ATTRACTIVE – An Auto-Updating Database for Experimental Protocols in Regenerative Medicine

Yuan-Mao Hung,Eric Y Chuang,Liang‐Chuan Lai ,Mong‐Hsun Tsai

doi:10.1109/access.2021.3082403

Yuan-Mao Hung, Eric Y Chuang + Show 2 more

Open Access

https://doi.org/10.1109/access.2021.3082403

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Abstract

Many research articles are published on regenerative medicine every year. However, only a small proportion of these articles provide experimental methods on organ/tissue differentiation. Therefore, we developed a database – ATTRACTIVE (An auTo-updating daTabase foR experimentAl protoCols in regeneraTIVe mEdicine) – that collects journal articles with differentiation methods in regenerative medicine and updates itself automatically on a regular basis. Since the number of articles in regenerative medicine was insufficient and unbalanced, which limited the performance of the supervised learning algorithms, we proposed an algorithm that combines cosine similarity and linear discriminant functions to classify articles based on their titles and abstracts more efficiently. The results show that our proposed methods out-performed other machine learning algorithms such as k-nearest neighbors, support vector machine, and long short-term memory methods. The classification accuracy reached 94.62%, even with a small and unbalanced dataset. Lastly, we incorporated our classifier into the database for automatic updates. The database is available at http://attractive.cgm.ntu.edu.tw/ .

Highlights

There are a considerable number of medical research articles published every year
Many of these articles are collected and stored in the National Center for Biotechnology Information (NCBI) - PubMed Central (PMC) [1] database, which allows researchers to access full-text articles for free
Researchers can find these articles via the Google Scholar search engine. When it comes to searching for regenerative medicine research articles that include differentiation methods for specific tissues/organs, both PMC and Google Scholar give numerous results [2], and most of these either do not relate to organ/tissue differentiation topics directly or lack differentiation methods, such as using the broad term ‘‘immunology’’

Summary

Introduction

There are a considerable number of medical research articles published every year. Many of these articles are collected and stored in the National Center for Biotechnology Information (NCBI) - PubMed Central (PMC) [1] database, which allows researchers to access full-text articles for free. Researchers can find these articles via the Google Scholar search engine When it comes to searching for regenerative medicine research articles that include differentiation methods for specific tissues/organs, both PMC and Google Scholar give numerous results [2] (about 10,000∼200,000 results), and most of these either do not relate to organ/tissue differentiation topics directly or lack differentiation methods, such as using the broad term ‘‘immunology’’. This forms an obstacle for regenerative medicine researchers to access a comprehensive set of published differentiation methods.

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