Machine learning methods for research highlight prediction in biomedical effects of nanomaterial application

Li, Yinghao; Pu, Qiumei; Li, Shuheng; Zhang, Hong; Wang, Xiaofeng; Yao, Haodong; Zhao, Leiga

doi:10.1016/j.patrec.2018.11.008

Cited by 17 publications

(7 citation statements)

References 25 publications

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“…With the development of nanotechnology (Li et al, 2019 ), nanomaterials have become a major resource for the development of novel therapeutic medicines and technologies designed to improve human health and the quality of life (Zhang and Webster, 2009 ; Esmaeili et al, 2020 ; Wang et al, 2020 ). In particular, due to their multiple functionality and excellent biocompatibility, iron-based nanomaterials are frequently used in the biomedical field, such as bioseparation, biosensors, magnetic resonance imaging (MRI), tumor hyperthermia, and drug delivery (Chen and Gu, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

Nano-Sized Iron Sulfide: Structure, Synthesis, Properties, and Biomedical Applications

2020

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Nano-sized iron sulfides have attracted intense research interest due to the variety of their types, structures, and physicochemical properties. In particular, nano-sized iron sulfides exhibit enzyme-like activity by mimicking natural enzymes that depend on an iron-sulfur cluster as cofactor, extending their potential for applications in biomedicine. The present review principally summarizes the synthesis, properties and applications in biomedical fields, demonstrating that nano-sized iron sulfides have considerable potential for improving human health and quality of life.

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Section: Introductionmentioning

confidence: 99%

Nano-Sized Iron Sulfide: Structure, Synthesis, Properties, and Biomedical Applications

2020

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“…Several machine learning models are jointly used to detect and foresight the emerging research topics, and experimental results on gene editing data set make it clear that is feasible to detect emerging research topics with multiple machine learning models 15 . Li et al used machine learning methods to conduct data mining (DM) on the research results of the biomedical effects of nanomaterials, and scientifically predicted the development trend of their research hotspots 16 . With the development of computer science, few studies have also begun to focus on the topic evolution trend forecasting of computer science.…”

Section: Introductionmentioning

confidence: 99%

“…15 Li et al used machine learning methods to conduct data mining (DM) on the research results of the biomedical effects of nanomaterials, and scientifically predicted the development trend of their research hotspots. 16 With the development of computer science, few studies have also begun to focus on the topic evolution trend forecasting of computer science. Abuhay et al employ the classic time series forecasting model AutoRegressive Integrated Moving Averages (ARIMAs) to predict the trend of research topics of international conference of computer science.…”

Section: Introductionmentioning

confidence: 99%

A scientific research topic trend prediction model based on multi‐LSTM and graph convolutional network

Xue

et al. 2022

Int J of Intelligent Sys

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In recent years, with the increase of social investment in scientific research, the number of research results in various fields has increased significantly. Accurately and effectively predicting the trends of future research topics can help researchers discover future research hotspots.However, due to the increasingly close correlation between various research themes, there is a certain dependency relationship between a large number of research themes. Viewing a single research theme in isolation and using traditional sequence problem processing methods cannot effectively explore the spatial dependencies between these research themes. To simultaneously capture the spatial dependencies and temporal changes between research topics, we propose a deep neural network-based research topic hotness prediction algorithm, a spatiotemporal convolutional network model. Our model combines a graph convolutional neural network (GCN) and Temporal Convolutional Network (TCN), specifically, GCNs are used to learn the spatial dependencies of research topics a and use space dependence to strengthen spatial characteristics. TCN is used to learn the dynamics of research topics' trends. Optimization is based on the calculation of weighted losses based on time distance. Compared with the current mainstream sequence prediction models and similar spatiotemporal models on the paper datasets, experiments show that, in research topic prediction tasks, our model can effectively capture spatiotemporal relationships and the predictions outperform state-of-art baselines.

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“…Machine learning models create a mapping of input data to the desired output using complex mathematical functions [1,2]. For a classification problem, the input data are mapped to the class labels that are known beforehand [2,3,4].…”

Section: Introductionmentioning

confidence: 99%

Towards a data-driven adaptive anomaly detection system for human activity

Yahaya

Lotfi

Mahmud

2021

Pattern Recognition Letters

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Machine learning methods for research highlight prediction in biomedical effects of nanomaterial application

Cited by 17 publications

References 25 publications

Nano-Sized Iron Sulfide: Structure, Synthesis, Properties, and Biomedical Applications

Nano-Sized Iron Sulfide: Structure, Synthesis, Properties, and Biomedical Applications

A scientific research topic trend prediction model based on multi‐LSTM and graph convolutional network

Towards a data-driven adaptive anomaly detection system for human activity

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