StemTextSearch: Stem cell gene database with evidence from abstracts

Chou Cheng Chen, Chung Liang Ho

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


Background Previous studies have used many methods to find biomarkers in stem cells, including text mining, experimental data and image storage. However, no text-mining methods have yet been developed which can identify whether a gene plays a positive or negative role in stem cells. Description StemTextSearch identifies the role of a gene in stem cells by using a text-mining method to find combinations of gene regulation, stem-cell regulation and cell processes in the same sentences of biomedical abstracts. Conclusions The dataset includes 5797 genes, with 1534 genes having positive roles in stem cells, 1335 genes having negative roles, 1654 genes with both positive and negative roles, and 1274 with an uncertain role. The precision of gene role in StemTextSearch is 0.66, and the recall is 0.78. StemTextSearch is a web-based engine with queries that specify (i) gene, (ii) category of stem cell, (iii) gene role, (iv) gene regulation, (v) cell process, (vi) stem-cell regulation, and (vii) species. StemTextSearch is available through

Original languageEnglish
Pages (from-to)150-159
Number of pages10
JournalJournal of Biomedical Informatics
Publication statusPublished - 2017 May 1

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Health Informatics


Dive into the research topics of 'StemTextSearch: Stem cell gene database with evidence from abstracts'. Together they form a unique fingerprint.

Cite this