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BioinformaticsBriefings in Bioinformatics
Peer-reviewed journal article

OncoPubMiner: A platform for oncology publication mining

Quan XuiD, Yueyue LiuiD, Jifang Hu, Xiaohong DuaniD, Niuben Song, Jiale Zhou, Jin-Cheng Zhai, Junyan Su, Siyao LiuiD, Fan Chen, Wei ZhengiD, Zhongjia Guo, Hexiang LiiD, Qiming ZhouiD, Beifang NiuiD

Abstract

Knowledge bases that are up-to-date and of expert quality are fundamental in biomedical research fields. A knowledge base established with human participation and subjected to multiple inspections is crucial for supporting clinical decision-making, especially in the exponentially growing field of precision oncology. The number of original publications in the field has skyrocketed with the advancement of technology and in-depth research evolved. It has become an increasingly pressing issue that researchers need to consider how to gather and mine these articles accurately and efficiently. In this paper, we present OncoPubMiner (https://oncopubminer.chosenmedinfo.com), a free and powerful system that combines text mining, data structure customization, publication search with online reading, project-centered and team-based data collection to realize a one-stop "keyword in, knowledge out" oncology publication mining platform. It was built by integrating all the open-access abstracts from PubMed and full-text articles from PubMed Central, and is updated on a daily basis. The system makes it straightforward to obtain precision oncology knowledge from scientific articles. OncoPubMiner will assist researchers in developing professional structured knowledge base systems efficiently, and bringing the oncology community closer to achieving precision oncology goals. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=150 SRC="FIGDIR/small/483968v1_ufig1.gif" ALT="Figure 1"> View larger version (64K): org.highwire.dtl.DTLVardef@68235aorg.highwire.dtl.DTLVardef@e9256aorg.highwire.dtl.DTLVardef@9783daorg.highwire.dtl.DTLVardef@15193e2_HPS_FORMAT_FIGEXP M_FIG OncoPubMiners one-stop "keyword in, knowledge out" workflow (A) is built on key features such as text mining (B), publication search (C), form customization (D), and team-based curation (E). C_FIG

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02 /Specification & Classification

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