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14 de sept. de 2022 · This article summarizes the potentials of AI and their application to several fields of biology, such as medicine, agriculture, and bio-based industry. Keywords: artificial intelligence, biotechnology, agriculture, medicine, crop yield, life science. Go to: 1. Introduction.
- 10.3390/life12091430
- 2022/09
- Life (Basel). 2022 Sep; 12(9): 1430.
14 de sept. de 2022 · Artificial intelligence (AI), currently a cutting-edge concept, has the potential to improve the quality of life of human beings. The fields of AI and biological research are becoming more intertwined, and methods for extracting and applying the information stored in live organisms are constantly being refined.
30 de jun. de 2023 · Artificial intelligence (AI) is advancing biomedical science in many ways, including improving image-based diagnostics; engineering strategies for improving movement related to injury, birth defects, or neurological or cardiovascular disease; as well as predicting behavior and nerve responses to stimuli.
2 de dic. de 2022 · In this chapter, we have given a brief overview of the impact of artificial intelligence (AI) in the biological sciences and bioinformatics (Varsha et al., 2021). Using simple examples and basic terminology, we briefly described the building blocks of AI and the steps that go into implementing a successful model.
22 de nov. de 2023 · AI technologies, methodologies, and applications can be used throughout the biological sciences and biology R&D, including in engineering biology (e.g., the application of engineering principles and the use of systematic design tools to reprogram cellular systems for a specific functional output).
27 de ago. de 2021 · AI for biology will be the cross-cutting technology that will enhance our ability to do biological research at every scale. We expect AI to revolutionize biology in the 21st century much like statistics transformed biology in the 20th century.
5 de may. de 2022 · Artificial Intelligence is transforming the way Biological Systems are analysed. Artificial Neural Network (ANN), Convolutional Neural Network (CNN), and Reinforcement Learning (RL) methods are used for the identification and detection of genomic structure and anomaly therein.
