Prediction of antimicrobial minimal inhibitory concentrations for Neisseria gonorrhoeae using machine learning models

Yasir, Muhammad; Karim, Asad Mustafa; Malik, Sumera Kausar; Bajaffer, Amal A.; Azhar, Esam I.

doi:10.1016/j.sjbs.2022.02.047

Cited by 23 publications

(21 citation statements)

References 37 publications

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“… 2022 ; Yasir et al. 2022 ) has given the best prediction results of 79.94 µg/m , 46.36 µg/m , and 8.86 ppb for PM2.5, PM10, and NO2, respectively. The average pollutant levels recorded during the pre-lockdown period (measured from Feb. 2019 to Feb 2020) for PM2.5, PM10, and NO2 are 99.96 µg/m , 58.65 µg/m , and 18.17 ppb.…”

Section: Discussionmentioning

confidence: 98%

Machine learning analysis on the impacts of COVID-19 on India’s renewable energy transitions and air quality

Stephan

Al‐Turjman²,

Wang

et al. 2022

Environ Sci Pollut Res

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India is severely affected by the COVID-19 pandemic and is facing an unprecedented public health emergency. While the country’s immediate measures focus on combating the coronavirus spread, it is important to investigate the impacts of the current crisis on India’s renewable energy transition and air quality. India’s economic slowdown is mainly compounded by the collapse of global oil prices and the erosion of global energy demand. A clean energy transition is a key step in enabling the integration of energy and climate. Millions in India are affected owing to fossil fuel pollution and the increasing climate heating that has led to inconceivable health impacts. This paper attempts to study the impact of COVID-19 on India’s climate and renewable energy transitions through machine learning algorithms. India is observing a massive collapse in energy demand during the lockdown as its coal generation is suffering the worst part of the ongoing pandemic. During this current COVID-19 crisis, the renewable energy sector benefits from its competitive cost and the Indian government’s must-run status to run generators based on renewable energy sources. In contrast to fossil fuel-based power plants, renewable energy sources are not exposed to the same supply chain disruptions in this current pandemic situation. India has the definite potential to surprise the global community and contribute to cost-effective decarbonization. Moreover, the country has a good chance of building more flexibility into the renewable energy sector to avoid an unstable future.

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Section: Discussionmentioning

confidence: 98%

Machine learning analysis on the impacts of COVID-19 on India’s renewable energy transitions and air quality

Stephan

Al‐Turjman²,

Wang

et al. 2022

Environ Sci Pollut Res

View full text Add to dashboard Cite

show abstract

“…In case of failure to identify resistance, it might elicit administration of suboptimal or ineffective antibiotic treatment and this constitutes a substantial risk, particularly in terminally ill people, and has dire consequences for patients. Overuse can also be a result to resistance and the unnecessary use of broad-spectrum antibiotics can be the consequence of resistance [ 1 ]. This puts patients at higher risks with severe side effects and drives hospital costs., and it can also substantially contribute to the development of drug resistance by applying undesired selective pressures [ 13 , 14 ].…”

Section: Discussionmentioning

confidence: 99%

“…The idea can be of value if the information on GEXP is added as a fail-safe strategy [ 15 ]. To acquire maximum mean prediction accuracy, regression models were also used on the same samples; nevertheless, the mean accuracy and performance of classification models was more reliable than regression models [ 1 , 16 , 17 ].…”

Section: Discussionmentioning

confidence: 99%

“…Increased resistance in pathogens toward antimicrobials is a major threat to public health and development. Increasing resistance hinders the usage of traditional antimicrobials and leads to higher rates of ineffective and unsuccessful empiric antibiotic therapy [ 1 ]. If not effectively cured, diseases give rise to suffering, indisposition, and finally loss of life, and impose a huge economic burden on society and healthcare systems.…”

Section: Introductionmentioning

confidence: 99%

“…Regardless of growing medical need, the antibiotic pipeline has slowed to a trickle over the last 20 years. Disturbingly, there are merely a few antimicrobial agents in the development phase to treat infections caused by multidrug-resistant pathogens [ 1 ].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Application of Decision-Tree-Based Machine Learning Algorithms for Prediction of Antimicrobial Resistance

Yasir

Karim²,

Malik³

et al. 2022

Antibiotics

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Timely and efficacious antibiotic treatment depends on precise and quick in silico antimicrobial-resistance predictions. Limited treatment choices due to antimicrobial resistance (AMR) highlight the necessity to optimize the available diagnostics. AMR can be explicitly anticipated on the basis of genome sequence. In this study, we used transcriptomes of 410 multidrug-resistant isolates of Pseudomonas aeruginosa. We trained 10 machine learning (ML) classifiers on the basis of data on gene expression (GEXP) information and generated predictive models for meropenem, ciprofloxacin, and ceftazidime drugs. Among all the used ML models, four models showed high F1-score, accuracy, precision, and specificity compared with the other models. However, RandomForestClassifier showed a moderate F1-score (0.6), precision (0.61), and specificity (0.625) for ciprofloxacin. In the case of ceftazidime, RidgeClassifier performed well and showed F1-score (0.652), precision (0.654), and specificity (0.652) values. For meropenem, KNeighborsClassifier exhibited moderate F1-score (0.629), precision (0.629), and specificity (0.629). Among these three antibiotics, GEXP data on meropenem and ceftazidime improved diagnostic performance. The findings will pave the way for the establishment of a resistance profiling tool that can predict AMR on the basis of transcriptomic markers.

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Predicting S. aureus antimicrobial resistance with interpretable genomic space maps

Pikalyova,

Orlov,

Horvath

et al. 2024

Molecular Informatics

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Increasing antimicrobial resistance (AMR) represents a global healthcare threat. To decrease the spread of AMR and associated mortality, methods for rapid selection of optimal antibiotic treatment are urgently needed. Machine learning (ML) models based on genomic data to predict resistant phenotypes can serve as a fast screening tool prior to phenotypic testing. Nonetheless, many existing ML methods lack interpretability. Therefore, we present a methodology for visualization of sequence space and AMR prediction based on the non‐linear dimensionality reduction method – generative topographic mapping (GTM). This approach, applied to AMR data of >5000 S. aureus isolates retrieved from the PATRIC database, yielded GTM models with reasonable accuracy for all drugs (balanced accuracy values ≥0.75). The Generative Topographic Maps (GTMs) represent data in the form of illustrative maps of the genomic space and allow for antibiotic‐wise comparison of resistant phenotypes. The maps were also found to be useful for the analysis of genetic determinants responsible for drug resistance. Overall, the GTM‐based methodology is a useful tool for both the illustrative exploration of the genomic sequence space and AMR prediction.

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Prediction of antimicrobial minimal inhibitory concentrations for Neisseria gonorrhoeae using machine learning models

Cited by 23 publications

References 37 publications

Machine learning analysis on the impacts of COVID-19 on India’s renewable energy transitions and air quality

Machine learning analysis on the impacts of COVID-19 on India’s renewable energy transitions and air quality

Application of Decision-Tree-Based Machine Learning Algorithms for Prediction of Antimicrobial Resistance

Predicting S. aureus antimicrobial resistance with interpretable genomic space maps

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