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This paper explored the temporal characteristics of clinical citations of biomedical papers, including how long it takes to receive its first clinical citation (the initial stage) and how long it takes to receive two or more clinical citations after its first clinical citation (the build-up stage). Over 23 million biomedical papers in PubMed between 1940 and 2013 and their clinical citations are used as the research data. We divide these biomedical papers into three groups and four categories from clinical citation level and translational science perspectives. We compare the temporal characteristics of biomedical papers of different groups or categories. From the perspective of clinical citation level, the results show that highly clinically cited papers had obvious advantages of receiving clinical citations over medium and lowly clinically cited papers in both the initial and build-up stages. Meanwhile, as the number of clinical citations increased in the build-up stage, the difference in the length of time to receive the corresponding number of clinical citations among the three groups of biomedical papers significantly increased. From the perspective of translational science, the results reveal that biomedical papers closer to clinical science more easily receive clinical citations than papers closer to basic science in both the initial and build-up stages. Moreover, we found that highly clinically cited papers had the desperate advantage of receiving clinical citations over even the clinical guidelines or clinical trials. The robustness analysis of the two aspects demonstrates the reliability of our results.

Drug research and development are embracing translational research for its potential to increase the number of drugs successfully brought to clinical applications. Using the publicly available PubMed database, we sought to describe the status of drug translational research, the distribution of translational lags for all drugs as well as the collaborations between basic science and clinical science in drug research. For each drug, an indicator called Translational Lag was proposed to quantify the interval time from its first PubMed article to its first clinical article. Meanwhile, the triangle of biomedicine was also used to visualize the status and multidisciplinary collaboration of drug translational research. The results showed that only 18.1% (24,410) of drugs/compounds had been successfully entering clinical research. It averagely took 14.38 years (interquartile range, 4 to 21 years) for a drug from the initial basic discovery to its first clinical research. In addition, the results also revealed that, in drug research, there was rare cooperation between basic science and clinical science, which were more inclined to cooperate within disciplines., Comment: 7pages, 1 figures

Keeping track of translational research is essential to evaluating the performance of programs on translational medicine. Despite several indicators in previous studies, a consensus measure is still needed to represent the translational features of biomedical research at the article level. In this study, we first trained semantic representations of biomedical entities and documents (i.e., bio entity2vec and bio doc2vec) based on over 30 million PubMed articles. With these vectors, we then developed a new measure called Translational Progression (TP) for tracking biomedical articles along the translational continuum. We validated the effectiveness of TP from two perspectives (Clinical trial phase identification and ACH classification), which showed excellent consistency between TP and other indicators. Meanwhile, TP has several advantages. First, it can track the degree of translation of biomedical research dynamically and in real time. Second, it is straightforward to interpret and operationalize. Third, it doesn%u2019t require labor-intensive MeSH labeling and it is suitable for big scholarly data as well as papers that are not indexed in PubMed. In addition, we examined the translational progressions of biomedical research from three dimensions (including overall distribution, time, and research topic), which revealed three significant findings. The proposed measure in this study could be used by policymakers to monitor biomedical research with high translational potential in real time and make better decisions. It can also be adopted and improved for other domains, such as physics or computer science, to assess the application value of scientific discoveries., Comment: 26 pages, 11 figures

The number of clinical citations received from clinical guidelines or clinical trials has been considered as one of the most appropriate indicators for quantifying the clinical impact of biomedical papers. Therefore, the early prediction of the clinical citation count of biomedical papers is critical to scientific activities in biomedicine, such as research evaluation, resource allocation, and clinical translation. In this study, we designed a four-layer multilayer perceptron neural network (MPNN) model to predict the clinical citation count of biomedical papers in the future by using 9,822,620 biomedical papers published from 1985 to 2005. We extracted ninety-one paper features from three dimensions as the input of the model, including twenty-one features in the paper dimension, thirty-five in the reference dimension, and thirty-five in the citing paper dimension. In each dimension, the features can be classified into three categories, i.e., the citation-related features, the clinical translation-related features, and the topic-related features. Besides, in the paper dimension, we also considered the features that have previously been demonstrated to be related to the citation counts of research papers. The results showed that the proposed MPNN model outperformed the other five baseline models, and the features in the reference dimension were the most important., Comment: 25 pages, 8 figures

International collaboration has become imperative in the field of AI. However, few studies exist concerning how distance factors have affected the international collaboration in AI research. In this study, we investigate this problem by using 1,294,644 AI related collaborative papers harvested from the Microsoft Academic Graph (MAG) dataset. A framework including 13 indicators to quantify the distance factors between countries from 5 perspectives (i.e., geographic distance, economic distance, cultural distance, academic distance, and industrial distance) is proposed. The relationships were conducted by the methods of descriptive analysis and regression analysis. The results show that international collaboration in the field of AI today is not prevalent (only 15.7%). All the separations in international collaborations have increased over years, except for the cultural distance in masculinity/felinity dimension and the industrial distance. The geographic distance, economic distance and academic distances have shown significantly negative relationships with the degree of international collaborations in the field of AI. The industrial distance has a significant positive relationship with the degree of international collaboration in the field of AI. Also, the results demonstrate that the participation of the United States and China have promoted the international collaboration in the field of AI. This study provides a comprehensive understanding of internationalizing AI research in geographic, economic, cultural, academic, and industrial aspects.

Despite the significant advances in life science, it still takes decades to translate a basic drug discovery into a cure for human disease. To accelerate the process from bench to bedside, interdisciplinary research (especially research involving both basic research and clinical research) has been strongly recommend by many previous studies. However, the patterns and the roles of the interdisciplinary characteristics in drug research have not been deeply examined in extant studies. The purpose of this study was to characterize interdisciplinary characteristics in drug research from the perspective of translational science, and to examine the role of different kinds of interdisciplinary characteristics in translational research for drugs.

This paper analyzes team collaboration in the field of Artificial Intelligence (AI) from the perspective of geographic distance. We obtained 1,584,175 AI related publications during 1950-2019 from the Microsoft Academic Graph. Three latitude-and-longitude-based indicators were employed to quantify the geographic distance of collaborations in AI over time at domestic and international levels. The results show team collaborations in AI has been more popular in the field over time with around 42,000 (38.4%) multiple-affiliation AI publications in 2019. The changes in geographic distances of team collaborations indicate the increase of breadth and density for both domestic and international collaborations in AI over time. In addition, the United States produced the largest number of single-country and internationally collaborated AI publications, and China has played an important role in international collaborations in AI after 2010., Comment: Accepted short paper submission to iConference 2021

Innovations in artificial intelligence (AI) are occurring at speeds faster than ever witnessed before. However, few studies have managed to measure or depict this increasing velocity of innovations in the field of AI. In this paper, we combine data on AI from arXiv and Semantic Scholar to explore the pace of AI innovations from three perspectives: AI publications, AI players, and AI updates (trial and error). A research framework and three novel indicators, Average Time Interval (ATI), Innovation Speed (IS) and Update Speed (US), are proposed to measure the pace of innovations in the field of AI. The results show that: (1) in 2019, more than 3 AI preprints were submitted to arXiv per hour, over 148 times faster than in 1994. Furthermore, there was one deep learning-related preprint submitted to arXiv every 0.87 hours in 2019, over 1,064 times faster than in 1994. (2) For AI players, 5.26 new researchers entered into the field of AI each hour in 2019, more than 175 times faster than in the 1990s. (3) As for AI updates (trial and error), one updated AI preprint was submitted to arXiv every 41 days, with around 33% of AI preprints having been updated at least twice in 2019. In addition, as reported in 2019, it took, on average, only around 0.2 year for AI preprints to receive their first citations, which is 5 times faster than 2000-2007. This swift pace in AI illustrates the increase in popularity of AI innovation. The systematic and fine-grained analysis of the AI field enabled to portrait the pace of AI innovation and demonstrated that the proposed approach can be adopted to understand other fast-growing fields such as cancer research and nano science., Comment: Journal of Informetrics 2021

This paper aimed to provide an approach to investigate the historical roots and evolution of research fields in China by extending the reference publication year spectroscopy (RPYS). RootCite, an open source software accepts raw data from both the Web of Science and the China Social Science Citation Index (CSSCI), was developed using python. We took iMetrics in China as the research case. 5,141 Chinese iMetrics related publications with 73,376 non-distinct cited references (CR) collected from the CSSCI were analyzed using RootCite. The results showed that the first CR in the field can be dated back to 1882 and written in English; but the majority (64.2%) of the CR in the field were Chinese publications. 17 peaks referring to 18 seminal works (13 in English and 5 in Chinese) were located during the period from 1900 to 2017. The field shared the same roots with that in the English world but has its own characteristics, and it was then shaped by contributions from both the English world and China. The five Chinese works have played irreplaceable and positive roles in the historical evolutionary path of the field, which should not be ignored, especially for the evolution of the field. This research demonstrated how RootCite aided the task of identifying the origin and evolution of research fields in China, which could be valuable for extending RPYS for countries with other languages., Comment: 19 pages,9 figures