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@article{Xu2021,

title = {Exploring the Interdisciplinary Nature of Precision Medicine：Network Analysis and Visualization},

author = {Xin Xu and Jiming Hu and Xiaoguang Lyu and Huang He and Cheng Xingyu },

doi = {10.2196/23562 },

year  = {2021},

date = {2021-01-11},

urldate = {2021-01-11},

journal = {JMIR Medical Informatics},

abstract = {The aim of this study is to present the nature of interdisciplinary collaboration in precision medicine based on co-occurrences and social network analysis. A total of 7544 studies about precision medicine, published between 2010 and 2019, were collected from the Web of Science database. We analyzed interdisciplinarity with descriptive statistics, co-occurrence analysis, and social network analysis. An evolutionary graph and strategic diagram were created to clarify the development of streams and trends in disciplinary communities. The results indicate that 105 disciplines are involved in precision medicine research and cover a wide range. However, the disciplinary distribution is unbalanced. Current cross-disciplinary collaboration in precision medicine mainly focuses on clinical application and technology-associated disciplines. The characteristics of the disciplinary collaboration network are as follows: (1) disciplinary cooperation in precision medicine is not mature or centralized; (2) the leading disciplines are absent; (3) the pattern of disciplinary cooperation is mostly indirect rather than direct. There are 7 interdisciplinary communities in the precision medicine collaboration network; however, their positions in the network differ. Community 4, with disciplines such as genetics and heredity in the core position, is the most central and cooperative discipline in the interdisciplinary network. This indicates that Community 4 represents a relatively mature direction in interdisciplinary cooperation in precision medicine. Finally, according to the evolution graph, we clearly present the development streams of disciplinary collaborations in precision medicine. We describe the scale and the time frame for development trends and distributions in detail. Importantly, we use evolution graphs to accurately estimate the developmental trend of precision medicine, such as biological big data processing, molecular imaging, and widespread clinical applications.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Louvel2020,

title = {What’s in a name? The three genealogies of the social insocial epigenetics},

author = {Séverine Louvel},

editor = {SAGE Publications},

doi = {https://doi.org/10.1177/0539018419897001},

year  = {2020},

date = {2020-01-22},

urldate = {2020-01-22},

journal = {Social Science Information},

abstract = {Social  epigenetics  – the  study  of  the  epigenetic  mechanisms  through  which  social  environments  become biologically embodied –   epitomizes recent claims that the boundaries between the natural and the social sciences should be reduced. Relying on a bibliometric study and on a qualitative analysis of publications  in  social  epigenetics,  this   paper   investigates   how   this  research  area defines   and operationalizes  the social dimensions that may have an impact on health status and disease risk. The paper  also  addresses  how  the  social  sciences  engage  with  social  epigenetics.  First,  the  paper  traces  social epigenetics back to five epistemic backgrounds –   two in animal research (on social defeat and early-life  adversity)  and  three  in  human  studies  (on  trauma,  early-life  nutrition  and  social  adversity  over the life-course). Second, it outlines the quest for epigenetic markers of social environments, and the associated expectations and controversies. Third, it analyses the three modes of engagement of the social  sciences  with  human  studies  in  social  epigenetics:  rejection  (social  epigenetics  trapped  in  the  quest  for  a  ‘social  brain’);  warning  and  call  for  responsibility  (social  epigenetics  has  shifted  from socioeconomic   contexts   to   individual   behaviors);   and   support   and   active   contribution   (social   epigenetics may strengthen  social  studies  of  health).  This  paper  argues  that  recent  developments  in  social  epigenetics  could  strengthen  this  third  mode  of  engagement  and  expand  the  scope  of  interdisciplinary collaboration between the natural and the social sciences.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Shen2019,

title = {Detecting the Interdisciplinary Nature and Topic Hotspots of Robotics in Surgery: Social Network Analysis and Bibliometric Study},

author = {Lining Shen and Shimin Wang and Wei Dai and Zhiguo Zhang},

doi = {https://doi.org/10.2196/12625},

year  = {2019},

date = {2019-03-19},

urldate = {2019-03-19},

journal = {Journal of Medical Internet Research},

volume = {21},

number = {3},

abstract = {Background: With the widespread application of a robot to surgery, growing literature related to robotics in surgery (RS) documents widespread concerns from scientific researchers worldwide. Although such application is helpful to considerably improve the accuracy of surgery, we still lack the understanding of the multidiscipline-crossing status and topic distribution related to RS. Objective: The aim of this study was to detect the interdisciplinary nature and topic hotspots on RS by analyzing the current publication outputs related to RS. Methods: The authors collected publications related to RS in the last 21 years, indexed by the Web of Science Core Collection. Various bibliometric methods and tools were used, including literature distribution analysis at the country and institution level and interdisciplinary collaboration analysis in the different periods of time. Co-word analysis was performed based on the keywords with high frequency. The temporal visualization bar presented the evolution of topics over time. Results: A total of 7732 bibliographic records related to RS were identified. The United States plays a leading role in the publication output related to RS, followed by Italy and Germany. It should be noted that the Yonsei University in South Korea published the highest number of RS-related publications. Furthermore, the interdisciplinary collaboration is uneven; the number of disciplines involved in each paper dropped from the initial 1.60 to the current 1.31. Surgery; Engineering; Radiology, Nuclear Medicine, and Medical Imaging; and Neurosciences and Neurology are the 4 core disciplines in the field of RS, all of which have extensive cooperation with other disciplines. The distribution of topic hotspots is in imbalanced status, which can be categorized into 7 clusters. Moreover, 3 areas about the evolution of topic were identified, namely (1) the exploration of techniques that make RS implemented, (2) rapid development of robotic systems and related applications in surgery, and (3) application of a robot to excision of tissues or organs targeted at various specific diseases. Conclusions: This study provided important insights into the interdisciplinary nature related to RS, which indicates that the researchers with different disciplinary backgrounds should strengthen cooperation to publish a high-quality output. The research topic hotspots related to RS are relatively scattered, which has begun to turn to the application of RS targeted at specific diseases. Our study is helpful to provide a potential guide to the direction of the field of RS for future research in the field of RS.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Hu2017,

title = {Discovering the interdisciplinary nature of Big Data research through social network analysis and visualization},

author = {Jiming Hu and Yin Zhang},

doi = {10.1007/s11192-017-2383-1},

year  = {2017},

date = {2017-05-01},

urldate = {2017-05-01},

journal = {Scientometrics},

volume = {112},

pages = {91–109},

abstract = {Big Data is a research field involving a large number of collaborating disciplines. Based on bibliometric data downloaded from the Web of Science, this study applies various social network analysis and visualization tools to examine the structure and patterns of interdisciplinary collaborations, as well as the recently evolving overall pattern. This study presents the descriptive statistics of disciplines involved in publishing Big Data research; and network indicators of the interdisciplinary collaborations among disciplines, interdisciplinary communities, interdisciplinary networks, and changes in discipline communities over time. The findings indicate that the scope of disciplines involved in Big Data research is broad, but that the disciplinary distribution is unbalanced. The overall collaboration among disciplines tends to be concentrated in several key fields. According to the network indicators, Computer Science, Engineering, and Business and Economics are the most important contributors to Big Data research, given their position and role in the research collaboration network. Centering around a few important disciplines, all fields related to Big Data research are aggregated into communities, suggesting some related research areas, and directions for Big Data research. An ever-changing roster of related disciplines provides support, as illustrated by the evolving graph of communities.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}