A convolutional neural network highlights mutations relevant to antimicrobial resistance in Mycobacterium tuberculosis

Green, Anna G.; Yoon, Chang Ho; Chen, Michael L; Ektefaie, Yasha; Fina, Mack; Freschi, Luca; Gröschel, Matthias I.; Kohane, Isaac S.; Beam, Andrew L.; Mustafa, Farhat

doi:10.1038/s41467-022-31236-0

Cited by 35 publications

(44 citation statements)

References 61 publications

(54 reference statements)

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“…Therefore, it is important to select models carefully, considering their appropriateness in terms of identification and interpretation. For instance, the authors in [ 38 , 60 , 61 , 62 , 63 , 64 , 65 , 66 ] used deep-learning and machine-learning models to identify different antibiotics. The authors in [ 38 ] used traditional machine learning and CNN to rapidly predict tuberculosis drug resistance accurately from genome sequences.…”

Section: Artificial Intelligence (Dl/ml) For Antimicrobial Resistancementioning

confidence: 99%

“…The authors in [ 38 ] used traditional machine learning and CNN to rapidly predict tuberculosis drug resistance accurately from genome sequences. For instance, in [ 60 ], mutations relevant to antimicrobial resistance in Mycobacterium tuberculosis are highlighted by a convolutional neural network. Interesting antibiotics were discovered by the authors in [ 61 ] using machine-learning models.…”

Section: Artificial Intelligence (Dl/ml) For Antimicrobial Resistancementioning

confidence: 99%

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Artificial Intelligence for Antimicrobial Resistance Prediction: Challenges and Opportunities towards Practical Implementation

2023

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Antimicrobial resistance (AMR) is emerging as a potential threat to many lives worldwide. It is very important to understand and apply effective strategies to counter the impact of AMR and its mutation from a medical treatment point of view. The intersection of artificial intelligence (AI), especially deep learning/machine learning, has led to a new direction in antimicrobial identification. Furthermore, presently, the availability of huge amounts of data from multiple sources has made it more effective to use these artificial intelligence techniques to identify interesting insights into AMR genes such as new genes, mutations, drug identification, conditions favorable to spread, and so on. Therefore, this paper presents a review of state-of-the-art challenges and opportunities. These include interesting input features posing challenges in use, state-of-the-art deep-learning/machine-learning models for robustness and high accuracy, challenges, and prospects to apply these techniques for practical purposes. The paper concludes with the encouragement to apply AI to the AMR sector with the intention of practical diagnosis and treatment, since presently most studies are at early stages with minimal application in the practice of diagnosis and treatment of disease.

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Section: Artificial Intelligence (Dl/ml) For Antimicrobial Resistancementioning

confidence: 99%

Section: Artificial Intelligence (Dl/ml) For Antimicrobial Resistancementioning

confidence: 99%

Artificial Intelligence for Antimicrobial Resistance Prediction: Challenges and Opportunities towards Practical Implementation

2023

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“…At https://tb-ml.github.io/tb-ml-containers/ , we provide example Docker containers for pre-processing and resistance prediction on M.tuberculosis data. So far, they include several neural networks (including one created by Green et al , 2022 and a variation which is independent of the dimensionality of the input data), one random forest model, and four pre-processing pipelines to generate input data for the models from either raw or aligned reads.…”

Section: Tb-ml Functionality and Implementationmentioning

confidence: 99%

TB-ML—a framework for comparing machine learning approaches to predict drug resistance ofMycobacterium tuberculosis

Libiseller-Egger

Wang

Deelder

et al. 2023

Bioinformatics Advances

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Motivation Machine learning (ML) has shown impressive performance in predicting antimicrobial resistance (AMR) from sequence data, including for Mycobacterium tuberculosis, the causative agent of tuberculosis. However, current ML development and publication practices make it difficult for researchers and clinicians to use, test, or reproduce published models. Results We packaged a number of published and unpublished ML models for predicting AMR of M. tuberculosis into Docker containers. Similarly, the pipelines required for pre-processing genomic data into the formats required by the models were also packaged into separate containers. By following a minimal container I/O standard we ensured as much interoperability as possible. We also created a command-line application, TB-ML, which can be used to easily combine pre-processing and prediction containers into complete pipelines ready for predicting resistance from novel, raw data with a single command. As long as there is adherence to this minimal standard for the container interface, containers produced by researchers holding new models can likewise be included in these pipelines, making benchmark comparisons of different models simple and facilitating faster uptake in the clinic. Availability and implementation TB-ML contains a simple Docker API written in Python and is available at https://github.com/jodyphelan/tb-ml. Example Docker containers for resistance prediction and corresponding data pre-processing as well as a tutorial on how to create new containers for TB-ML are available at https://tb-ml.github.io/tb-ml-containers/.

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“…Another study retrieved 222 prominent features for resistance prediction using the Multi-task Wide and Deep Neural Network with fastq files obtained from whole genome sequencing which showed high efficacy. This was followed by development of another model—Wide and Deep Neural Network model, with still better accuracy ( Green et al, 2022 ) ( Sharma et al, 2022 ). Other algorithms used for this purpose and which showed high accuracy include Classification Trees and Gradient Boosted Trees, which was used to unravel new mutations which can concur resistance to fourteen drugs.…”

Section: Introductionmentioning

confidence: 99%

Advances in computational frameworks in the fight against TB: The way forward

Naidu

Nayak

et al. 2023

Front. Pharmacol.

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Around 1.6 million people lost their life to Tuberculosis in 2021 according to WHO estimates. Although an intensive treatment plan exists against the causal agent, Mycobacterium Tuberculosis, evolution of multi-drug resistant strains of the pathogen puts a large number of global populations at risk. Vaccine which can induce long-term protection is still in the making with many candidates currently in different phases of clinical trials. The COVID-19 pandemic has further aggravated the adversities by affecting early TB diagnosis and treatment. Yet, WHO remains adamant on its “End TB” strategy and aims to substantially reduce TB incidence and deaths by the year 2035. Such an ambitious goal would require a multi-sectoral approach which would greatly benefit from the latest computational advancements. To highlight the progress of these tools against TB, through this review, we summarize recent studies which have used advanced computational tools and algorithms for—early TB diagnosis, anti-mycobacterium drug discovery and in the designing of the next-generation of TB vaccines. At the end, we give an insight on other computational tools and Machine Learning approaches which have successfully been applied in biomedical research and discuss their prospects and applications against TB.

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A convolutional neural network highlights mutations relevant to antimicrobial resistance in Mycobacterium tuberculosis

Cited by 35 publications

References 61 publications

Artificial Intelligence for Antimicrobial Resistance Prediction: Challenges and Opportunities towards Practical Implementation

Artificial Intelligence for Antimicrobial Resistance Prediction: Challenges and Opportunities towards Practical Implementation

TB-ML—a framework for comparing machine learning approaches to predict drug resistance ofMycobacterium tuberculosis

Advances in computational frameworks in the fight against TB: The way forward

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