Predicting Scientific Impact via Heterogeneous Academic Network Embedding

2019 ◽  
pp. 555-568
Author(s):  
Chunjing Xiao ◽  
Jianing Han ◽  
Wei Fan ◽  
Senzhang Wang ◽  
Rui Huang ◽  
...  
Keyword(s):  
Network Embedding ◽  
Scientific Impact ◽  
Academic Network

scholarly journals Heterogeneous Academic Network Embedding Based Multivariate Random-Walk Model for Predicting Scientific Impact

2020 ◽  
Author(s):  
Yongwei Qiao ◽  
Leilei Sun ◽  
Jianing Han ◽  
Chunjing Xiao
Keyword(s):  
Random Walk ◽  
Local Structure ◽  
Random Walk Model ◽  
Network Embedding ◽  
Scientific Impact ◽  
Structure Information ◽  
Impact Prediction ◽  
The Future ◽  
Text Information ◽  
Academic Network

Abstract The prediction of current scientific impact of papers and authors has been extensively studied to help researchers find valuable papers and recent research directions, also help policymakers make recruitment decisions or funding allocation. However, how to accurately evaluate the future impact of them, especially for new papers and young researchers, is the focus of scientific impact prediction research, and is less explored. Existing graph-based methods heavily depend on the global structure information of heterogeneous academic network and ignore the local structure information and text information, which may provide important clues to identify influential papers and authors with novel perspective. In this paper, we propose a hybrid model called ESMR to predict the future influence of papers and authors by mainly exploiting these information mentioned above. Specifically, we first put forward a novel network embedding-based model, which can capture not only the local structure information, but also the text information of papers into a unified embedding representation. Then, the future impact of papers and authors is mutually ranked by integrating the learned embedding representations into a multivariate random-walk model. Empirical results on two real datasets demonstrate that the proposed method significantly outperforms the existing state-of-the-art ranking methods.

Heterogeneous dynamical academic network for learning scientific impact propagation

2021 ◽  
pp. 107839
Author(s):  
Xovee Xu ◽  
Ting Zhong ◽  
Ce Li ◽  
Goce Trajcevski ◽  
Fan Zhou
Keyword(s):  
Scientific Impact ◽  
Academic Network

A Survey of Network Embedding for Drug Analysis and Prediction

2020 ◽  
Vol 21 ◽  
Author(s):  
Zhixian Liu ◽  
Qingfeng Chen ◽  
Wei Lan ◽  
Jiahai Liang ◽  
Yiping Pheobe Chen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Protein Function ◽  
Dimensional Space ◽  
Auxiliary Information ◽  
Matrix Decomposition ◽  
Drug Analysis ◽  
Machine Learning Algorithms ◽  
Superior Performance ◽  
Network Embedding ◽  
Similarity Estimation

: Traditional network-based computational methods have shown good results in drug analysis and prediction. However, these methods are time consuming and lack universality, and it is difficult to exploit the auxiliary information of nodes and edges. Network embedding provides a promising way for alleviating the above problems by transforming network into a low-dimensional space while preserving network structure and auxiliary information. This thus facilitates the application of machine learning algorithms for subsequent processing. Network embedding has been introduced into drug analysis and prediction in the last few years, and has shown superior performance over traditional methods. However, there is no systematic review of this issue. This article offers a comprehensive survey of the primary network embedding methods and their applications in drug analysis and prediction. The network embedding technologies applied in homogeneous network and heterogeneous network are investigated and compared, including matrix decomposition, random walk, and deep learning. Especially, the Graph neural network (GNN) methods in deep learning are highlighted. Further, the applications of network embedding in drug similarity estimation, drug-target interaction prediction, adverse drug reactions prediction, protein function and therapeutic peptides prediction are discussed. Several future potential research directions are also discussed.

Recent Patents on Anti-Cancer Potential of Helenalin

2020 ◽  
Vol 15 (2) ◽  
pp. 132-142
Author(s):  
Priyanka Kriplani ◽  
Kumar Guarve
Keyword(s):  
Synergistic Effects ◽  
Therapeutic Interventions ◽  
Active Constituent ◽  
Scientific Impact ◽  
Isolation Techniques ◽  
Anti Cancer ◽  
Prevention Of Cancer ◽  
Synthetic Derivatives

Background: Arnica montana, containing helenalin as its principal active constituent, is the most widely used plant to treat various ailments. Recent studies indicate that Arnica and helenalin provide significant health benefits, including anti-inflammatory, neuroprotective, antioxidant, cholesterol-lowering, immunomodulatory, and most important, anti-cancer properties. Objective: The objective of the present study is to overview the recent patents of Arnica and its principal constituent helenalin, including new methods of isolation, and their use in the prevention of cancer and other ailments. Methods: Current prose and patents emphasizing the anti-cancer potential of helenalin and Arnica, incorporated as anti-inflammary agents in anti-cancer preparations, have been identified and reviewed with particular emphasis on their scientific impact and novelty. Results: Helenalin has shown its anti-cancer potential to treat multiple types of tumors, both in vitro and in vivo. It has also portrayed synergistic effects when given in combination with other anti- cancer drugs or natural compounds. New purification/isolation techniques are also developing with novel helenalin formulations and its synthetic derivatives have been developed to increase its solubility and bioavailability. Conclusion: The promising anti-cancer potential of helenalin in various preclinical studies may open new avenues for therapeutic interventions in different tumors. Thus clinical trials validating its tumor suppressing and chemopreventive activities, particularly in conjunction with standard therapies, are immediately required.

Information and scientific impact of Advanced Therapies in the age of mass media: an Altmetrics-based analysis of Tissue Engineering. (Preprint)

2020 ◽  
Author(s):  
Antonio Santisteban ◽  
Julia Moran ◽  
Miguel Ángel Martín Piedra ◽  
Antonio Campos Muñoz ◽  
José Antonio Moral Muñoz ◽  
...  
Keyword(s):  
Tissue Engineering ◽  
Mass Media ◽  
Prediction Equation ◽  
Factorial Analysis ◽  
Online Media ◽  
Research Groups ◽  
Academic Institutions ◽  
Scientific Impact ◽  
Online Platforms ◽  
The Impact

BACKGROUND Tissue engineering (TE) constitutes a multidisciplinary field aiming to construct artificial tissues to regenerate end-staged organs. Its development has taken placed since the last decade of the 20th century, entailing a clinical revolution. In this sense, TE research groups have worked and shared relevant information in the mass media era. Thus, it would be interesting to study the online dimension of TE research and to compare it with traditional measures of scientific impact. OBJECTIVE To evaluate TE online dimension from 2012 to 2018 by using metadata obtained from the Web of Science (WoS) and Altmetrics and to develop a prediction equation for the impact of TE documents from Almetrics scores. METHODS We have analyzed 23,719 TE documents through descriptive and statistical methods. First, TE temporal evolution was exposed for WoS and fifteen online platforms (News, Blogs, Policy, Twitter, Patents, Peer review, Weibo, Facebook, Wikipedia, Google, Reddit, F1000, Q&A, Video and Mendeley readers). The 10 most-cited TE original articles were ranked according to WoS citations and the Altmetric Attention Score. Second, in order to better comprehend TE online framework, a correlation and factorial analysis were performed based on the suitable results previously obtained for the Bartlett Sphericity and Kaiser-Meyer-Olkin tests. Finally, the liner regression model was applied to elucidate the relation between academy and online media and to construct a prediction equation for TE from Altmetrics data. RESULTS TE dynamic shows an upward trend in WoS citations, Twitter, Mendeley Readers and Altmetric Scores. However, WoS and Altmetrics rankings for the most cited documents clearly differs. When compared, the best correlation results were obtained for Mendeley readers and WoS (ρ=0.71). In addition, the factorial analysis identified six factors that could explain the previously observed differences between TE academy, and the online platforms evaluated. At this point, the mathematical model constructed is able to predict and explain more than the 40% of TE WoS citations from Altmetrics scores. CONCLUSIONS The scientific information related to the construction of bioartificial tissues increasingly reaches society through different online media. Because of the focus of TE research importantly differs when the academic institutions and online platforms are compared, it could be stated that basic and clinical research groups, academic institutions and health politicians should take it into account in a coordinated effort oriented to the design and implementation of adequate strategies for information diffusion and population health education.

Racial Inequality in Academia: Systemic Origins, Modern Challenges, and Policy Recommendations

2021 ◽  
Vol 8 (1) ◽  
pp. 11-18
Author(s):  
Cydney H. Dupree ◽  
C. Malik Boykin
Keyword(s):  
Public Policy ◽  
Racial Inequality ◽  
Quality Education ◽  
Policy Recommendations ◽  
Scientific Impact ◽  
Protest Movements ◽  
Recruitment And Retention ◽  
Ideal World ◽  
Future Leaders

In an ideal world, academia serves society; it provides quality education to future leaders and informs public policy—and it does so by including a diverse array of scholars. However, research and recent protest movements show that academia is subject to race-based inequities that hamper the recruitment and retention of scholars of color, reducing scientific impact. This article provides critical systemic context for racism in academia before reviewing research on psychological, interpersonal, and structural challenges to reducing racial inequality. Policy challenges include (a) the cultivation of harmful stereotypes, (b) the education of racially ignorant future leaders, and (c) the dedication of resources to science that informs only a few, rather than many. Finally, recommendations specify critical features of hiring, retention, transparency, and incentives that can diversify academia, create a more welcoming environment to scholars of color, and maximize the potential for innovative and impactful science.

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