Machine Learning for Environmental Toxicology: A Call for Integration and Innovation.
Machine Learning for Environmental Toxicology: A Call for Integration and Innovation.
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DOI：
10.1021/acs.est.8b05382
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发表时间：
2018-10
影响因子：
11.4
通讯作者：
T. Miller;M. Gallidabino;J. MacRae;C. Hogstrand;N. Bury;L. Barron;J. Snape;S. Owen
中科院分区：
文献类型：
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作者：
T. Miller;M. Gallidabino;J. MacRae;C. Hogstrand;N. Bury;L. Barron;J. Snape;S. Owen
计算能力的最新进展使机器学习（ML）在所有科学领域的应用成为可能。从一个数据丰富的景观到一个新的假设，关系和知识正在出现的结果。虽然ML与人工智能（AI）有关，但它们并不相同。ML是AI的一个分支，涉及应用统计算法使系统能够学习。学习可以涉及数据解释、模式识别和决策。然而，ML在环境毒理学中的应用和接受程度，更具体地说，对于我们的观点，环境风险评估（ERA），仍然很低。ML是颠覆性研究技术的一个例子，迫切需要这种技术来科普所需工作的复杂性和规模。
Recent advances in computing power have enabled the application of machine learning (ML) across all areas of science. A step change from a data-rich landscape to one where new hypotheses, relationships, and knowledge is emerging as a result. While ML is related to artificial intelligence (AI), they are not the same. ML is a branch of AI involving the application of statistical algorithms to enable a system to learn. Learning can involve data interpretation, identification of patterns and decision making. However, application and acceptance of ML within environmental toxicology, and more specifically for our viewpoint, environmental risk assessment (ERA), remains low. ML is an example of a disruptive research technology, which is urgently needed to cope with the complexity and scale of work required.