Antimicrobial resistance and machine learning: past, present, and future

Farhat, Faiza; Athar, Md Tanwir; Ahmad, Sultan; Madsen, Dag Øivind; Sohail, Shahab Saquib

doi:10.3389/fmicb.2023.1179312

Cited by 7 publications

(5 citation statements)

References 44 publications

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“…Various indicators have been used in the literature for bibliometric analysis, including total article count, average citations per article (ACPA), total citation count, total link strength, and Hirsch index (Hindex). These metrics are commonly used in bibliometric studies, with the H-index being a widely recognized measure of research quality and quantity for authors and research areas (Farhat et al, 2023a). ACPA is also widely accepted as a measure of research impact for individual works, authors, and publication outlets.…”

Section: Methodology Data Extractionmentioning

confidence: 99%

“…One such notable AI language model is ChatGPT (https://openai.com/chatgpt), an autoregressive language model that uses deep learning techniques to generate coherent and contextually relevant responses to user inputs. Since its launch, ChatGPT has gained significant attention and adoption in various domains, includinrg content generation, healthcare, education, data science, accounting, finance, tourism, and customer support/assistance (Carvalho and Ivanov, 2023;Dowling and Lucey, 2023;Dwivedi et al, 2023a,b;Gupta et al, 2023;Ray, 2023;Sallam, 2023;Sohail et al, 2023a;Wood et al, 2023). The introduction of ChatGPT has also sparked discussions and debates surrounding its potential implications across various domains (Baumgartner, 2023;Ivanov and Soliman, 2023;Lo, 2023).…”

Section: Introductionmentioning

confidence: 99%

“…Notably, issues related to ethical considerations and biases (Ray, 2023;Sohail et al, 2023b) and the impact of large language models on knowledge assessment (Farhat et al, 2023b;Gilson et al, 2023) have garnered attention in recent discourse. Scholarly investigation of ChatGPT has emerged as a critical area of research, aiming to understand its impact, applications, and future directions (Dave et al, 2023;Gupta et al, 2023;Ray, 2023;Roumeliotis and Tselikas, 2023;Sohail et al, 2023a).…”

Section: Introductionmentioning

confidence: 99%

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The scholarly footprint of ChatGPT: a bibliometric analysis of the early outbreak phase

Farhat,

Silva,

Hassani

et al. 2024

Front. Artif. Intell.

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This paper presents a comprehensive analysis of the scholarly footprint of ChatGPT, an AI language model, using bibliometric and scientometric methods. The study zooms in on the early outbreak phase from when ChatGPT was launched in November 2022 to early June 2023. It aims to understand the evolution of research output, citation patterns, collaborative networks, application domains, and future research directions related to ChatGPT. By retrieving data from the Scopus database, 533 relevant articles were identified for analysis. The findings reveal the prominent publication venues, influential authors, and countries contributing to ChatGPT research. Collaborative networks among researchers and institutions are visualized, highlighting patterns of co-authorship. The application domains of ChatGPT, such as customer support and content generation, are examined. Moreover, the study identifies emerging keywords and potential research areas for future exploration. The methodology employed includes data extraction, bibliometric analysis using various indicators, and visualization techniques such as Sankey diagrams. The analysis provides valuable insights into ChatGPT's early footprint in academia and offers researchers guidance for further advancements. This study stimulates discussions, collaborations, and innovations to enhance ChatGPT's capabilities and impact across domains.

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Section: Methodology Data Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The scholarly footprint of ChatGPT: a bibliometric analysis of the early outbreak phase

Farhat,

Silva,

Hassani

et al. 2024

Front. Artif. Intell.

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“…A recent study revealed a substantial increase in publications and research on the use of machine learning in the field of antimicrobial resistance [8]. These methods have broad applications in microbiology, from diagnostics, drug and vaccine discovery and applications in epidemiology [1,3,9].…”

Section: Introductionmentioning

confidence: 99%

Tackling the Antimicrobial Resistance “Pandemic” with Machine Learning Tools: A Summary of Available Evidence

Rusic,

Kumric,

Seselja Perisin

et al. 2024

Microorganisms

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Antimicrobial resistance is recognised as one of the top threats healthcare is bound to face in the future. There have been various attempts to preserve the efficacy of existing antimicrobials, develop new and efficient antimicrobials, manage infections with multi-drug resistant strains, and improve patient outcomes, resulting in a growing mass of routinely available data, including electronic health records and microbiological information that can be employed to develop individualised antimicrobial stewardship. Machine learning methods have been developed to predict antimicrobial resistance from whole-genome sequencing data, forecast medication susceptibility, recognise epidemic patterns for surveillance purposes, or propose new antibacterial treatments and accelerate scientific discovery. Unfortunately, there is an evident gap between the number of machine learning applications in science and the effective implementation of these systems. This narrative review highlights some of the outstanding opportunities that machine learning offers when applied in research related to antimicrobial resistance. In the future, machine learning tools may prove to be superbugs’ kryptonite. This review aims to provide an overview of available publications to aid researchers that are looking to expand their work with new approaches and to acquaint them with the current application of machine learning techniques in this field.

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“…Indeed, the emergence of bioinformatics has resulted in a significant change across a wide range of academic disciplines, including antibiotic drug discovery (Farhat et al 2023). The authors highlight the accelerated and efficient nature of the in-silico methodology, which is reshaping modern drug discovery, including antibiotic discovery.…”

Section: Introductionmentioning

confidence: 99%

Antimicrobial and FAD synthetases modulating activities of leporins A-C isolated from the sponge-associated fungus Trichoderma sp.

LOSUNG,

GINTING,

ABDJUL

et al. 2023

Biodiversitas

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Abstract. Losung F, Ginting EL, Abdjul B, Kapojos MM, Maarisit W, Mentang F, Sumilat DA, Balansa W, Mangindaan REP. 2023. Antimicrobial and FAD synthetases modulating activities of leporins A-C isolated from the sponge-associated fungus Trichoderma sp. Biodiversitas 24: 6502-6515. The emergence of microbial resistance poses a formidable threat to human health, requiring the discovery of new antibiotics. In this study, we investigated the antimicrobial potential and molecular structures of the metabolites produced by a sponge's symbiont fungal species, Trichoderma sp., in vitro against S. aureus IAM 12544T and Candida albicans IFM 4954 and in-silico against the emerging antibacterial target, prokaryotic bifunctional synthetases (FADS). The molecular structures were determined using spectroscopic techniques (1D, 2D NMR, HRESIMS), while the assessment of biological activities, physicochemical properties, and molecular modifications was performed through a slightly modified disk agar diffusion method, molecular docking, SwissAdme and pkCMS tools, and bioisosterism, respectively. The analysis of spectroscopic data supported the identification of leporins A-C (1-3) as the metabolites, which exhibited strong binding affinities against the 2X0K protein target (-8.9 to -9.4 kcal/mol). Despite their being slightly weaker than known FADS modulators such as compounds 4 (-10.5 kcal/mol) and 5 (-10.5 kcal/mol), leporins A-C demonstrated a stronger binding affinity than compound 6 (-9.6 to -10.5 kcal/mol). Notably, substituting a methyl group with a fluorine atom in 1-3 resulted in lepofluorins A-C (1a-3a), which exhibited enhanced binding affinities and improved physicochemical properties compared to the existing FADS modulators. These findings suggest that leporins A-C (1-3), particularly lepofluorins A-C (1a-3a), have potential as putative novel modulators of FADS. This study provides valuable insights into the design and development of new antibiotics to combat microbial resistance.

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Antimicrobial resistance and machine learning: past, present, and future

Cited by 7 publications

References 44 publications

The scholarly footprint of ChatGPT: a bibliometric analysis of the early outbreak phase

The scholarly footprint of ChatGPT: a bibliometric analysis of the early outbreak phase

Tackling the Antimicrobial Resistance “Pandemic” with Machine Learning Tools: A Summary of Available Evidence

Antimicrobial and FAD synthetases modulating activities of leporins A-C isolated from the sponge-associated fungus Trichoderma sp.

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