P. aeruginosa and Burkholderia
dolosa infection in lung transplant recipients [49]. Similarly,
independent studies have evaluated the use of CT-PA (P. aerugi-
nosa cocktail), AB-SA01 and NOV012 (Staphylococcus aureus
cocktail) for treatment against chronic rhinosinusitis [50; 51; 52].
Uyttebroek et al., in 2021, also summarized the use of bacterio-
phage against recalcitrant chronic rhinosinusitis due to S. aureus
and P. aeruginosa colonization and biofilm formation [53]. A
recent study in 20 patients positive for Mycobacterium infec-
tion treated with intravenous phage administration, 11 patients
showed a favorable response, and phage neutralizing antibodies
were found in 8 patients [54].

Topical phage administration is a reliable strategy to ensure
maximum phage stability, with minimal immune responses. PP1-
131 , phage cocktail used against P. aeruginosa burn wound infec-
tions, was found to reduce the bacterial load, but at a significantly
lower rate in comparison to the standard of care treatment. The
drop in the phage performance was linked to the loss of titer
during manufacturing where the participants received low phage
concentrations than intended [55]. FAGOMA (Spanish Network
of Bacteriophages and Transducing Elements) in its regard to
bacteriophage therapy advised the use of phage-based therapy
only for patients infected with MDR pathogens or in case of
antibiotic hypersensitivity and infection in antibiotic reluctant
areas such as the prosthetics [56].

Even with a vast number of in vivo and clinical studies, com-
mercialization of phage-based therapy has been under scrutiny
due to the heterogeneity in the outcome and also due to the lack
of unreported adverse effects. From the stage of selection of
phages (single phage/cocktails), mode of administration to the
treatment duration, no standards have been established till date.
Onsea et al., in 2021, devised a multidisciplinary strategy to
overcome aforementioned hurdles in establishing phage therapy
as a standard of care treatment [57]. Consecutive reports have

been updated on the status of phage therapy in Germany from the
1930s till date along with the present challenges in phage produc-
tion and commercialization on a large scale [58]. Phage-based
therapeutic product development should ensure high quality, ef-
ficacy, and safety for clinical usage and, moreover, should be
GMP-certified [59].

Another widely accepted aspect of phage application in clin-
ical practice is the adjunct use of phage with antibiotic. Liu et
al., in 2020, evaluated the phage-antibiotic interactions (antag-
onism, synergy, additive) by analyzing the stoichiometry and
among different classes of antibiotics. Through specific real-time
readout synograph’, they concluded that the mechanism of phage-
antibiotic synergy is antibiotic class dependent, and the syner-
gistic activity can be suppressed by bacterial growth conditions
[60]. The resensitization of antibiotic-resistant strains during
phage-antibiotic combination therapy is attributed to the trade-off
costs during bacterial fitness. Wang et al., in 2021, demonstrated
the use of colistin-phage (Phab24) combinations against Acineto-
bacter baumannii, and reported that phage Phab24 was capable
of eliminating both colistin-sensitive and resistant strains along
with increased sensitivity of phage-resistant mutants to colistin
[61]. Emerging proofs are indicative of successful phage therapy,
whereas resistance is a significant factor in establishing the same.

Antimicrobial and phage resistance
The resurrection of phage therapy and augmentation of an-

tibiotics have shown potential in developing efficient bacterici-
dal therapeutics; whereas, phage and antibiotic/drug resistance
prevails as a major concern. Bacteria attain antimicrobial re-
sistance by employing one or more mechanisms that may in-
volve either drug alteration/inactivation, overcoming intracellular
drug accumulation, modification of the drug binding sites, ef-
flux pumps, or by forming biofilms, Figure 1 [62]. ESKAPE
pathogens also acquire novel resistance mechanisms which are
not part of their natural intrinsic defense methods. Enterococcus
faecium produces penicillin-binding-protein 5 (PBP5) that pro-
vides protection against β-lactam drugs (penicillin, ampicillin,
and cephalosporins). E. faecium also resists the combined doses
of aminoglycosides and β-lactam/glycopeptides by chromoso-
mal AAC (6’)-I enzyme [63]. The clonal complex 17 (CC17)
strains of E. faecium are assigned to be responsible for hospital-
acquired E. faecium infection and contain virulence and resis-
tance genes conferring protection against a series of antibiotics,
including ampicillin and quinolones [64]. Similarly, S. aureus
have attained resistance against penicillinase-resistant drugs like
methicillin, cloxacillin, and oxacillin (MRSA strains) but remain
susceptible to glycopeptides. Resistance towards β-lactam drugs
is conferred by plasmid-encoded blaz gene as well as through
PBPs (mecA & mecC) [65]. Last resort drugs like colistin and
tigecycline are used for ESBL (extended-spectrum β-lactamases)
and carbapenemase-producing Klebsiella pneumoniae infections;
whereas, colistin resistance is also reported (mcr gene) giving
rise to untreatable pan-drug infections [66].

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Mary et al., 2023 Machine Learning Mediated Advanced Phage and Antimicrobial Therapy - A Futuristic Approach

In regard to Acinetobacter baumannii and P. aeruginosa, the
intrinsic defense is quite intriguing with impermeable outer mem-
brane and enhanced efflux pumps. β-lactamases and PBP confer
protection against β-lactam and carbapenem drugs in most cases
[67]. In P. aeruginosa, OprD loss is associated with imipenem
resistance [68].

Antibiogram is one of the most commonly used cost-effective
in vitro resistance detection strategies. Other phenotypic meth-
ods include MIC detection (micro-dilution method), breakpoint
agar method, biochemical tests, immunographic tests (CARBA-5,
RESIST-4), electrochemical test (BYG test), motility test, stain-
ing, etc. MicroScan walk assay, BD Phoenix, and Vittek 2 are
examples of semi-automated MIC determination approaches [69].
Bacterial strain identification and differentiation are carried out
using MALDI-TOF analysis and strain-specific tests like MGP
(methyl-α-D-glucopyranoside) test (E. faecium), slide agglutina-
tion test ( S. aureus), modified Hodge test, and carbapenemase
inhibition method ( P. aeruginosa & A. baumannii), etc. Geno-
typic characterization is ad hoc and mostly targets the detection of
genes including superoxide dismutase (sodA), vanA, vanB (gly-
copeptides resistance genes), mecA, mecC (methicillin resistance
genes), blaKPC, blaNDM, blaIMP, blaVIM, blaOXA-48 (carbapenem
resistance), mcr (colistin resistance), aphA6 , armA (amikacin
resistance) through LAMP, microarray, real-time PCR, and whole
genome sequencing (WGS) [69].

Bacteriophage resistance is a complex yet a natural evolution-
ary process in bacteria. Nevertheless, the implications of phage
resistance have more arena of challenges than antimicrobial resis-
tance. Phage resistance is associated with trade-off /fitness costs
that can alter the genotypic and phenotypic characteristics of
the bacterial strain. Studies have reported reduced virulence, en-
hanced cross-resistance, altered antibiogram, changes in biofilm
formation, permeability variations, and several other modifica-
tions during phage exposure [70]. Hesse et al., in 2020, evaluated
57 phage-resistant mutants ( K. pneumoniae) and reported that
each mutant had distinct genes involving in mutation but with
similar function associated with assembly or synthesis of surface
receptors which ultimately affected phage adsorption in mutants
[71]. Phage-resistant UPEC (Uropathogenic E. coli) strains ex-
hibited alterations in LPS (lipopolysaccharide) and were con-
firmed via WGS [72]. Garb et al., studied the defense-associated
sirtuin (DSR) proteins in 2022, and found that SIR2 (N-terminal
sirtuin) domain acts as NAD+ depletors and helps bacteria to
abort phage adsorption and propagation [73]. One of the major
findings by Laure et al., in 2022, on trade-off costs in Salmonella
typhimurium was the increased β-lactamase activity of mutants
obtained after phage and phage +ampicillin treatment [74]. Li
et al., in 2022, investigated multiple mutant strains of P. aerugi-
nosa and revealed major mutations in pilT and pilB (type IV pili)
genes and chromosomal deletions of approximately 294 kb in-
volving galU (UTP-glucose-1-phosphate uridylyltransferase) and
hmgA (homogentisate 1,2-dioxygenase) [75]. Enhanced DNA
exonuclease activity through overexpression of mpr gene in My-

cobacterium smegmatis was proved to confer resistance against
phage [76]. Owen et al., in 2021, identified BstA-phage-defense
proteins encoded by prophage that inhibit exogenous lytic phage
infections [77]. Similarly, Charity et al., in 2022, correlated
mTmII prophage genome integration in Salmonella typhimurium
to cause increased fitness and drug resistance [78]. Aforemen-
tioned studies are rather a glimpse at the ongoing research to
unravel phage-host interactions and resistance mechanisms. A
compelling need to develop sophisticated strategies to control and
allow the development of prediction models prevails. The data
produced through these studies could be put in use to manifest
a much clearer picture for the development of phage and other
antimicrobial therapeutics.

Machine learning
Machine learning (ML) is a scale-up strategy in refining and

developing Artificial Intelligence (AI). Contradictory to sym-
bolic AI; ML focuses on learning from datasets and develops an
algorithm that could be novel with a distinct understanding of
certain features and their respective weights [79]. In biomedical
research, computational methodologies have failed in analyzing
enormous datasets and ML comes in handy where a labeled/un-
labeled dataset is used to train, validate, and test algorithms.
Interpretation using ML becomes more systematic and allows
classification, clustering, and, more importantly, prediction [80].
Supervised, semi-supervised, unsupervised, and reinforcement
learning are the major learning methods used in ML. In layman’s
terms, ML algorithms are fed with raw data, data that are labeled
(input/output, cause/effect), or with unlabeled data; either way,
ML identifies hidden patterns and allows classification/regression
(supervised) grouping/clustering (unsupervised) [81]. Supervised
learning uses a labeled dataset with identified features and tar-
gets, and unsupervised datasets use unlabeled (raw) data where
the algorithm determines the best features and target [82]. Re-
gression and classification are two common tasks in supervised
learning. On the other hand, association, clustering, and anomaly
detection are tasks in unsupervised ML. Reinforcement learn-
ing is a trial-and-error-based learning strategy and is regarded
as the best attempt at modeling human-like learning experience
[83]. Support vector machine (SVM), linear regression, logistic
regression, naïve Bayes classifier (NB), ANN, k-nearest neighbor
(kNN), random forest, and decision trees are ML algorithms that
are used to understand the relationship among the features and
outcome/target [84]. While linear regression (univariate/multi-
variate) uses a linear line to describe the relationship, logistic
regression predicts a sigmoidal relationship between features
and the probability of an outcome. Decision trees classify the
data based on features, beginning from a root node and parti-
tioning into decision and terminal nodes. Random forest is an
ensemble producing several decision trees [83]. Deep learning
is a subset of ML, where hidden neural layers perform learning
and decision-making more efficiently without other intervention
[85]. Stochastic models are more approachable in the case of

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Mary et al., 2023 Machine Learning Mediated Advanced Phage and Antimicrobial Therapy - A Futuristic Approach

Figure 1. Antibiotic and Phage resistance mechanisms in bacteria. A) Intrinsic phage defence involves production of thick outer membrane and receptor modifica-
tions. After the phage genome enters the bacterial cell restriction modification (RM) system or CRISPR Cas system degrades or inactivates the genome and avoids
further transcription, translation and phage assembly. B) Antibiotic or drug resistance mechanisms involves enzymatically inactivating the drugs, drug modification
and target site alteration. Intrinsic mechanisms involve biofilm production, reduced drug uptake by altering membrane permeability and enhanced efflux pumps.

randomness and ambiguity in data. In 2021, Goodswen et al.,
reviewed the uses of ML in biology, which included disease
diagnosis, predicting outbreaks/vaccine candidate/drug targets,
drug resistance identification, and microbial interactions [86].
With the assistance of some reliable methodological strategies,
it will be more realistic to model the global pan-genome. The
Expectation-Maximization (EM) algorithm is the multitude ap-
proach for determining maximum a posteriori estimates (MAP)
or maximum likelihood estimates (MLE) for undocumented mod-
els in statistical analysis [87]. Reboredo et al., in 2021, have
updated the trends in the usage of ML in drug discovery; the
authors also emphasized on the economic advantages of ML in
detecting active compounds and thereby reducing the cost and
effort in processing large sample sizes in pre-clinical and clinical
studies [88]. Convolutional Neural Network (CNN) in ML is re-
portedly efficient in analyzing images (MRI, CT, PET scans) with
significant application in nuclear medicine [89]. With unlimited
application in the biomedical sector along with novel develop-
ments, ML has become a reliable tool. Among several other
applications, ML in AMR and development of phage therapy is a
growing area of interest.

Machine Learning and Antimicrobial Resistance
Computation of data ensures greater accuracy in analysis,

which is the foremost requirement for data analysis in the biomed-
ical sector. Several infectious diseases are frequently being
treated with antimicrobial drugs; however, antimicrobial resis-
tance in bacteria has become a generic reason for treatment fail-
ure. Developments in machine learning (ML) have allowed the
detection and prediction-based applications in antimicrobial re-
sistance [90]. Distinct tools for determining the antimicrobial
resistance genes and virulence genes based on ML are currently
available (Figure 2).

A specific approach of ML in AMR is by analyzing the suit-
able synergistic drugs for the development of possible combi-
natorial therapies. INDIGO (Inferring drug interactions using
chemogenomics and orthology) uses the best-fitted synergistic
drugs for the treatment of intra-abdominal infection using Gen-
tamycin -Ampicillin developed using model predictions [91].
Another refined model that is a bit different in application is
the MAGENTA (metabolism and genomics-based tailoring of
antibiotic regimens), which facilitates the treatment of biofilm
using combinatorial Rifampicin dosage [92]. Determination of
antimicrobial resistance using ML is now being used in various

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Mary et al., 2023 Machine Learning Mediated Advanced Phage and Antimicrobial Therapy - A Futuristic Approach

Figure 2. Machine learning algorithm utilized antibiotic and/or virulence gene databases to predict antimicrobial resistance, alternative therapies and its correspond-
ing side effects. The Machine learning algorithms can be used to develop personalized medicine and also can predict the synergistic effects of certain combinatorial
therapies.

sectors, including the first line of intensive care. ML is not only
limited to a targeted approach but also narrows down the frequent
use of multiple antibiotics under the same diseased conditions
[93]. For medical professionals, ML has now become the key
predictor for antimicrobial resistance, where ML-based tools are
taken into consideration for allowing the screening of the best-
suited antibiotics for the pathogens. Recursive partitioning is
applicable for the prediction-based analysis of ESBL production
from Klebsiella sp. and E. coli [94]. Modified recursive par-
titioning models are found to be advantageous for developing
the logistic regression models [95]. XGBoost, one of the openly
accessible platforms for ML-based algorithms, has guided the
prediction-based analysis of antibiotic resistance for some of
the gram-negative bacterial species [96]. The predicted perfor-
mance by XGBoost is better than other risk assessment tools;
however, the prediction model is a bit selective and applicable
to limited antibiotics. The ML system can also be futuristic for
determining the extent of antibiotic resistance during the culture
collection. ML would capture the empowerment for the upcom-
ing advancement of antimicrobials determination. ML enables
the development of personalized medicinal approaches to cut
down frequent uses of antibiotics. The Random Forest Classifier,
one of the most supervised ML algorithms, is allocated to accel-

erate the screening of MDR bacteria among the patients in ICU
[97]. The associated empirical data of the ML-based algorithm
can also be implemented for local antimicrobial susceptibility
assessment. Some of the ICU antimicrobial susceptibility-based
ML algorithms are Multilayer Perceptron and J48 [98].

As discussed earlier, the principles of ML-based systems are
i.) being trained from the existing datasets (supervised) and ii.)
analyzing or giving output based on unlabeled data (unsuper-
vised). Information regarding the underlying drug interactions
has to be made available for ML to learn and develop algorithms
for predicting the best combinatorial treatment approach. Primar-
ily, certain attributes like changes in gene expression, response
to the drugs, etc., need to be characterized before computerized
for decision-making [99]. Based on the available datasets of
drug information like chemical structure and function, the algo-
rithms are trained to determine the combinatorial therapy. Among
multiple algorithms, CoSynE (Combination Synergy Estimation)
governs the direct structural analysis for an individual drug can-
didate to analyze the overall combinatorial therapy. Basically, to
have knowledge of every aspect of the combinatorial compounds,
there should be prior data for the drug and the target as well [100].
With the CoSynE approach, the level of application can be ex-
tended towards diversified combinatorial drug designing against

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Mary et al., 2023 Machine Learning Mediated Advanced Phage and Antimicrobial Therapy - A Futuristic Approach

drug-resistant E. coli and also against malarial parasite Plasmod-
ium [99]. Through consistent records, the ML algorithms were
able to predict drugs to counteract antimicrobial resistance in
Mycobacterium and Salmonella [101]. The predictability is well-
curated to reframe the ML so that antibiotic resistance can lay
the foundation for the model-based prediction.

The extent of genetic variations and drug resistance can be
supervised using Machine Learning algorithms. The drug re-
sistance predictability is done so far in accordance with IC50
indicators. The various searches are based upon the retrieval of
different sequence databases. Tuberculosis, caused by M. tuber-
culosis, is one of the infectious diseases which could be eradi-
cated using antibiotics, but due to the dosage’s dysfunctionality
and random uses of antibiotics, it concludes with a highlighted
remark towards the development of MDR Tuberculosis [102].
Administration of fluoroquinolones is currently considered, but
the usage could lead to after effects due to reported toxicity. ML
predicts novel drugs, mechanistic performance of the drugs, mi-
croarray data construction and analysis, side effect prediction, etc.
Based upon the existing known dataset, ML-based algorithms
can display the futuristic blind prediction. The performance
based on the suggestive prediction can confer the right indication
towards clarifying the Single Nucleotide Variance followed by
the mutation within the genome of Mycobacterium tuberculosis.
Sequenced based algorithms and structure-based simulations em-
ploy the right directive approach to analyze the genomic variation,
so that suitability for selecting the target gene can be obtained.
Deep learning is efficient in predictable analysis to distinctively
diagnose the inherent antimicrobial peptide (AMP) to combat
antimicrobial resistance. Deep Learning was able to evaluate
AMPs as antimicrobial agents and predicted that AMP can sur-
pass MDR in carbapenemase-producing E. coli. Deep learning is
also capable of simulating the newer versions of AMPs from the
existing peptides [103]. With access to different databases like
ARGs, a number of Deep Learning models have been recruited so
far to deliberately screen the best-hit searches for antimicrobial
genes. A summary of the ML tools for antimicrobial resistance
and its associated factors is depicted in Table 1.

Machine Learning in phage therapy
Machine learning has arisen as a promising strategy in phage

therapy prediction, intending to discern the most effective bacte-
riophage that can selectively target distinct bacterial strains. Cur-
rently, various techniques exist to measure bacteriophage-host
interactions experimentally, such as PhageFISH-CLEM [113],
microfluidic digital multiplex PCR [114], flow cytometry [115],
agar overlay assay [116], RNA-sequencing [117], spot test, and
efficiency of plating [118]. While these methods are highly ac-
curate, they are costly, labor-intensive, time-consuming, and can
turn out to be inconclusive. To overcome these limitations, re-
searchers have developed high-precision computational methods
for predicting phage-host interactions. The initial phase in phage
therapy is the selection of suitable phage, characterized by three

indicators that include 1. Presence of temperate markers 2. Pres-
ence of anti-microbial genes and 3. Presence of Virulence Gene.
ML basically uses the whole phage genome and corresponding
proteome analysis, and from the sequence similarity and anal-
ysis of the conserved domains, determines whether the phage
undergoes lytic or lysogenic cycle. A recent approach has been
developed to determine the suitability of phage for therapeutic
purposes based on an online single-step predictor tool. This pre-
dictor tool utilizes the protein features such as integrase, Cro/CI
repressor protein, anti-repressor proteins, immunity repressors,
etc., along with ABRicat tools for determining the antibacterial
and virulence genes. Apart from ML, other computational tools
are also in use to identify host range and host-phage interactions.
The alignment-free and alignment-based techniques are widely
used tools, where the sequence homology and sequence similarity
among host and phage are read by computational tools to predict
its host range [119]. BLAST is a classic example of an alignment-
based method. While the alignment-based method (e.g., Phirbo)
is the most reliable strategy with the maximum prediction accu-
racy, certain factors like the prediction of multiple related hosts,
spurious alignment, and artifacts lead to comparable false results.
Alignment-free tools use similarity in the sequence composition
of codons, oligonucleotide frequencies, etc. This tool comes in
handy where the host and phage lack sequence homology, and
thus alignment-based tools are not suitable.

ML develops algorithms based on dataset creation (phage
genomes and its corresponding protein sequences), feature gen-
eration (custom scripts), and validation. Computational evalu-
ation precedes other methods by allowing the comparison in a
large dataset, especially advantageous for non-cultivable bacteria
and the availability of limited host strains. HostPhinder exam-
ines phage genome sequences to predict the bacterial host of
phage [120], VirHostMatcher measures CRISPR sequences and
alignment-free similarity to predict virus-host interaction [121],
WIsH outperforms VirHostMatcher at various taxonomic levels
and predicts the host range of bacteriophages through genome
sequences [122], Machine learning takes into consideration the
measurable properties referred to as features’. Nucleotide se-
quence is a characteristic feature and is used by ML algorithms
like Prokaryotic Virus Host Predictor (PHP) and Host Taxon Pre-
dictor (HTP) for phage-host interaction prediction. The absolute
relative oligonucleotide frequencies and a Gaussian model are
used to predict the host in HTP and PHP, respectively. Based on
the virus-host associations, ILMF-VH and LMFH-VH integrate
the virus and host similarity network [123], [124]. Leite’s method
uses a One-Class learning method to predict the host-viral interac-
tion at the bacterial strain level [125], SpacePHARER (CRISPR
Spacer PhageHost Pair Finder) predicts bacterial and viral in-
teractions at the protein level by comparing spacers and phage
[126]. On the other hand, VirSorter uses phage-host interaction
signals to predict the phage-host interaction [127], and Pred-
PHI (Predicting Phage- Host Interactions) predicts the prokar-
yote-phage interaction by sequence data [128]. PhageTB lever-

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Mary et al., 2023 Machine Learning Mediated Advanced Phage and Antimicrobial Therapy - A Futuristic Approach

Table 1. Methods, Tools & Algorithms, Model Organisms, Genes, Databases, Applications, and References

Method Tools & Algorithms used Model organ-

ism

Genes Database Applications Reference

Pan-genome con-

struction, Random

forest

Scoary, Prodigal, CD-HIT, Glim-

mer3, Naive-Bayes classifier

E. coli AMR genes (from

core and accessory

gene clusters)

PATRIC, CARD Analyses the accessory part of

the genome to predict ARGs

[104]

WGS, Antibiogram K-mer, Random forest, XGBoost S. aureus SCC mec genes PATRIC Predicts Antimicrobial Pheno-

type Resistance

[105]

Colony screening,

RNA sequencing,

DNA sequenc-

ing, SNP calling,

Pangenome analy-

sis and indel call-

ing, Multilocus

sequence typing

MSA2VCF, Illumina HiSeq 2500,

MAFFT, SAMtools, BamTools,

BCFtools

P. aeruginosa PA14 gene UCBPPPA14, MLST

database

ML identified biomarkers

assess AMR profiles through a

molecular test system

[106]

Phylogenomics,

and genome se-

quencing

mafft, raxml-ng, T-REX, TreeTime E. coli malT gene, manZ BiMat software Studies the phage-bacterial co-

evolution dynamics (molecular

and ecological mechanisms)

[107]

Oxford Nano-pore

Technologies, Sin-

gle Nucleotide Real

Time (SMRT)

REBASE, DECIPHER, Clustal

Omega

Cutibacterium

acnes

Mob genes, Res

gene

REBASE, NCBI Genome

database, PFAM database,

CRISPR-CAS Finder

Restriction-methylation and

host protective mechanisms in

C. acnes strains

[108]

Twitching motil-

ity assays, Swim-

ming motility as-

say. Drosophila

melanogaster

virulence assays.

Secreted enzyme as-

say. Antimicrobial

resistance assay.

Shearing assays.

MICROB Express kit, Agilent 2100

bioanalyzer, BEDTools software

v2.16.2.

P. aeruginosa Morons CLC Genomics Workbench

software v5.1

Bacterial and phage symbiotic

interaction, Bacterial adaptation

in various selective pressures

[109]

Mathematical

model using an

arbitrium-like com-

munication system

in a serial passage

set up.

Matlab R2017b Bacillus GitHub Small peptide mediated sig-

nal communication (phage-

phage),phage life cycle predic-

tion.

[110]

Antimicrobial sus-

ceptibility testing,

Statistical analysis

of multidrug resis-

tance, Association

set mining

SENTRY, Apriori S. aureus AMR genes Analysis of Multidrug Resis-

tance in Staphylococcus aureus

[111]

Serotyping, An-

tibiotic suscep-

tibility testing,

Set-covering ma-

chine, CMY-2 locus

analysis

Python libraries, SISTR, IQTree,

Prodigal v2.6.3, Kover v2.0.0,

BWA-MEM

Salmonella

enterica

AMR genes Plasmidfinder database,

DIAMOND v0.8.36

AMR genomic characterization

of Non-typhoidal Salmonella

serovars to train prediction

models for AMR phenotypes.

[112]

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Mary et al., 2023 Machine Learning Mediated Advanced Phage and Antimicrobial Therapy - A Futuristic Approach

ages accurately identifies hosts for bacteriophages using genomic
sequences [129].

These user-friendly tools enable the study of phage inter-
actions, identifying potential phages that can specifically tar-
get pathogenic bacteria, leaving beneficial bacteria intact. Ma-
chine learning-based approaches for phage therapy prediction
have shown great promise in identifying effective bacteriophages
for targeted treatment of bacterial infections. These methods
offer faster and more cost-effective alternatives to traditional
experimental techniques. However, challenges in data avail-
ability, model selection, evaluation metrics, interpretability, and
user-friendliness must be addressed for their successful imple-
mentation in clinical settings [130]. Additionally, tools like
PHACTS (Phage Classification Tool Set) and BACPHLIP (BAC-
terioPHage LIfestyle Predictor) are widely used for determining
phage lifestyle by annotating proteomes and classifying lifestyles
based on conserved protein domains [131], [132].

BacteriophageHostPrediction is one other ML which con-
siders more features, including genomic sequences, protein se-
quences, protein secondary structures, and physiochemical prop-
erties. PHERI identifies bacterial host from phage sequence
through annotated protein sequence clusters. PhageLeads are
ML that focuses on predicting the lifestyle of phage (lytic or
temperate) using protein features of temperate markers. The
quick prediction times of PhageLeads make it a valuable tool
for efficiently identifying phages suitable for combating antibi-
otic resistance, and its ability to detect resistance and virulence
genes further enhances its utility [133]. Incorporating protein
biological feature spaces may further enhance the functional sim-
ilarities predictions [134]. Several databases like CARD [135],
ShortBred AR [136], MEGARes [137], and VFDB [138] aid in
detecting these genes. By overcoming these challenges, machine
learning-based phage therapy prediction holds significant poten-
tial in combating antibiotic resistance and improving treatment
outcomes for bacterial infections. After the development of ML,
the usability is determined by several factors such as operating
system restriction, automation, and reproducibility. PHP, Host-
Phinder are web-based prediction tools that are also available,
which do not require a specific OS. Table 2 summarizes the ML
algorithms developed for phage research.

Databases in antimicrobial resistance and phage re-

search
In silico approaches for omics studies have become more

widely available, which has in turn made it feasible to precisely
recognize and catalog determinants in the case of antibiotic re-
sistance and its associated genes. AMR archives, and software
applications tools have been constructed for WGS-AST based
on the available databases, which includes the Comprehensive
Antibiotic Resistance Database (CARD), ResFinder and its com-
panion database PointFinder, ARG-ANNOT, and many more
(Table 3) [139]. The CARD offers an informatics paradigm for
the notation and assessment of resistomes through integrating

the Antibiotic Resistance Ontology (ARO) [140]. CARD is a
database on the molecular basis of antimicrobial resistance that
is ontology-focused. CARD has the potential to serve as both
reference material and software instruments and resources for
directing AMR investigation, particularly for ARG details and
other findings from genomic information and metagenomic facts.
This is made possible through the combination of an extensive
modulated concept of ARO, with ARG [141]. The Resistance
Gene Identifier (RGI), anticipates AMR from genome-wide facts
and data using the bioinformatics prediction approaches and
coordinated in CARD, and is a sophisticated strategy [142]. AM-
RFinderPlus, a tool of NCBI, analyzes protein annotations and/or
gathered nucleotide sequence to find AMR genes, resistance-
linked point mutations, and particular subclasses of genes. The
Pathogen Detection operations make use of AMRFinderPlus, and
these data are shown in NCBI’s Isolate Browser [143]. AMRFind-
erPlus makes use of the carefully selected Hidden Markov Mod-
els and Reference Gene Database from the NCBI. The NCBI’s
Pathogen Detection Project incorporates the output of AMRFind-
erPlus to quickly group and locate associated pathogenic genetic
patterns residing in food, environment, and people with illnesses.
MicroBIGG-E (genomic data) together with the AMRFinderPlus;
findings are provided in a more thorough manner, with extra data
such as strain names and source of isolates [144]. In AMRFind-
erPlus the outcomes are provided for download by the users in
two interfaces graphics [139]. Each unique isolate has a synopsis
of its antimicrobial resistance, stress-related responses, and vir-
ulence gene sequence generated in the Isolates Browser, which
may also be retrieved for more research.

Challenges and Opportunities
The use of machine learning in phage therapy faces several

limitations and challenges. The future of health care system will
be influenced by the intervention of AI and ML from organiza-
tion to personalized precision care. The input of ML will also
positively impact the diagnosis systems. One major obstacle
is the need for diverse and unbiased datasets to train predic-
tive models. Current datasets often suffer from data imbalance,
leading to biased predictions and limited generalization to other
bacterial species. Enriching a dataset with a broader range of
phage-bacterium pairs involving different species and strains is
essential for improving model accuracy and applicability [147].
Another limitation lies in the reliance on outdated databases to
extract informative features that affect model relevance and per-
formance. Incorporating up-to-date and comprehensive databases
is crucial for generating more reliable predictions. Addition-
ally, using deep learning models while achieving high accuracy
presents challenges in model interpretability. These models are
often considered "black boxes," creating challenges for users
to comprehend the underlying decision-making process [152].
Balancing accuracy and interpretability is crucial for user trust
and the real-world application of the models. The computational
module for predicting phage lifestyle faces the limitation of uncer-

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Mary et al., 2023 Machine Learning Mediated Advanced Phage and Antimicrobial Therapy - A Futuristic Approach

Table 2. Machine Learning based tools to aid bacteriophage research.

Machine learning predictors Purpose of ML Tools used Major data sources - Algorithms Predicting protein Efficiency Applications References

Gradient Boosting Classifier

(GBC)

Predict phage virion protein (PVPs)

using phage protein sequences.

protlearn AdaBoost Classifier (ABC), Virion proteins
80 & 83 % accuracy for training

and independent dataset.

Discovery of holins and other

phage-driven proteins,

endolysins, and exopolysaccharides

Identification

of phage virion proteins (PVPs)

[145]

PhageLeads

Determines the presence of

temperate markers, antimicrobial

resistance virulence genes

ABRicate tool,BACPHLIP Plasmid Finder
Integrases, Cro/CI repressor

proteins, immunity repressors

accuracy of 96.5%

Predicts the lifestyle of

phageDetects lysogenic protein

and temperate markers

[146]

PHACTS
Predicts the interaction of

phage-bacterium

HostPhinder,K-Nearest

Neighbors (K-NN),

Random Forests (RF)],

Support Vector Machines (SVM) ,

and Artificial Neural Networks(ANN)

GenBank,

phagesDB.org,

GeneMarkS,

DOMINE,

GeneMarkS,

Pfam HMM,

HMMER API

Receptor-binding proteins 86% to 90% accuracy Predicts phage-host interaction [147]

PhageTB,

BLASTHost,

BLASTPhage ,

CRISPRPred .

Predicts phage-host interactions VirHostMatcher-Net XGBoost, Multi-layer Perceptron Accuracy of 67.9- 93.5%

Identifying hosts, assessing

phage-host interactions,

aids candidate phage therapy

[129]

AcrNET Anti-CRISPR analysis

RaptorX,ESM-1b,POSSUM,

PaCRISPR,

AcRanker,DeepAcr,

Hidden Markov Model (HMM) ,

MEME

anti-CRISPRdb,

PaCRISPR,

Acrs database,

UniProt,UniParc,

AlphaFold

Acr protein,anti-CRISPR proteins. -
Determining Anti CRISPR from

the large-scale protein database

[148]

PredPHI
Identification of phage-host

interactions from sequence data

K-Means clustering PhagesDB ,GenBank, 81.00%
Development of personalized

treatment for bacterial infections.

[149]

Cryo-electron tomography

(cryo-ET)

Phage based therapy against

K. pneumoniae strains.

ChimeraX,HMMER,HHPred,Phyre2 Capsid and tail fibers of phage.
Receptor-binding proteins,

protein gp118,protein gp119

IMOD,RoseTTAFold

Functional insight into Kp24

adaptation to

variable surfaces of capsulated

bacterial pathogens

[150]

Pred-BVP-Unb
Identification of BVPs within

a huge volume of proteins

MATLAB tool,ADBoost, KNN, Universal Protein Resource Bacteriophage virion proteins

92.54% & 83.06% accuracy

on benchmark and independent

datasets

Designing antibacterial drugs Expedite

discovery of BVP

[151]

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Mary et al., 2023 Machine Learning Mediated Advanced Phage and Antimicrobial Therapy - A Futuristic Approach

Table 3. List of accessible databases for antimicrobial / phage resistant genes, receptor sequences, genomic sequences and SNPs.

Databases Type of Input Sequence Format of the
input sequence

Purpose Developed/
Maintained by

Supporting Tools/
DB

Link of the website

Comprehensive Antibiotic
Resistance Database
(CARD)

Nucleotide,
Amino acid

FASTA SNPs, validated and curated reference sequencing details and
molecular foundation of antimicrobial resistance

NCBI RGI https://card.mcmaster.ca
/home

AMRFinder Nucleotide, Amino acid FASTA, GFF Identifies AMR genes & resistance-associated point mutations NCBI - https://www.ncbi.nlm.ni
h.gov/pathogens/antimic
robial-
resistance/AMRFinder/

Pathosystems Resource
Integration Centre
(PATRIC)

Nucleotide, Amino acid FASTA Large database of infectious bacterial genomic information BRCs by NIAID - http://www.patricbrc.org

MEGARes Nucleotide FASTQ A metagenomics dataset consisting of AMR genes that are
identified, characterized and evaluated.

- AMR++
(Bioinformatics
pipeline), CARD,
ARG-ANNOT,
LAHEY

https://megares.meglab.
org/

ResFinder Nucleotide FASTA, FASTQ Imports assembled contigs, data sequences or completed
genomes and detects AMR genes.

- web-based portal
and Python script

https://cge.cbs.dtu.dk/ser
vices/ResFinder/

Food and Environment
associated Anti-Microbial
Resistance Database
(FEAMR DB)

- - Nonclinical test results as well as dietary and surroundings
related AMR data worldwide

Antimicrobial
Research Lab,
Department of
Biotechnology,
University of
Mumbai

CARD, NDARO,
MegaRes, NCBI

https://feamrudbt-
amrlab.mu.ac.in/

National Databases of Antibiotic
Resistant Organisms
(NDARO)

- - A co-operative, centrally controlled, cross-agency center where
researchers may obtain AMR data to enable real-time pathogen
monitoring.

NCBI AMRFinder -

Functional Antibiotic Resistant
Metagenomic Element Database
(FARMEDB)

Nucleotide, Amino acid FASTA It explores functional metagenomics antibiotic
resistant genetic variables, serves as an opportunity for
investigating AR in the majority of bacteria that are difficult to
culture in lab. It serves as a repository for globally available
antibiotic resistance linked genome sequences, predicted amino
acid sequences, regulatory factors, jumping genes, and
predicted peptides associated with antibiotic resistant genes.

University of
Washington

- http://staff.washington.e
du/jwallace/farme/index.
html

LREfinder Nucleotide FASTA, FASTQ Offers information on the genes and alterations that cause
enterococci to become tolerant to linezolid.

Centre for Genomic
Epidemiology

- https://cge.cbs.dtu.dk/ser
vices/LRE-finder/

Galileo AMR Nucleotide FASTA Offers quick and precise labelling of genes associated with
AMR for any DNA fragment of gram-negative bacteria.

Arc Bio MARA, RAC https://galileoamr.arcbio
.com/mara/

ARG-miner - - Gathersandaccessallof thedata fromvariousARGresources. Itoffers
proofofARGs,specificallyplasmids,viruses,orprophages, thatcanbe
carriedbyMGEs.

- ARDB, ARG-
ANNOT,
MEGARes, CARD,
NDARO,
ResFinder, UniProt,
PATRIC

https://bench.cs.vt.edu/a
rgminer/#/home

ShortBRED Amino acid FASTA Facilitates the highly selective characterization of target protein
families and AMR genes in shotgun metagomics sequence
reading information.

The Huttenhower
Lab, Department of
Biostatistics,
Havard T.H. Chan
School of Public
Health

ARDB, CARD http://huttenhower.sph.h
arvard.edu/shortbred

Comprehensive β-
lactamase Molecular Annotation
Resource
(CBMAR)

Nucleotide, Amino acid FASTA The approach groups beta-lactamases into classes and then
subsequently subgroups them based on factors such as gene
location, phylogenetic relationships, active site, parent
fingerprints, mutational characteristics, antibiotic resistance
characteristics, blocker vulnerability, and nucleotide diversity.

- LAHEY, PDB,
UniProt,
GeneBank, LacED,
ARBD,

http://proteininformatics
.org/mkumar/lactamased
b/

DeepARG Nucleotide, Amino acid FASTA, FASTQ It makes highly confident predictions about ARGs from quick
reads and complete gene length sequences based on
metagenomic study of environmental sources.

- ARDB, CARD,
UniProt

https://bench.cs.vt.edu/d
eeparg

INTEGRALL Nucleotide FASTA gathers and arranges the integrons' data - - https://integrall.bio.ua.
ppt/?

Phage Receptor Database
(PhReD)

- FASTQ, SCF It gathers bacterial receptors that are necessary for phage-host
identification and interacting associations.

Bio-Conversion
Databank
Foundation

- -

The Actinobacteriophage
Database

- - It sequences, identities, defines and characterizes
Mycobacteriophages.

Department of
Biologicals
Sciences at the
University of
Pitsburg

- https://phagesdb.org/

Beta-lactamase database (BLDB)
Lahey Clinic database

Nucleotide - It collects architectural and biochemical characteristics, along
with sequencing data, for every known BL. It describes the
genetic factors that give resistance to betalactam substances.

Part of Bacterial
Antimicrobial
Resistance
Reference Gene
Database, NCBI

PDB http://bldb.eu

http://www.lahey.org/St
udies/

Antibacterial Biocide and Metal
Resistance Genes Database
(BacMet)

Nucleotide, Amino acid FASTA It aims to target genetic factor that provide resistance to metal-
based substances and biocidal compounds. -

- http://bacmet.biomedici
ne.gu.se/

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Mary et al., 2023 Machine Learning Mediated Advanced Phage and Antimicrobial Therapy - A Futuristic Approach

tain predictions owing to the variability from random sampling
during classification. Continuously expanding the phage lifestyle
database to include more known phage lifestyles will enhance
prediction precision and sensitivity [153]. Furthermore, achiev-
ing high-precision rates for complex classification schemes, such
as host strains and phage families, remains challenging [132].
Addressing these challenges requires refining the methodology
and exploring novel classification methods. It is necessary to
update the tool with new features and experimentally validated
data to improve its accuracy and reliability [154]. The insuffi-
cient data on ssDNA and RNA phage is a significant limitation
attributed to experimental constraints and resulted in the lack
of respective genomic data in databases. Extensive research on
phage clades and its diversity can bring much revolution in the
collection of genetic information and databases [155; 156]. Fur-
thermore, predicting phage-host relationships in complex micro-
bial environments remains challenging because current methods
often demand large amounts of homogeneous data. Develop-
ing more data-efficient techniques, such as similarity networks
and machine learning, is crucial for accurate predictions [152].
Despite advances in computational methods, identification of
phage-host interactions remains a central challenge for effective
phage therapy. The application of deep learning techniques has
shown promise, but their lack of interpretability hinders user un-
derstanding. Improving model interpretability while maintaining
predictive performance is necessary for practical implementation.
Integrating sequence similarity information and exploring novel
features for phage-host interaction pairs could enhance model
robustness and accuracy [129]. In conclusion, addressing the
limitations and challenges of using machine learning in phage
therapy, including dataset bias, outdated databases, model inter-
pretability, and the need for continuous updates, will be pivotal
in developing personalized therapies against antibiotic-resistant
bacterial infections and in advancing the field of phage therapy.

Acknowledgements
I extend my sincere gratitude to the Central University of

Tamil Nadu for its profound support for the successful completion
of the manuscript.

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Highlights in BioScience Page 18 of 18 December 2023|Volume 6

http://bioscience.highlightsin.org/

	Abstract
	Background
	Phage therapy
	Antimicrobial and phage resistance
	Machine learning

	Machine Learning and Antimicrobial Resistance 
	Machine Learning in phage therapy
	Databases in antimicrobial resistance and phage research
	Challenges and Opportunities
	Acknowledgements

