Accomplishments and challenges in literature data mining for biology

Hirschman, Lynette; Park, Jong C.; Tsujii, Jun’ichi; Wong, Limsoon; Wu, Cathy H.

doi:10.1093/bioinformatics/18.12.1553

Cited by 224 publications

(103 citation statements)

References 35 publications

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“…The experimental data is embedded in tens of thousands of published literature and it is difficult for the individual researcher to extract a comprehensive view of the gene function and regulation in different genomes. Research on different genomic sequences within recent years has resulted exponential growth of related literature (Cohen, et al, 2005;Hirschman, et al, 2002). To help individual scientists to keep up with all the new information, a complete system that not only compiles the experimental evidences but also logically integrates the knowledge related to gene function and regulation is desired.…”

Section: -6mentioning

confidence: 99%

Text mining biomedical literature for constructing gene regulatory networks

Song

Chen

2009

Interdiscip Sci Comput Life Sci

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Section: -6mentioning

confidence: 99%

Text mining biomedical literature for constructing gene regulatory networks

Song

Chen

2009

Interdiscip Sci Comput Life Sci

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“…They achieved a precision of 91%, but with a poor recall of only 21%. We recommend [3] and [12] for a more comprehensive overview of the "accomplishments and challenges" of text mining.…”

Section: Motivationmentioning

confidence: 99%

Improving Text Mining with Controlled Natural Language: A Case Study for Protein Interactions

Kuhn

Royer

Fuchs

et al. 2006

Lecture Notes in Computer Science

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Abstract. Linking the biomedical literature to other data resources is notoriously difficult and requires text mining. Text mining aims to automatically extract facts from literature. Since authors write in natural language, text mining is a great natural language processing challenge, which is far from being solved. We propose an alternative: If authors and editors summarize the main facts in a controlled natural language, text mining will become easier and more powerful. To demonstrate this approach, we use the language Attempto Controlled English (ACE). We define a simple model to capture the main aspects of protein interactions. To evaluate our approach, we collected a dataset of 459 paragraph headings about protein interaction from literature. 56% of these headings can be represented exactly in ACE and another 23% partially. These results indicate that our approach is feasible.

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“…This effort may be facilitated by text mining technologies. A complete review of text mining and the specific problems encountered in biological text mining is beyond the scope of this introduction but there are many dedicated reviews on these topics [12,13]. On the surface, neither manual searching nor text mining approaches initially appear to be a significant problem.…”

Section: Text Mining For Literature-based Annotationsmentioning

confidence: 99%

Reconstructing protein complexes: From proteomics to systems biology

Armstrong¹,

Pocklington²,

Cumiskey³

et al. 2006

Proteomics

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Modern high throughput technologies in biological science often create lists of interesting molecules. The challenge is to reconstruct a descriptive model from these lists that reflects the underlying biological processes as accurately as possible. Once we have such a model or network, what can we learn from it? Specifically, given that we are interested in some biological process associated with the model, what new properties can we predict and subsequently test? Here, we describe, at an introductory level, a range of bioinformatics techniques that can be systematically applied to proteomic datasets. When combined, these methods give us a global overview of the network and the properties of the proteins and their interactions. These properties can then be used to predict functional pathways within the network and to examine substructure. To illustrate the application of these methods, we draw upon our own work concerning a complex of 186 proteins found in neuronal synapses in mammals. The techniques discussed are generally applicable and could be used to examine lists of proteins involved with the biological response to electric or magnetic fields.

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Accomplishments and challenges in literature data mining for biology

Cited by 224 publications

References 35 publications

Text mining biomedical literature for constructing gene regulatory networks

Text mining biomedical literature for constructing gene regulatory networks

Improving Text Mining with Controlled Natural Language: A Case Study for Protein Interactions

Reconstructing protein complexes: From proteomics to systems biology

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