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1.  SenseLab 
Briefings in bioinformatics  2007;8(3):150-162.
This article presents the latest developments in neuroscience information dissemination through the SenseLab suite of databases: NeuronDB, CellPropDB, ORDB, OdorDB, OdorMapDB, ModelDB and BrainPharm. These databases include information related to: (i) neuronal membrane properties and neuronal models, and (ii) genetics, genomics, proteomics and imaging studies of the olfactory system. We describe here: the new features for each database, the evolution of SenseLab’s unifying database architecture and instances of SenseLab database interoperation with other neuroscience online resources.
doi:10.1093/bib/bbm018
PMCID: PMC2756159  PMID: 17510162
neuroscience; databases; SenseLab; neuroinformatics; Human Brain Project
2.  Informatics challenges in Structured RNA 
Briefings in bioinformatics  2007;8(5):294-303.
The world of regulatory RNAs is fast expanding into mainstream molecular biology as both a subject of intense mechanistic study and as a tool for functional characterization. The RNA world is one of complex structures that carry out catalysis, sense metabolites and synthesize proteins. The dynamic and structural nature of RNAs presents a whole new set of informatics challenges to the computational community. The ability to relate structure and dynamics to function will be key to understanding this complex world. I review several important classes of structured RNAs that present our community with a series of biologically novel informatics challenges. I also review available informatics tools that have been recently developed in the field.
doi:10.1093/bib/bbm026
PMCID: PMC2629073  PMID: 17611237
RNA; Folding; Informatics; Riboswitch; Ribosome; RNAi
3.  Frontiers of biomedical text mining: current progress 
Briefings in bioinformatics  2007;8(5):358-375.
It is now almost 15 years since the publication of the first paper on text mining in the genomics domain, and decades since the first paper on text mining in the medical domain. Enormous progress has been made in the areas of information retrieval, evaluation methodologies and resource construction. Some problems, such as abbreviation-handling, can essentially be considered solved problems, and others, such as identification of gene mentions in text, seem likely to be solved soon. However, a number of problems at the frontiers of biomedical text mining continue to present interesting challenges and opportunities for great improvements and interesting research. In this article we review the current state of the art in biomedical text mining or ‘BioNLP’ in general, focusing primarily on papers published within the past year.
doi:10.1093/bib/bbm045
PMCID: PMC2516302  PMID: 17977867
text mining; natural language processing; information extraction; text summarization; image mining; question answering; literature-based discovery; evaluation; user orientation

Results 1-3 (3)