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Use Cases and Future Aspects of Intelligent Techniques in Microbial Data Analysis

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Microbial Data Intelligence and Computational Techniques for Sustainable Computing

Part of the book series: Microorganisms for Sustainability ((MICRO,volume 47))

Abstract

Microbes, including bacteria, archaea, fungi, and viruses, are fundamental to our ecosystems, health, and industries. Microbial data analysis has become indispensable in understanding their roles and interactions. In this era of big data, advanced techniques, such as high-throughput sequencing, metagenomics, and bioinformatics, have accelerated microbial research. This chapter explores the significance of intelligent techniques, particularly machine learning and artificial intelligence, in revolutionizing microbial data analysis. The aim of this chapter is to showcase the pivotal role of intelligent techniques in microbial data analysis across diverse domains, from ecology and public health to biotechnology. We delve into case studies that highlight the practical applications of these techniques and the transformative impact they have had on microbial research. Several case studies are presented, illustrating the applications of intelligent techniques in microbial research. These include predicting disease risk through gut microbiome analysis, antibiotic resistance prediction, environmental microbiology for ecosystem management, bioprocess optimization in biotechnology, and AI-powered antibiotic susceptibility testing. Each case study demonstrates how intelligent techniques have enhanced data analysis, prediction, and decision-making in their respective domains. Microbial data analysis, driven by intelligent techniques, has ushered in a new era of understanding and harnessing the power of microorganisms. The future of microbial data analysis holds immense promise, with emerging trends including the integration of omics data, explainable AI, personalized microbiome analysis, and the development of ethical and regulatory frameworks. Collaborative research and data sharing are expected to further advance our understanding of the microbial world, offering solutions to some of the most critical challenges of our time.

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Authors and Affiliations

  1. Department of Biotechnology, Faculty of Science and Technology, University of Central Punjab, Lahore, Punjab, Pakistan

    Muhammad Naveed, Syeda Izma Makhdoom, Arooj Azeem, Sarmad Mehmood, Maida Salahuddin & Zeerwah Rajpoot

  2. Department of Zoology, Shah Abdul Latif University, Khairpur Mirs, Sindh, Pakistan

    Zaibun-nisa Memon

  3. Biodiversity Park, Cholistan Institute of Desert Studies (CIDS), The Islamia University of Bahawalpur, Bahawalpur, Pakistan

    Muhammad Abdullah

  4. Department of Botany, University of Gujrat, Gujrat, Pakistan

    Muhammad Majeed

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  1. Muhammad Naveed
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  2. Zaibun-nisa Memon
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  3. Muhammad Abdullah
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  4. Syeda Izma Makhdoom
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  5. Arooj Azeem
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  6. Sarmad Mehmood
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  7. Maida Salahuddin
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  8. Zeerwah Rajpoot
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  9. Muhammad Majeed
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Correspondence to Muhammad Majeed .

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Editors and Affiliations

  1. Department of Computer Science, Babasaheb Bhimrao Ambedkar University, Amethi, Uttar Pradesh, India

    Aditya Khamparia

  2. Department of Computer Science, Babasaheb Bhimrao Ambedkar University, Lucknow, Uttar Pradesh, India

    Babita Pandey

  3. School of Bioengineering and Biosciences, Lovely Professional University, Phagwara, Punjab, India

    Devendra Kumar Pandey

  4. Department of Computer Science, Maharaja Agrasen Institute of Technology, Delhi, Delhi, India

    Deepak Gupta

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Naveed, M. et al. (2024). Use Cases and Future Aspects of Intelligent Techniques in Microbial Data Analysis. In: Khamparia, A., Pandey, B., Pandey, D.K., Gupta, D. (eds) Microbial Data Intelligence and Computational Techniques for Sustainable Computing. Microorganisms for Sustainability, vol 47. Springer, Singapore. https://doi.org/10.1007/978-981-99-9621-6_17

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