|Title:||ATTRACTIVE - An Auto-Updating Database for Experimental Protocols in Regenerative Medicine||Authors:||Hung Y.-M
ERIC YAO-YU CHUANG
|Keywords:||Classification (of information); Database systems; Nearest neighbor search; Regenerative Medicine; Support vector machines; Automatic updates; Classification accuracy; Cosine similarity; Differentiation methods; Experimental methods; Experimental protocols; K-nearest neighbors; Linear discriminant functions; Learning algorithms||Issue Date:||2021||Journal Volume:||9||Start page/Pages:||75202-75210||Source:||IEEE Access||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/. ? 2013 IEEE.
|Appears in Collections:||生醫電子與資訊學研究所|
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