@article {17487,
	title = {Visual Exploration across Biomedical Databases},
	journal = {IEEE/ACM Transactions on Computational Biology and Bioinformatics},
	volume = {8},
	year = {2011},
	month = {2011/04//March},
	pages = {536 - 550},
	abstract = {Though biomedical research often draws on knowledge from a wide variety of fields, few visualization methods for biomedical data incorporate meaningful cross-database exploration. A new approach is offered for visualizing and exploring a query-based subset of multiple heterogeneous biomedical databases. Databases are modeled as an entity-relation graph containing nodes (database records) and links (relationships between records). Users specify a keyword search string to retrieve an initial set of nodes, and then explore intra- and interdatabase links. Results are visualized with user-defined semantic substrates to take advantage of the rich set of attributes usually present in biomedical data. Comments from domain experts indicate that this visualization method is potentially advantageous for biomedical knowledge exploration.},
	keywords = {Bioinformatics, Biomedical computing, biomedical databases, cross-database exploration, Data exploration and discovery, Data visualization, database management systems, Databases, Factual, DNA, graph theory, Information Storage and Retrieval, information visualization., Keyword search, medical computing, natural language processing, Proteins, semantic networks, semantics, sequences, text mining, User-Computer Interface, user-defined semantics, visual databases},
	isbn = {1545-5963},
	doi = {10.1109/TCBB.2010.1},
	author = {Lieberman,M.D. and Taheri, S. and Guo,Huimin and Mirrashed,F. and Yahav,I. and Aris,A. and Shneiderman, Ben}
}
@conference {16278,
	title = {MetaPhyler: Taxonomic profiling for metagenomic sequences},
	booktitle = {2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)},
	year = {2010},
	month = {2010/12/18/21},
	pages = {95 - 100},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {A major goal of metagenomics is to characterize the microbial diversity of an environment. The most popular approach relies on 16S rRNA sequencing, however this approach can generate biased estimates due to differences in the copy number of the 16S rRNA gene between even closely related organisms, and due to PCR artifacts. The taxonomic composition can also be determined from whole-metagenome sequencing data by matching individual sequences against a database of reference genes. One major limitation of prior methods used for this purpose is the use of a universal classification threshold for all genes at all taxonomic levels. We propose that better classification results can be obtained by tuning the taxonomic classifier to each matching length, reference gene, and taxonomic level. We present a novel taxonomic profiler MetaPhyler, which uses marker genes as a taxonomic reference. Results on simulated datasets demonstrate that MetaPhyler outperforms other tools commonly used in this context (CARMA, Megan and PhymmBL). We also present interesting results obtained by applying MetaPhyler to a real metagenomic dataset.},
	keywords = {Bioinformatics, CARMA comparison, Databases, Genomics, Linear regression, marker genes, matching length, Megan comparison, metagenomic sequences, metagenomics, MetaPhyler, microbial diversity, microorganisms, molecular biophysics, molecular configurations, Pattern classification, pattern matching, phylogenetic classification, Phylogeny, PhymmBL comparison, reference gene database, Sensitivity, sequence matching, taxonomic classifier, taxonomic level, taxonomic profiling, whole metagenome sequencing data},
	isbn = {978-1-4244-8306-8},
	doi = {10.1109/BIBM.2010.5706544},
	author = {Liu,Bo and Gibbons,T. and Ghodsi,M. and Pop, Mihai}
}
@conference {16275,
	title = {Inexact Local Alignment Search over Suffix Arrays},
	booktitle = {IEEE International Conference on Bioinformatics and Biomedicine, 2009. BIBM {\textquoteright}09},
	year = {2009},
	month = {2009/11/01/4},
	pages = {83 - 87},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {We describe an algorithm for finding approximate seeds for DNA homology searches. In contrast to previous algorithms that use exact or spaced seeds, our approximate seeds may contain insertions and deletions. We present a generalized heuristic for finding such seeds efficiently and prove that the heuristic does not affect sensitivity. We show how to adapt this algorithm to work over the memory efficient suffix array with provably minimal overhead in running time. We demonstrate the effectiveness of our algorithm on two tasks: whole genome alignment of bacteria and alignment of the DNA sequences of 177 genes that are orthologous in human and mouse. We show our algorithm achieves better sensitivity and uses less memory than other commonly used local alignment tools.},
	keywords = {bacteria, Bioinformatics, biology computing, Computational Biology, Costs, DNA, DNA homology searches, DNA sequences, Educational institutions, generalized heuristic, genes, Genetics, genome alignment, Genomics, human, inexact local alignment search, inexact seeds, local alignment, local alignment tools, memory efficient suffix array, microorganisms, molecular biophysics, mouse, Organisms, Sensitivity and Specificity, sequences, suffix array, USA Councils},
	isbn = {978-0-7695-3885-3},
	doi = {10.1109/BIBM.2009.25},
	author = {Ghodsi,M. and Pop, Mihai}
}
@article {12888,
	title = {Microbial oceanography in a sea of opportunity},
	journal = {Nature},
	volume = {459},
	year = {2009},
	month = {2009/05/13/},
	pages = {180 - 184},
	abstract = {Plankton use solar energy to drive the nutrient cycles that make the planet habitable for larger organisms. We can now explore the diversity and functions of plankton using genomics, revealing the gene repertoires associated with survival in the oceans. Such studies will help us to appreciate the sensitivity of ocean systems and of the ocean{\textquoteright}s response to climate change, improving the predictive power of climate models.},
	keywords = {Astronomy, astrophysics, Biochemistry, Bioinformatics, Biology, biotechnology, cancer, cell cycle, cell signalling, climate change, Computational Biology, development, developmental biology, DNA, drug discovery, earth science, ecology, environmental science, Evolution, evolutionary biology, functional genomics, Genetics, Genomics, geophysics, immunology, interdisciplinary science, life, marine biology, materials science, medical research, medicine, metabolomics, molecular biology, molecular interactions, nanotechnology, Nature, neurobiology, neuroscience, palaeobiology, pharmacology, Physics, proteomics, quantum physics, RNA, Science, science news, science policy, signal transduction, structural biology, systems biology, transcriptomics},
	isbn = {0028-0836},
	doi = {10.1038/nature08056},
	url = {http://www.nature.com/nature/journal/v459/n7244/abs/nature08056.html},
	author = {Bowler,Chris and Karl,David M. and Rita R Colwell}
}
@article {14620,
	title = {Guest Editors{\textquoteright} Introduction to the Special Section on Algorithms in Bioinformatics},
	journal = {IEEE/ACM Transactions on Computational Biology and Bioinformatics},
	volume = {5},
	year = {2008},
	month = {2008/12//Oct},
	pages = {482 - 483},
	keywords = {Abstracts, Algorithm design and analysis, Bioinformatics, Biological system modeling, biology computing, Computational Biology, Computational modeling, Computer science, Genomics, sequences},
	isbn = {1545-5963},
	doi = {10.1109/TCBB.2008.116},
	author = {Giancarlo,Raffaele and Hannenhalli, Sridhar}
}
@conference {15271,
	title = {Uncovering Genomic Reassortments among Influenza Strains by Enumerating Maximal Bicliques},
	booktitle = {IEEE International Conference on Bioinformatics and Biomedicine, 2008. BIBM {\textquoteright}08},
	year = {2008},
	month = {2008/11/03/5},
	pages = {223 - 230},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {The evolutionary histories of viral genomes have received significant recent attention due to their importance in understanding virulence and the corresponding ramifications to public health. We present a novel framework to detect reassortment events in influenza based on the comparison of two distributions of phylogenetic trees, rather than a pair of, possibly unreliable, consensus trees. We show how to detect all high-probability inconsistencies between two distributions of trees by enumerating maximal bicliques within a defined incompatibility graph. In the process, we give the first quadratic delay algorithm for enumerating maximal bicliques within general bipartite graphs. We demonstrate the utility of our approach by applying it to several sets of influenza genomes (both human- and avian-hosted) and successfully identify all known reassortment events and a few novel candidate reassortments. In addition, on simulated datasets, our approach correctly finds implanted reassortments and rarely detects reassortments where none were introduced.},
	keywords = {avian hosted influenza genome, Bioinformatics, Capacitive sensors, Delay, diseases, Event detection, general bipartite graphs, genomic reassortments, Genomics, graph theory, high probability inconsistencies, History, human hosted influenza genome, incompatibility graph, Influenza, influenza strain, maximal biclique, maximal biclique enumeration, microorganisms, phylogenetic trees, Phylogeny, Public healthcare, quadratic delay algorithm, reassortment, reassortment event detection, Tree graphs, viral genome evolutionary history, virulence},
	isbn = {978-0-7695-3452-7},
	doi = {10.1109/BIBM.2008.78},
	author = {Nagarajan,N. and Kingsford, Carl}
}
@article {18717,
	title = {Structural Biology: Analysis of {\textquoteright}downhill{\textquoteright} protein folding; Analysis of protein-folding cooperativity (Reply)},
	journal = {Nature},
	volume = {445},
	year = {2007},
	month = {2007/02/15/},
	pages = {E17-E18 - E17-E18},
	abstract = {Ferguson et al. and Zhou and Bai criticize the quality of our nuclear magnetic resonance (NMR) data and atom-by-atom analysis of global {\textquoteright}downhill{\textquoteright} folding, also claiming that the data are compatible with two-state folding.},
	keywords = {Astronomy, astrophysics, Biochemistry, Bioinformatics, Biology, biotechnology, cancer, cell cycle, cell signalling., climate change, Computational Biology, development, developmental biology, DNA, drug discovery, earth science, ecology, environmental science, Evolution, evolutionary biology, functional genomics, Genetics, Genomics, geophysics, immunology, interdisciplinary science, life, marine biology, materials science, medical research, medicine, metabolomics, molecular biology, molecular interactions, nanotechnology, Nature, neurobiology, neuroscience, palaeobiology, pharmacology, Physics, proteomics, quantum physics, RNA, Science, science news, science policy, signal transduction, structural biology, systems biology, transcriptomics},
	isbn = {0028-0836},
	doi = {10.1038/nature05645},
	url = {http://www.nature.com/nature/journal/v445/n7129/full/nature05645.html?lang=en},
	author = {Sadqi,Mourad and Fushman, David and Mu{\~n}oz,Victor}
}
@book {18365,
	title = {Advances in Computers: Computational Biology and Bioinformatics},
	year = {2006},
	month = {2006/11/22/},
	publisher = {Academic Press},
	organization = {Academic Press},
	abstract = {The field of bioinformatics and computational biology arose due to the need to apply techniques from computer science, statistics, informatics, and applied mathematics to solve biological problems. Scientists have been trying to study biology at a molecular level using techniques derived from biochemistry, biophysics, and genetics. Progress has greatly accelerated with the discovery of fast and inexpensive automated DNA sequencing techniques. As the genomes of more and more organisms are sequenced and assembled, scientists are discovering many useful facts by tracing the evolution of organisms by measuring changes in their DNA, rather than through physical characteristics alone. This has led to rapid growth in the related fields of phylogenetics, the study of evolutionary relatedness among various groups of organisms, and comparative genomics, the study of the correspondence between genes and other genomic features in different organisms. Comparing the genomes of organisms has allowed researchers to better understand the features and functions of DNA in individual organisms, as well as provide insights into how organisms evolve over time. The first four chapters of this book focus on algorithms for comparing the genomes of different organisms. Possible concrete applications include identifying the basis for genetic diseases and tracking the development and spread of different forms of Avian flu. As researchers begin to better understand the function of DNA, attention has begun shifting towards the actual proteins produced by DNA. The final two chapters explore proteomic techniques for analyzing proteins directly to identify their presence and understand their physical structure.- Written by active PhD researchers in computational biology and bioinformatics},
	keywords = {Bioinformatics, Computational Biology, Computers / Bioinformatics, Computers / Computer Science, Computers / Data Processing, Computers / Information Theory, Computers / Interactive \& Multimedia, Computers / Programming / General, Computers / Reference, Computers / Software Development \& Engineering / General, Mathematics / Applied, Science / Life Sciences / Biology},
	isbn = {9780120121687},
	author = {Zelkowitz, Marvin V and Tseng,Chau-Wen}
}
@conference {14442,
	title = {Is there a grand challenge or X-prize for data mining?},
	booktitle = {Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining},
	series = {KDD {\textquoteright}06},
	year = {2006},
	month = {2006///},
	pages = {954 - 956},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {This panel will discuss possible exciting and motivating Grand Challenge problems for Data Mining, focusing on bioinformatics, multimedia mining, link mining, text mining, and web mining.},
	keywords = {Bioinformatics, data mining, grand challenge, image mining, link mining, multimedia mining, text mining, video mining, web mining, X-prize},
	isbn = {1-59593-339-5},
	doi = {10.1145/1150402.1150535},
	url = {http://doi.acm.org/10.1145/1150402.1150535},
	author = {Piatetsky-Shapiro,Gregory and Grossman,Robert and Djeraba,Chabane and Feldman,Ronen and Getoor, Lise and Zaki,Mohammed}
}
@article {17109,
	title = {Dynamic Query Tools for Time Series Data Sets: Timebox Widgets for Interactive Exploration},
	journal = {Information VisualizationInformation Visualization},
	volume = {3},
	year = {2004},
	month = {2004/03/20/},
	pages = {1 - 18},
	abstract = {Timeboxes are rectangular widgets that can be used in direct-manipulation graphical user interfaces (GUIs) to specify query constraints on time series data sets. Timeboxes are used to specify simultaneously two sets of constraints: given a set of N time series profiles, a timebox covering time periods x1{\textellipsis}x2 (x1 <= x2) and values y1{\textellipsis}y2 (y1 <= y2) will retrieve only those n√N that have values y1 <= y2 during all times x1 <= x <= x2. TimeSearcher is an information visualization tool that combines timebox queries with overview displays, query-by-example facilities, and support for queries over multiple time-varying attributes. Query manipulation tools including pattern inversion and {\textquoteleft}leaders \& laggards{\textquoteright} graphical bookmarks provide additional support for interactive exploration of data sets. Extensions to the basic timebox model that provide additional expressivity include variable time timeboxes, which can be used to express queries with variability in the time interval, and angular queries, which search for ranges of differentials, rather than absolute values. Analysis of the algorithmic requirements for providing dynamic query performance for timebox queries showed that a sequential search outperformed searches based on geometric indices. Design studies helped identify the strengths and weaknesses of the query tools. Extended case studies involving the analysis of two different types of data from molecular biology experiments provided valuable feedback and validated the utility of both the timebox model and the TimeSearcher tool. Timesearcher is available at http://www.cs.umd.edu/hcil/timesearcher},
	keywords = {angular queries, Bioinformatics, dynamic query, Graphical user interfaces, temporal data, time series, timeboxes, TimeSearcher, visual query},
	isbn = {1473-8716, 1473-8724},
	doi = {10.1057/palgrave.ivs.9500061},
	url = {http://ivi.sagepub.com/content/3/1/1},
	author = {Hochheiser,Harry and Shneiderman, Ben}
}
@conference {17113,
	title = {Dynamic querying for pattern identification in microarray and genomic data},
	booktitle = {2003 International Conference on Multimedia and Expo, 2003. ICME {\textquoteright}03. Proceedings},
	volume = {3},
	year = {2003},
	month = {2003/07/06/9},
	pages = {III- 453-6 vol.3 - III- 453-6 vol.3},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Data sets involving linear ordered sequences are a recurring theme in bioinformatics. Dynamic query tools that support exploration of these data sets can be useful for identifying patterns of interest. This paper describes the use of one such tool - timesearcher - to interactively explore linear sequence data sets taken from two bioinformatics problems. Microarray time course data sets involve expression levels for large numbers of genes over multiple time points. Timesearcher can be used to interactively search these data sets for genes with expression profiles of interest. The occurrence frequencies of short sequences of DNA in aligned exons can be used to identify sequences that play a role in the pre-mRNA splicing. Timesearcher can be used to search these data sets for candidate splicing signals.},
	keywords = {Bioinformatics, data sets, Displays, dynamic querying, expression profiles, Frequency, Gene expression, genes, Genetics, genomic data, Genomics, linear ordered sequences, macromolecules, medical signal processing, Mice, Microarray, pattern identification, pattern recognition, premRNA splicing, Query processing, sequences, Signal processing, splicing, TimeSearcher},
	isbn = {0-7803-7965-9},
	doi = {10.1109/ICME.2003.1221346},
	author = {Hochheiser,H. and Baehrecke,E. H and Mount, Stephen M. and Shneiderman, Ben}
}
@article {17259,
	title = {Interactively exploring hierarchical clustering results [gene identification]},
	journal = {Computer},
	volume = {35},
	year = {2002},
	month = {2002/07//},
	pages = {80 - 86},
	abstract = {To date, work in microarrays, sequenced genomes and bioinformatics has focused largely on algorithmic methods for processing and manipulating vast biological data sets. Future improvements will likely provide users with guidance in selecting the most appropriate algorithms and metrics for identifying meaningful clusters-interesting patterns in large data sets, such as groups of genes with similar profiles. Hierarchical clustering has been shown to be effective in microarray data analysis for identifying genes with similar profiles and thus possibly with similar functions. Users also need an efficient visualization tool, however, to facilitate pattern extraction from microarray data sets. The Hierarchical Clustering Explorer integrates four interactive features to provide information visualization techniques that allow users to control the processes and interact with the results. Thus, hybrid approaches that combine powerful algorithms with interactive visualization tools will join the strengths of fast processors with the detailed understanding of domain experts},
	keywords = {algorithmic methods, arrays, Bioinformatics, biological data sets, biology computing, Data analysis, data mining, data visualisation, Data visualization, DNA, Fluorescence, gene functions, gene identification, gene profiles, Genetics, Genomics, Hierarchical Clustering Explorer, hierarchical systems, interactive exploration, interactive information visualization tool, interactive systems, Large screen displays, meaningful cluster identification, metrics, microarray data analysis, pattern clustering, pattern extraction, Process control, Sensor arrays, sequenced genomes, Tiles},
	isbn = {0018-9162},
	doi = {10.1109/MC.2002.1016905},
	author = {Seo,Jinwook and Shneiderman, Ben}
}
@conference {16466,
	title = {Optimized seamless integration of biomolecular data},
	booktitle = {Proceedings of the IEEE 2nd International Symposium on Bioinformatics and Bioengineering Conference, 2001},
	year = {2001},
	month = {2001/11/04/6},
	pages = {23 - 32},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Today, scientific data is inevitably digitized, stored in a variety of heterogeneous formats, and is accessible over the Internet. Scientists need to access an integrated view of multiple remote or local heterogeneous data sources. They then integrate the results of complex queries and apply further analysis and visualization to support the task of scientific discovery. Building a digital library for scientific discovery requires accessing and manipulating data extracted from flat files or databases, documents retrieved from the Web, as well as data that is locally materialized in warehouses or is generated by software. We consider several tasks to provide optimized and seamless integration of biomolecular data. Challenges to be addressed include capturing and representing source capabilities; developing a methodology to acquire and represent metadata about source contents and access costs; and decision support to select sources and capabilities using cost based and semantic knowledge, and generating low cost query evaluation plans},
	keywords = {analysis, Bioinformatics, biology computing, cost based knowledge, Costs, Data analysis, data mining, data visualisation, Data visualization, Data warehouses, decision support, digital library, Educational institutions, information resources, Internet, low cost query evaluation plans, Mediation, meta data, metadata, molecular biophysics, multiple local heterogeneous data sources, multiple remote heterogeneous data sources, optimized seamless biomolecular data integration, scientific discovery, scientific information systems, semantic knowledge, software libraries, visual databases, Visualization},
	isbn = {0-7695-1423-5},
	doi = {10.1109/BIBE.2001.974408},
	author = {Eckman,B. A and Lacroix,Z. and Raschid, Louiqa}
}
@conference {14621,
	title = {Hardness of flip-cut problems from optical mapping [DNA molecules application]},
	booktitle = {Compression and Complexity of Sequences 1997. Proceedings},
	year = {1997},
	month = {1997/06/11/13},
	pages = {275 - 284},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Optical mapping is a new technology for constructing restriction maps. Associated computational problems include aligning multiple partial restriction maps into a single {\textquotedblleft}consensus{\textquotedblright} restriction map, and determining the correct orientation of each molecule, which was formalized as the exclusive binary flip cut (EBFC) problem by Muthukrishnan and Parida (see Proc. of the First ACM Conference on Computational Molecular Biology (RECOMB), Santa Fe, p.209-19, 1997). Here, the authors prove that the EBFC problem, as well as a number of its variants, are NP-complete. They also identify another problem formalized as binary shift cut (BSC) problem motivated by the fact that there might be missing fragments at the beginnings and/or the ends of the molecules, and prove it to be NP-complete. Therefore, they do not have efficient, that is, polynomial time solutions unless P=NP},
	keywords = {binary shift cut, Biochemistry, Bioinformatics, biological techniques, Biology, biomedical optical imaging, combinatorial mathematics, combinatorial problem, computational complexity, computational problems, DNA, DNA molecule, exclusive binary flip cut, flip-cut problem hardness, Genetic engineering, Genomics, MATHEMATICS, molecular biology, molecular biophysics, molecule orientation, multiple partial restriction maps, NP-complete problem, optical mapping, polynomial time solutions, Polynomials, sequences},
	isbn = {0-8186-8132-2},
	doi = {10.1109/SEQUEN.1997.666922},
	author = {Dancik,V. and Hannenhalli, Sridhar and Muthukrishnan,S.}
}
