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Using Machine reading to Understand Alzheimer's and Related Diseases from the Literature

Journal of Data and Information Science 2017年第4期2卷 81-94页

作者： Satoshi Tsutsui Yi Bu Ying Ding School of Informatics Computingand EngineeringIndiana UniversityBloomingtonIN 47408USA School of Information Management Wuhan UniversityWuhan 430072China

Purpose： This paper aims to better understand a large number of papers in the medical domain of Alzheimer＇s disease （AD） and related diseases using the Machine reading approach. Design/methodology/approach： The study uses the topic modeling method to obtain an overview of the field, and employs open information extraction to further comprehend the field at a specific fact level. Findings： Several topics within the AD research field are identified, such as the Human Immunodeficiency Virus （HIV）/Acquired Immune Deficiency Syndrome （AIDS）, which can help answer the question of how A1DS/HIV and AD are very different yet related diseases. Research limitations： Some manual data cleaning could improve the study, such as removing incorrect facts found by open information extraction. Practical implications： This study uses the literature to answer specific questions on a scientific domain, which can help domain experts find interesting and meaningful relations among entities in a similar manner, such as to discover relations between AD and AIDS/HIV. Origlnality/value： Both the overview and specific information from the literature are obtained using two distinct methods in a complementary manner. This combination is novel because previous work has only focused on one of them, and thus provides a better way to understand an important scientific field using data-driven methods.

关键词： Machine reading Alzheimer＇s disease Knowledge discovery Data mining

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An all-round Al-Chemist with a scientific mind

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National Science Review 2022年第10期9卷 158-168页

作者： Qing Zhu Fei Zhang Yan Huang Hengyu Xiao Lu Yuan Zhao Xu Chun Zhang Tao Song Xin Sheng Tang Xiang Li Guo He Bao Chen Chong Jun Yi Zhou Yi Han Zhang Baicheng Zhang Jia Qi Cao Man Luo Song Wang Gui Lin Ye Wan Jun Zhang Xin Chen Shuang Cong Donglai Zhou Huirong Li Jialei Li Gang Zou Wei Wei Shang Jun Jiang Yi Luo Hefei National Research Center for Physical Sciences at the Microscale School of Chemistry and Materials Science University of Science and Technology of China School of Information Science and Technology University of Science and Technology of China Hefei Ji Shu Quantum Technology Co.Ltd Hefei National Laboratory University of Science and Technology of China

The realization of automated chemical experiments by robots unveiled the prelude to an artificial intelligence(AI) laboratory.Several AI-based systems or robots with specific chemical skills have been demonstrated,but conducting all-round scientific research remains challenging.Here,we present an all-round AI-Chemist e quipped with scientific data intelligence that is capable of performing basic tasks generally re quired in chemical research.Based on a service platform,the AI-Chemist is able to automatically read the literatures from a cloud database and propose experimental plans accordingly.It can control a mobile robot in-house or online to automatically execute the complete experimental process on 14workstations,including synthesis,characterization and performance tests.The experimental data can be simultaneously analysed by the computational brain of the AI-Chemist through machine learning and Bayesian optimization,allowing a new hypothesis for the next iteration to be proposed.The competence of the AI-Chemist has been scrutinized by three different chemical tasks.In the future,the more advanced all-round AI-Chemists equipped with scientific data intelligence may cause changes to the landscape of the chemical laboratory.

关键词： AI-Chemist computational brain mobile robot Machine reading all-round research

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