What the papers say: Text mining for genomics and systems biology

Abstract

Keeping up with the rapidly growing literature has become virtually impossible for most scientists. This can have dire consequences. First, we may waste research time and resources on reinventing the wheel simply because we can no longer maintain a reliable grasp on the published literature. Second, and perhaps more detrimental, judicious (or serendipitous) combination of knowledge from different scientific disciplines, which would require following disparate and distinct research literatures, is rapidly becoming impossible for even the most ardent readers of research publications. Text mining -- the automated extraction of information from (electronically) published sources -- could potentially fulfil an important role -- but only if we know how to harness its strengths and overcome its weaknesses. As we do not expect that the rate at which scientific results are published will decrease, text mining tools are now becoming essential in order to cope with, and derive maximum benefit from, this information explosion. In genomics, this is particularly pressing as more and more rare disease-causing variants are found and need to be understood. Not being conversant with this technology may put scientists and biomedical regulators at a severe disadvantage. In this review, we introduce the basic concepts underlying modern text mining and its applications in genomics and systems biology. We hope that this review will serve three purposes: (i) to provide a timely and useful overview of the current status of this field, including a survey of present challenges; (ii) to enable researchers to decide how and when to apply text mining tools in their own research; and (iii) to highlight how the research communities in genomics and systems biology can help to make text mining from biomedical abstracts and texts more straightforward.

From comorbidities of chronic obstructive pulmonary disease to identification of shared molecular mechanisms by data integration

“…Secondly, and in order to overcome text-mining biases [67] we investigated the selected genes in High-throughput based studies of gene expression and/or DNA Methylation for smoking, age and training (as a proxy for physical activity); see description in Additional file 16: Table S7.…”

Construction and Validation of Food Webs Using Logic-Based Machine Learning and Text Mining

“…There has been an increasing interest in using text mining in Bioinformatics and Systems biology (Harmston et al, 2010). In general, there are three main approaches (Cohen and Hunter, 2008): the first simply searches for the co-occurrence of concepts in the same textual unit (Jenssen et al, 2001); the second is related to rule-based systems (Blaschke et al, 1999) emphasising on the knowledge of language structure; and the third includes statistical and machine learning based systems (Cohen and Hunter, 2004) that generate classifiers operating on different levels of the text-mining process.…”

Verification of systems biology research in the age of collaborative competition

“…As the explosive growth of biomedical data strains the capacity of human curation, computational methods to mine the literature are becoming increasingly important 10 . But automatic text mining has its own weaknesses, such as the difficulty of extracting information from figures or tables, and the ambiguities of interpretation inherent in natural language.…”