Created at 7am, Apr 17
ilkeScience
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Applications of Artificial Intelligence in Biological Science
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frtjbpck35B_LIGWYqY1FgK4E7noKu0jJJZcSZdBtH4
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PDF
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38
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jina_embeddings_v2_base_en
Index Type
hnsw

Artificial intelligence is the capacity for thought, learning, and response in any machine or computer program. It belongs to the computer science subfield. Artificial intelligence holds significant importance and popularity not only in the computer science and information technology departments but also in many other sectors. Applications of artificial intelligence have improved people's lives in several ways. Artificial Intelligence gives employment in the fields of machine learning and technology has increased significantly, as has the effectiveness and caliber of work. The use of artificial intelligence in books, both academic and non-academic, has a significant influence on many facets of life. Applications of artificial intelligence in biotech include scientific demonstrations, sedate testing, imaging tests, and medical proof that is visually focused. In addition, artificial intelligence is employed to supervise fundamental clinical data and find useful writing. There has been a clear description of the several procedures and applications of artificial intelligence in biotechnology throughout this chapter.Kushwaha, Vandna & Prasad, Shambhoo & Kumar, Pradeep. (2024). Applications of Artificial Intelligence in Biological Science.

al.,2018).
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The endeavor of focused design of microbial products, such as biostimulants, biofertilizers, and biopesticides, to address tasks such as increased nutrient absorption or plant immune system, is another example of how crop-soil microbial interactions Page 8 of 85
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al.,2020). further for disclosing how biopesticides alter the soil microbial ecology in an unintended way. Large databases on worldwide (soil) biodiversity are essential for study on global change, and understanding the factors that contribute to ecosystem functioning and biodiversity losses is vital for maintaining stable ecosystem health. further for disclosing how biopesticides alter the soil microbial ecology in an unintended way (Keiblinger et.al.,2016). The implementation of computomics, with modern highthroughput omic measurement platforms, is fundamental to unravel environmental system understanding and discover keystone taxa that are crucial in sustaining ecosystem functions that are vital for human life and wellbeing (Trivedi, et.al.,2020). Crosscutting challenges i) Enabling trustworthy AI model development
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Reproducibility problems have plagued biotechnological and biological research in recent decades. study rigorously examining the origins of these issues, as demonstrated by studies like (Freedman, et al.,2014) reveals that the issues are not limited to a single component but rather occur across the entire chain of study objects, from biological material to data generated/collected to data processing/analysis. One particular kind of data processing that presents unique reproducibility issues is the development of AI models (Gundersen et. al., 2018). Particularly with regard to the creation of AI models, the constraints arise from the availability of suitable quality, or fit for purpose, large enough volumes of data. The availability of biological material that is suitable for the goal is often a determining factor in the availability of the data. The widespread use of the FAIR principles (Wilkinson, et. al.,2016) and their domain or topical extensions, such FAIR-Health has increased the
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