生物数据中的人工智能

I. Chakraborty, Dr. Amarendranath Choudhury, T. Banerjee
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引用次数: 10

摘要

人工智能(AI)或机器学习是当今时代数据挖掘和大数据分析的主要选择。通过有效的学习和适应模型,它为几个工程应用提供了解决方案。其中包括人工神经网络建模、基于推理的决策算法、模拟模型、DNA计算和量子计算等技术。随着人工智能在生物医学研究中的应用,处理此类数据时的模糊性和随机性显著降低。快速的技术进步帮助人工智能技术以更有效、更方便地处理此类模糊数据的方式发展。该综述全面介绍了机器学习和人工智能计算模型、生物工程中使用的先进数据分析和优化方法,如药物设计和分析、医学成像、生物启发学习和分析适应性等。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Artificial Intelligence in Biological Data
Artificial Intelligence (AI) or Machine learning in present era serves as the primary choice for data mining and big data analysis. With effective learning and adaptation model, it provides solutions to several engineering applications. These include techniques such as Artificial Neural Network modelling, Reasoning based decision algorithms, Simulation models, DNA computing and Quantum computing among several others. With the application of AI in Biomedical research, the fuzziness and randomness in handling such type of data has significantly reduced. Rapid technological advancements have helped AI techniques evolve in manner which promotes handling such fuzzy data effectively and much more conveniently. The review presents a comprehensive view of machine learning and AI computing models, advanced data analytics and optimisation approaches used in Bioengineering such as Drug Designing and Analysis, Medical imaging, biologically inspired learning and adaption for analytics, etc.
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