Biomedical text mining and its applications in cancer research

บทความในวารสาร

ผู้เขียน/บรรณาธิการ

กลุ่มสาขาการวิจัยเชิงกลยุทธ์

ไม่พบข้อมูลที่เกี่ยวข้อง

รายละเอียดสำหรับงานพิมพ์

รายชื่อผู้แต่ง: Zhu F., Patumcharoenpol P., Zhang C., Yang Y., Chan J., Meechai A., Vongsangnak W., Shen B.

ผู้เผยแพร่: Elsevier

ปีที่เผยแพร่ (ค.ศ.): 2013

วารสาร: Journal of Biomedical Informatics (1532-0464)

Volume number: 46

Issue number: 2

หน้าแรก: 200

หน้าสุดท้าย: 211

จำนวนหน้า: 12

นอก: 1532-0464

URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84875635420&doi=10.1016%2fj.jbi.2012.10.007&partnerID=40&md5=c1e2223e351e960bd6a0dd1750f996c3

ภาษา: English-Great Britain (EN-GB)

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บทคัดย่อ

Cancer is a malignant disease that has caused millions of human deaths. Its study has a long history of well over 100. years. There have been an enormous number of publications on cancer research. This integrated but unstructured biomedical text is of great value for cancer diagnostics, treatment, and prevention. The immense body and rapid growth of biomedical text on cancer has led to the appearance of a large number of text mining techniques aimed at extracting novel knowledge from scientific text. Biomedical text mining on cancer research is computationally automatic and high-throughput in nature. However, it is error-prone due to the complexity of natural language processing. In this review, we introduce the basic concepts underlying text mining and examine some frequently used algorithms, tools, and data sets, as well as assessing how much these algorithms have been utilized. We then discuss the current state-of-the-art text mining applications in cancer research and we also provide some resources for cancer text mining. With the development of systems biology, researchers tend to understand complex biomedical systems from a systems biology viewpoint. Thus, the full utilization of text mining to facilitate cancer systems biology research is fast becoming a major concern. To address this issue, we describe the general workflow of text mining in cancer systems biology and each phase of the workflow. We hope that this review can (i) provide a useful overview of the current work of this field; (ii) help researchers to choose text mining tools and datasets; and (iii) highlight how to apply text mining to assist cancer systems biology research. ฉ 2012 Elsevier Inc.

คำสำคัญ

Biomedical text, Systems biology