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1.  GLay: community structure analysis of biological networks 
Bioinformatics  2010;26(24):3135-3137.
Summary: GLay provides Cytoscape users an assorted collection of versatile community structure algorithms and graph layout functions for network clustering and structured visualization. High performance is achieved by dynamically linking highly optimized C functions to the Cytoscape JAVA program, which makes GLay especially suitable for decomposition, display and exploratory analysis of large biological networks.
PMCID: PMC2995124  PMID: 21123224
2.  Integrating and annotating the interactome using the MiMI plugin for cytoscape 
Bioinformatics  2008;25(1):137-138.
Summary: The MiMI molecular interaction repository integrates data from multiple sources, resolves interactions to standard gene names and symbols, links to annotation data from GO, MeSH and PubMed and normalizes the descriptions of interaction type. Here, we describe a Cytoscape plugin that retrieves interaction and annotation data from MiMI and links out to multiple data sources and tools. Community annotation of the interactome is supported.
Availability: MiMI plugin v3.0.1 can be installed from within Cytoscape 2.6 using the Cytoscape plugin manager in ‘Network and Attribute I/0’ category. The plugin is also preloaded when Cytoscape is launched using Java WebStart at by querying a gene and clicking ‘View in MiMI Plugin for Cytoscape’ link.
PMCID: PMC2638934  PMID: 18812364
3.  A bioinformatics analysis of the cell line nomenclature 
Bioinformatics  2008;24(23):2760-2766.
Motivation: Cell lines are used extensively in biomedical research, but the nomenclature describing cell lines has not been standardized. The problems are both linguistic and experimental. Many ambiguous cell line names appear in the published literature. Users of the same cell line may refer to it in different ways, and cell lines may mutate or become contaminated without the knowledge of the user. As a first step towards rationalizing this nomenclature, we created a cell line knowledgebase (CLKB) with a well-structured collection of names and descriptive data for cell lines cultured in vitro. The objectives of this work are: (i) to assist users in extracting useful information from biomedical text and (ii) to highlight the importance of standardizing cell line names in biomedical research. This CLKB contains a broad collection of cell line names compiled from ATCC, Hyper CLDB and MeSH. In addition to names, the knowledgebase specifies relationships between cell lines. We analyze the use of cell line names in biomedical text. Issues include ambiguous names, polymorphisms in the use of names and the fact that some cell line names are also common English words. Linguistic patterns associated with the occurrence of cell line names are analyzed. Applying these patterns to find additional cell line names in the literature identifies only a small number of additional names. Annotation of microarray gene expression studies is used as a test case. The CLKB facilitates data exploration and comparison of different cell lines in support of clinical and experimental research.
Availability: The web ontology file for this cell line collection can be downloaded at
Supplementary information: Supplementary data are available at Bioinformatics online.
PMCID: PMC2639272  PMID: 18849319
4.  SciMiner: web-based literature mining tool for target identification and functional enrichment analysis 
Bioinformatics  2009;25(6):838-840.
Summary:SciMiner is a web-based literature mining and functional analysis tool that identifies genes and proteins using a context specific analysis of MEDLINE abstracts and full texts. SciMiner accepts a free text query (PubMed Entrez search) or a list of PubMed identifiers as input. SciMiner uses both regular expression patterns and dictionaries of gene symbols and names compiled from multiple sources. Ambiguous acronyms are resolved by a scoring scheme based on the co-occurrence of acronyms and corresponding description terms, which incorporates optional user-defined filters. Functional enrichment analyses are used to identify highly relevant targets (genes and proteins), GO (Gene Ontology) terms, MeSH (Medical Subject Headings) terms, pathways and protein–protein interaction networks by comparing identified targets from one search result with those from other searches or to the full HGNC [HUGO (Human Genome Organization) Gene Nomenclature Committee] gene set. The performance of gene/protein name identification was evaluated using the BioCreAtIvE (Critical Assessment of Information Extraction systems in Biology) version 2 (Year 2006) Gene Normalization Task as a gold standard. SciMiner achieved 87.1% recall, 71.3% precision and 75.8% F-measure. SciMiner's literature mining performance coupled with functional enrichment analyses provides an efficient platform for retrieval and summary of rich biological information from corpora of users' interests.
Availability: A server version of the SciMiner is also available for download and enables users to utilize their institution's journal subscriptions.
Supplementary information: Supplementary data are available at Bioinformatics online.
PMCID: PMC2654801  PMID: 19188191
5.  Cytoscape ESP: simple search of complex biological networks 
Bioinformatics  2008;24(12):1465-1466.
Summary: Cytoscape enhanced search plugin (ESP) enables searching complex biological networks on multiple attribute fields using logical operators and wildcards. Queries use an intuitive syntax and simple search line interface. ESP is implemented as a Cytoscape plugin and complements existing search functions in the Cytoscape network visualization and analysis software, allowing users to easily identify nodes, edges and subgraphs of interest, even for very large networks.
PMCID: PMC2427162  PMID: 18445605
6.  MiSearch adaptive pubMed search tool 
Bioinformatics  2008;25(7):974-976.
Summary: MiSearch is an adaptive biomedical literature search tool that ranks citations based on a statistical model for the likelihood that a user will choose to view them. Citation selections are automatically acquired during browsing and used to dynamically update a likelihood model that includes authorship, journal and PubMed indexing information. The user can optionally elect to include or exclude specific features and vary the importance of timeliness in the ranking.
Supplementary information: Supplementary data are available at Bioinformatics online.
PMCID: PMC2660869  PMID: 18326507

Results 1-6 (6)