@inbook{0def67ffcada4958a943400316216dc0,
title = "Text-Mining and Neuroscience",
abstract = "The wealth and diversity of neuroscience research are inherent characteristics of the discipline that can give rise to some complications. As the field continues to expand, we generate a great deal of data about all aspects, and from multiple perspectives, of the brain, its chemistry, biology, and how these affect behavior. The vast majority of research scientists cannot afford to spend their time combing the literature to find every article related to their research, nor do they wish to spend time adjusting their neuroanatomical vocabulary to communicate with other subdomains in the neurosciences. As such, there has been a recent increase in the amount of informatics research devoted to developing digital resources for neuroscience research. Neuroinformatics is concerned with the development of computational tools to further our understanding of the brain and to make sense of the vast amount of information that neuroscientists generate (French & Pavlidis, 2007). Many of these tools are related to the use of textual data. Here, we review some of the recent developments for better using the vast amount of textual information generated in neuroscience research and publication and suggest several use cases that will demonstrate how bench neuroscientists can take advantage of the resources that are available.",
keywords = "Document classification, Information retrieval, Machine learning, Neuroinformatics, Neuroscience, Ontologies, Text-mining",
author = "Ambert, {Kyle H.} and Cohen, {Aaron M.}",
year = "2012",
doi = "10.1016/B978-0-12-388408-4.00006-X",
language = "English (US)",
series = "International Review of Neurobiology",
publisher = "Academic Press Inc.",
pages = "109--132",
booktitle = "International Review of Neurobiology",
}