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Identification of Vacant and Emerging Technologies in Smart Mobility Through the GTM-Based Patent Map Development. SUSTAINABILITY 2020. [DOI: 10.3390/su12229310] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
With the development of the online platforms and the Internet of Things (IoT), various transportation services have been provided, and the lifestyle of the general public has changed significantly. However, the speed of development of technologies and services for the mobility handicapped has been relatively slow. Accordingly, in this paper, the smart mobility patent data for the mobility handicapped is subdivided through clustering to derive the mobility handicapped-related vacant technologies, and the prospect of the vacant technology is verified. For each cluster, a technology level map is generated in consideration of the technology growth level and the scope of authority of the vacant technology derived through the generative topographic map (GTM) patent map, and the level of the vacant technology is checked in terms of quantity and quality. Both indicators perform time series analyses on superior technology to predict technology trends and determine the technology’s promisingness. Unlike the precedent studies that focused only on quantitative analysis methods, this paper identified the usefulness of the technology through clustering and various verification processes and materialized it as a vacant technology that is applicable to actual R&D. Accordingly, through this empirical paper, it is possible to understand the current level of vacant technology in smart mobility for the mobility handicapped and establish an R&D strategy to prevent monopoly in technology in the future market and maintain competitiveness. It can also be utilized for new technology development in consideration of convergence with currently developed technology.
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Pros and cons of virtual screening based on public “Big Data”: In silico mining for new bromodomain inhibitors. Eur J Med Chem 2019; 165:258-272. [DOI: 10.1016/j.ejmech.2019.01.010] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2018] [Revised: 12/24/2018] [Accepted: 01/05/2019] [Indexed: 12/22/2022]
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Casciuc I, Zabolotna Y, Horvath D, Marcou G, Bajorath J, Varnek A. Virtual Screening with Generative Topographic Maps: How Many Maps Are Required? J Chem Inf Model 2018; 59:564-572. [DOI: 10.1021/acs.jcim.8b00650] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Iuri Casciuc
- Laboratoire de Chémoinformatique UMR 7140 CNRS, Institut LeBel 4, rue B. Pascal 67081 Strasbourg, France
| | - Yuliana Zabolotna
- Laboratoire de Chémoinformatique UMR 7140 CNRS, Institut LeBel 4, rue B. Pascal 67081 Strasbourg, France
| | - Dragos Horvath
- Laboratoire de Chémoinformatique UMR 7140 CNRS, Institut LeBel 4, rue B. Pascal 67081 Strasbourg, France
| | - Gilles Marcou
- Laboratoire de Chémoinformatique UMR 7140 CNRS, Institut LeBel 4, rue B. Pascal 67081 Strasbourg, France
| | - Jürgen Bajorath
- B-IT, Limes, Unit Chem. Biol. & Med. Chem., University of Bonn, 53115 Bonn, Germany
| | - Alexandre Varnek
- Laboratoire de Chémoinformatique UMR 7140 CNRS, Institut LeBel 4, rue B. Pascal 67081 Strasbourg, France
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