Evidence map›Paper›PMID 35721858›Full record

ArticleFrontiers in bioengineering and biotechnology2022

Discovering Interdisciplinary Research Based on Neural Networks.

Tao He, Wei Fu, Jianqiao Xu, Zhihong Zhang, Jiuxing Zhou, Ying Yin, Zhenjie Xie

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in bioengineering and biotechnology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Tao HeDepartment of Information Security, Naval University of Engineering, Wuhan, China.
Wei FuDepartment of Information Security, Naval University of Engineering, Wuhan, China.
Jianqiao XuDepartment of Information Security, Naval University of Engineering, Wuhan, China.
Zhihong ZhangDepartment of Information Security, Naval University of Engineering, Wuhan, China.
Jiuxing ZhouDepartment of Information Security, Naval University of Engineering, Wuhan, China.
Ying YinDepartment of Information Security, Naval University of Engineering, Wuhan, China.
Zhenjie XieDepartment of Information Security, Naval University of Engineering, Wuhan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Interdisciplinary research promotes the emergence of scientific innovation. Researchers want to find interdisciplinary research in their research field. However, the number of scientific papers published today is increasing, and completing this task by hand is time-consuming and laborious. A neural network is a machine learning model that simulates the connection mode of neurons in the human brain. It is an important application of bionics in the artificial intelligence field. This paper proposes an approach to discovering interdisciplinary research automatically. The method generates an IRD-BERT neural network model for discovering interdisciplinary research based on the pre-trained model BERT. IRD-BERT is used to simulate the domain knowledge of experts, and author keywords can be projected into vector space by this model. According to the keyword distribution in the vector space, keywords with semantic anomalies can be identified. Papers that use these author keywords are likely to be interdisciplinary research. This method is applied to discover interdisciplinary research in the deep learning research field, and its performance is better than that of similar methods.

Indexed as

BERTdeep learninginterdisciplinary researchneural networkvector space

Identifiers

PMID35721858
PMCPMC9203848

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.