Text mining for biology - the way forward: opinions from leading scientists

被引：50

作者：

Altman, Russ B. ^{[2
]}

Bergman, Casey M. ^{[3
]}

Blake, Judith ^{[4
]}

Blaschke, Christian ^{[5
]}

Cohen, Aaron ^{[6
]}

Gannon, Frank ^{[7
]}

Grivell, Les ^{[8
]}

Hahn, Udo ^{[9
]}

Hersh, William ^{[6
]}

Hirschman, Lynette ^{[1
]}

Jensen, Lars Juhl ^{[10
,11
]}

Krallinger, Martin ^{[12
]}

Mons, Barend ^{[13
,14
]}

O'Donoghue, Sean I. ^{[10
]}

Peitsch, Manuel C. ^{[15
]}

Rebholz-Schuhmann, Dietrich ^{[16
]}

Shatkay, Hagit ^{[17
]}

Valencia, Alfonso ^{[12
]}

机构：

[1] Mitre Corp, Bedford, MA 01730 USA

[2] Stanford Univ, Stanford, CA 94305 USA

[3] Univ Manchester, Manchester M13 9PT, Lancs, England

[4] Jackson Lab, Bar Harbor, ME 04609 USA

[5] Bioalma, Madrid 28760, Spain

[6] Oregon Hlth & Sci Univ, Portland, OR 97239 USA

[7] Sci Fdn Ireland, Dublin, Ireland

[8] EMBO, D-69117 Heidelberg, Germany

[9] Univ Jena, D-07743 Jena, Germany

[10] European Mol Biol Lab, D-69117 Heidelberg, Germany

[11] Univ Copenhagen, Panum Inst, NNF Ctr Prot Res, DK-2200 Copenhagen, Denmark

[12] CNIO, E-28029 Madrid, Spain

[13] Erasmus MC, NL-2300 RC Leiden, Netherlands

[14] Leiden Univ, Med Ctr, NL-2300 RC Leiden, Netherlands

[15] Swiss Inst Bioinformat, CH-1015 Lausanne, Switzerland

[16] EBI, Hinxton CB10 1SD, England

[17] Queens Univ, Sch Comp, Kingston, ON K7L 3N6, Canada

来源：

GENOME BIOLOGY | 2008年 / 9卷

关键词：

D O I：

10.1186/gb-2008-9-S2-S7

中图分类号：

Q81 [生物工程学（生物技术）]; Q93 [微生物学];

学科分类号：

071005 ; 0836 ; 090102 ; 100705 ;

摘要：

This article collects opinions from leading scientists about how text mining can provide better access to the biological literature, how the scientific community can help with this process, what the next steps are, and what role future BioCreative evaluations can play. The responses identify several broad themes, including the possibility of fusing literature and biological databases through text mining; the need for user interfaces tailored to different classes of users and supporting community-based annotation; the importance of scaling text mining technology and inserting it into larger workflows; and suggestions for additional challenge evaluations, new applications, and additional resources needed to make progress.

引用

页数：15

共 38 条

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