Quality Control Pipeline for Next Generation Sequencing Data Analysis

Nayak, Debasish Swapnesh Kumar; Das, Jayashankar; Swarnkar, Tripti

doi:10.1007/978-981-16-9873-6_20

Cited by 11 publications

(1 citation statement)

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“…Biological Annotation: The final findings shed light on whether antibiotic-resistance genes are present or absent in the NGS data. The biological validation boosts the adaptability of the DL model [45], [46]. These forecasts can help medical professionals track AMR trends and make educated decisions about the use of antibiotics.…”

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confidence: 98%

Artificial Intelligence in Battle against Antimicrobial Resistance: Opportunities and Challenges

Debasish Swapnesh Kumar Nayak

2023

IJRITCC

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Due to the overuse and abuse of antibiotics, antimicrobial resistance (AMR) poses a serious risk to socioeconomic development and public health. A paradigm shift is required to address this dilemma, and artificial intelligence (AI) appears as a possible remedy. AI, including machine learning (ML) and deep learning (DL), has demonstrated significant promise in several medical research fields, especially in the fight against AMR. Applications of AI in AMR use cutting-edge computational methods to analyze gene expression and whole-genome sequencing data, assisting in discovering infectious disease etiology and disease subtypes. These AI-driven systems have several advantages over more conventional ones, including less need for human involvement, more accuracy, and lower costs. However, they also encounter difficulties, such as inconsistent performance across datasets, with data volume critically influencing model efficacy. The accessibility and expense of high-throughput sequencing data, particularly next-generation sequencing data, also pose challenges to the wider application of AI models for AMR investigation. Despite these difficulties, AI has significant promise in the fight against AMR, and its advantages and disadvantages must be carefully considered in order to build successful tactics for dealing with this urgent worldwide problem. We assess research papers on AMR analysis using AI on various datasets and contrast the effectiveness of various AI models. We thoroughly reviewed the DL models used up to this point in the field of AMR, and we additionally discussed the challenges that come with deploying these approaches. This paper offers a thorough overview of AI's applications in AMR analysis, highlighting both its benefits and drawbacks.

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confidence: 98%