Volume 9 Supplement 9

Proceedings of the Fifth Annual MCBIOS Conference. Systems Biology: Bridging the Omics

Open Access

Proceedings of the 2008 MidSouth Computational Biology and Bioinformatics Society (MCBIOS) Conference

  • Jonathan D Wren1Email author,
  • Dawn Wilkins2,
  • James C Fuscoe3,
  • Susan Bridges4,
  • Stephen Winters-Hilt5 and
  • Yuriy Gusev6
BMC Bioinformatics20089(Suppl 9):S1

DOI: 10.1186/1471-2105-9-S9-S1

Published: 12 August 2008


MCBIOS 2008 was held February 23–24, 2008 in Oklahoma City, Oklahoma at the Cox Convention Center in Bricktown. It was the best attended in the series of MCBIOS conferences (140 registrants) with the most participation (68 posters submitted). Informative and engaging keynote talks were delivered by Dr. Bruce Roe and Dr. Edward Dougherty. The full agenda is online at http://www.okbios.org.

Student poster award winners were: Vinay Ravindrakumar of University of Arkansas for Medical Sciences (1st place), Quan Shi of Little Rock Central High School (2nd) and Brian Roux of the University of New Orleans (UNO) (3rd), with honorary mentions going to Murat Eren of UNO and Prashanti Manda of Mississippi State University (MSU). Student talk winners were: Daniel Quest of the University of Nebraska Medical Center (1st place), Nan Wang of MSU (2nd), and William Sanders of MSU (3rd).

Proceedings summary

This year, 19 out of 27 submitted papers were accepted for inclusion in the official conference proceedings (70%), similar to the number published from MCBIOS 2007 [126]. Each paper was peer-reviewed by at least two reviewers. Our goal in peer-review for the Proceedings is to be inclusive enough to accurately reflect the scope of scientific work presented at the conference yet rigorous enough such that only the highest quality work presented is selected for inclusion in the official proceedings. The general themes of this year's proceedings papers fall into five categories, discussed below.

Systems biology

Biological systems can be modeled as complex systems with many interactions between the components. One goal of emerging systems biology is to analyze very large complex biological networks such as protein-protein interactions, metabolism, and regulation to identify functional modules and to assign the functions to certain components of the system. Mutlu Mete et al. [27] devised a new methodology called SCAN (Structural Clustering Algorithm for Networks) that can efficiently find clusters or functional modules in complex biological networks, as well as hubs and outliers. In addition, nodes can be classified into various roles based on their structures. Interpretations of functional groups found by SCAN showed superior performance over CNM, a well-known modularity-based clustering algorithm.

Analysis of microarray gene expression data is challenging and may lead to biased or incomplete biological interpretations. To gain a more holistic (i.e., systemic) picture, it is essential to integrate a careful statistical approach with biological knowledge from various sources into the analysis. Mikhail Dozmorov et al. [28] present an integrative approach to microarray analysis and demonstrate how the various steps in their process support each other and refine the current model of cell-matrix interaction. With their method, they were able to identify inflammation and G-protein signaling as processes affected by the extracellular matrix.

Metastases are responsible for the majority of cancer fatalities. The molecular mechanisms governing metastasis are poorly understood, hindering early diagnosis and treatment. Unlike most previous studies, a study by Andrey Ptitsyn et al. [29] proposes an approach that puts into focus gene interaction networks and molecular pathways rather than separate marker genes. This study indicates that regardless of the tissue of origin, all metastatic tumors share a number of common features related to changes in basic energy metabolism, cell adhesion/cytoskeleton remodeling, antigen presentation and cell cycle regulation.

Circadian rhythm is a crucial factor in orchestration of plant physiology, keeping it in synchrony with the daylight cycle. Previous studies reported approximately 16% of plant genes behaved in a circadian fashion, while studies in mammals suggested circadian baseline oscillation in nearly 100% of genes. Andrey Ptitsyn [30] presents a comprehensive analysis of periodicity in two independent Arabidopsis thaliana data sets. This study indicates a more pervasive role of gene expression oscillation in the molecular physiology of plants than previously believed. Application of advanced algorithms identified circadian baseline oscillation in almost all plant genes as well as a complex orchestration of gene expression timing in important biological pathways.


Chromatography coupled to mass spectrometry is a powerful way to resolve and compare the relative abundance of chemical compounds within heterogeneous biological samples. However the resulting data sets are 2 or 3-dimensional, presenting formidable obstacles to peak alignment – a process required to ensure sample comparison is conducted appropriately. The first dimension of separation is chromatographic elution time, which varies from run to run for each molecular species. To solve this problem, Minho Chae et al. [31] developed an iterative block-shifting approach that adjusts for variation in retention time without distorting peak area. They first matched chemically identical peaks based on both retention-time and mass-spectral information. Non-peak regions of each chromatogram were stretched or compressed to align peaks with a reference chromatogram, thus preserving the shapes of matched peaks. Their approach compared favorably to other approaches, and was superior in preservation of peak area.

Also, in the proceedings, Tianxiao Huan et al. describe Proteolens, a new tool to navigate and visualize biological networks [32].

Microarray studies

Microarrays are a powerful technology and an area of active research interest in bioinformatics, with a focus on the development of novel methods for analysis and interpretation of experiments [3349]. This year's proceedings reflect this area of active research interest with several reports that focus on the development of methods and analysis of microarray data.

Microarray-based molecular signatures have played an increasing role in diagnosis, prognosis and risk/safety assessments, the first step of which is to identify a set of informative genes. Zhenqiang Su et al. [50] investigate a new gene selection approach to identify informative genes. The rationale of the approach is that informative genes should consistently be significantly differentially expressed for different variations of sample size. Genes exhibiting significance throughout the iterations are considered a Very Important Pool (VIP) of genes. It was found that the genes identified by the VIP method, but not by the p-value ranking approach, are also related to the disease investigated, and these genes are part of the pathways derived from the common genes shared by both the VIP and p-ranking methods. Moreover, the binary classifiers built from these genes are statistically equivalent to those built from the top 50 p-value ranked genes in distinguishing different types of samples. Therefore, the VIP gene selection approach could identify additional subsets of informative genes that would not always be selected by the p-value ranking method.

The paper by Taewon Lee et al. [51] presents a method to test the significance of expression changes within a group of genes, while considering the correlation structure among genes in each group. This method enables the rapid detection of gene expression changes, indicating altered cell functions or pathways, and facilitates the interpretation of the data. Application of the method to real data shows that it is an improved, practical method to evaluate the effects of treatments on functional classes of genes, such as those based on Gene Ontology descriptors.

Also in the proceedings, Arun Rawat et al. report on a method of microarray graph mining to derive co-expressed genes [52], and Leming Shi et al. report on an impressively large study of the reproducibility of gene lists for microarray experiments, and conclude with recommendations for detecting significant differential expression [53].

Genomic analysis

As more and more genomes become fully sequenced in the coming years, gene identification is still a limiting factor to scientific discovery. Since a significant proportion of genes exist as members of families of genes with related functions, Ronald Frank et al. [54] have employed a strategy to identify these gene family members using patterns indicating negative selection pressure on the coding region. The authors tested the strategy on several well-characterized gene families from Arabidopsis thaliana and report their success in correctly identifying several members of each gene family starting with one known member and using only EST data.

Highly accurate and reproducible genotype calling are paramount for genome-wide association studies (GWAS), since errors introduced by calling algorithms can lead to inflation of false associations between genotype and phenotype. Most genotype calling algorithms currently used for GWAS are based on multiple arrays, consisting of many samples. Huixiao Hong et al. [55] observed that batch size and composition affect the genotype calling results in GWAS using the algorithm BRLMM. The larger the differences in batch sizes, the larger the effect. The more homogenous the samples in the batches, the more consistent the genotype calls. The inconsistency propagates to the lists of significantly associated single nucleotide polymorphisms identified in downstream association analysis. Thus, uniform and large batch sizes should be used to make genotype calls for GWAS. In addition, samples of high homogeneity should be placed into the same batch.

The cellular machinery by which genes are expressed is both complex and an active area of recent bioinformatics research [5666]. A first step in understanding this process is to locate the binding positions of transcription factors over the chromosome. Since the search space is large, advanced computational tools play a central role in solving this problem. Despite the development of nearly two hundred tools to elucidate transcription factor binding sites, much controversy still remains on how to build methods with high sensitivity and specificity. Central in this debate is determining the factors that will improve the quality of computational predictions. The paper by Daniel Quest et al. [67], presents a novel benchmarking strategy to automate and evaluate methods designed to detect transcription factor binding sites. The strategy allows researchers, for the first time, to evaluate transcription factor detection methods on the genome scale. In particular, researchers can vary the data, algorithms, parameters and transcription factor binding site representations to determine the method best suited to their problem of interest. The proposed platform allows for rapid evaluation of deficits in current models and paves the way to develop new tools to overcome these problems.

Also, the Garner Lab extends their work on predicting the impact of single nucleotide polymorphisms (SNPs) in a paper by Vinayak Kulkarni et al. [68], and Jerzy Zielinski et al. report on a method of analyzing genomic sequences by a time-dependent autoregressive moving average [69].


Text-mining is an area of bioinformatics whereby identification and analysis of trends in text is done computationally [7078]. To this end, Cory Giles and Jonathan Wren developed a method of identifying directional relationships within text (e.g., chemical X increases heart rate, or gene Y elevates inflammation) using natural language processing (NLP) [79]. Their goals were also to make their system scalable to large bodies of text (e.g. MEDLINE has 18 million records and counting), as well as understanding how much apparent contradiction takes place when attempting to extract isolated facts from within a greater context from these huge bodies of text.

Christopher Bottoms and Dong Xu study atom-naming conventions in the Protein Data Bank and find that some names are assigned ad hoc, resulting in duplicate names and creating problems for standardization and data-mining [80].

In [81], Roux and Winters-Hilt describe Hybrid SVM/HMM structural sensors for use in analysis of stochastic sequential data. They begin with a novel approach to classification using Support Vector Machines and Markov Models with application to detecting Intron-Exon and Exon-Intron (5' and 3') splice sites. The approach also includes the application of Shannon Entropy based analysis of the stochastic datasets to detect minimal data components for feature extraction. Results are presented for a variety of eukaryotic species.

In the Winters-Hilt group, work continues on developing nanopore detector signal analysis via machine learning methods for classification and knowledge discovery. In [82], Churbanov and Winters-Hilt describe the application of a distributed Mixture of Hidden Markov Models (MHMMs) to the problem of channel current blockade clustering and associated analyte classification. The distributed MHMM provides a feature extraction that is equivalent to that of the sequential HMM with a speedup factor approximately equal to the number of independent CPUs operating on the data.

Future meetings

The Sixth annual MCBIOS Conference will be held in Starkville, Mississippi in early spring, 2009. See http://www.MCBIOS.org for further information on MCBIOS and future meetings. MCBIOS and OKBIOS are both regional affiliates of the International Society for Computational Biology http://www.ISCB.org.



We thank the Conference Committee, Program Committee, student volunteers and sponsors for their help in organizing MCBIOS 2008. We would like to especially thank Jim Mason and Frank Waxman for their sponsorship of MCBIOS 2008 as well as OKBIOS 2004 and OKBIOS 2005. We also thank our peer-reviewers for their quality efforts to review submitted manuscripts.

This article has been published as part of BMC Bioinformatics Volume 9 Supplement 9, 2008: Proceedings of the Fifth Annual MCBIOS Conference. Systems Biology: Bridging the Omics. The full contents of the supplement are available online at http://www.biomedcentral.com/1471-2105/9?issue=S9

Authors’ Affiliations

Arthritis and Immunology Research Program, Oklahoma Medical Research Foundation
Computer & Information Science Department, The University of Mississippi, University
Center for Functional Genomics, Division of Systems Toxicology, National Center for Toxicological Research, U.S. Food and Drug Administration
Department of Computer Science and Engineering, Mississippi State University
Department of Computer Science, University of New Orleans, and The Research Institute for Children
Department of Surgery, Health Sciences Center, The University of Oklahoma


  1. Computational frontiers in biomedicine. Proceedings of the Fourth Annual Conference of the MidSouth Computational Biology and Bioinformatics Society. February 1–3, 2007. New Orleans, Louisiana, USA BMC Bioinformatics 2007,8(Suppl 7):S1–25. 10.1186/1471-2105-8-S7-S1
  2. Winters-Hilt S: The alpha-hemolysin nanopore transduction detector – single-molecule binding studies and immunological screening of antibodies and aptamers. BMC Bioinformatics 2007,8(Suppl 7):S9. 10.1186/1471-2105-8-S7-S9PubMed CentralPubMedGoogle Scholar
  3. Ding Y, Dang X, Peng H, Wilkins D: Robust clustering in high dimensional data using statistical depths. BMC Bioinformatics 2007,8(Suppl 7):S8. 10.1186/1471-2105-8-S7-S8PubMed CentralPubMedGoogle Scholar
  4. Pirooznia M, Gong P, Guan X, Inouye LS, Yang K, Perkins EJ, Deng Y: Cloning, analysis and functional annotation of expressed sequence tags from the Earthworm Eisenia fetida. BMC Bioinformatics 2007,8(Suppl 7):S7. 10.1186/1471-2105-8-S7-S7PubMed CentralPubMedGoogle Scholar
  5. Yuan JS, Burris J, Stewart NR, Mentewab A, Stewart CN Jr: Statistical tools for transgene copy number estimation based on real-time PCR. BMC Bioinformatics 2007,8(Suppl 7):S6. 10.1186/1471-2105-8-S7-S6PubMed CentralPubMedGoogle Scholar
  6. Nagarajan V, Elasri MO: Structure and function predictions of the Msa protein in Staphylococcus aureus. BMC Bioinformatics 2007,8(Suppl 7):S5. 10.1186/1471-2105-8-S7-S5PubMed CentralPubMedGoogle Scholar
  7. Mei N, Guo L, Liu R, Fuscoe JC, Chen T: Gene expression changes induced by the tumorigenic pyrrolizidine alkaloid riddelliine in liver of Big Blue rats. BMC Bioinformatics 2007,8(Suppl 7):S4. 10.1186/1471-2105-8-S7-S4PubMed CentralPubMedGoogle Scholar
  8. Schnackenberg LK, Sun J, Espandiari P, Holland RD, Hanig J, Beger RD: Metabonomics evaluations of age-related changes in the urinary compositions of male Sprague Dawley rats and effects of data normalization methods on statistical and quantitative analysis. BMC Bioinformatics 2007,8(Suppl 7):S3. 10.1186/1471-2105-8-S7-S3PubMed CentralPubMedGoogle Scholar
  9. Loganantharaj R, Atwi M: Towards validating the hypothesis of phylogenetic profiling. BMC Bioinformatics 2007,8(Suppl 7):S25. 10.1186/1471-2105-8-S7-S25PubMed CentralPubMedGoogle Scholar
  10. Bridges SM, Magee GB, Wang N, Williams WP, Burgess SC, Nanduri B: ProtQuant: a tool for the label-free quantification of MudPIT proteomics data. BMC Bioinformatics 2007,8(Suppl 7):S24. 10.1186/1471-2105-8-S7-S24PubMed CentralPubMedGoogle Scholar
  11. Sanders WS, Bridges SM, McCarthy FM, Nanduri B, Burgess SC: Prediction of peptides observable by mass spectrometry applied at the experimental set level. BMC Bioinformatics 2007,8(Suppl 7):S23. 10.1186/1471-2105-8-S7-S23PubMed CentralPubMedGoogle Scholar
  12. Guo L, Mei N, Dial S, Fuscoe J, Chen T: Comparison of gene expression profiles altered by comfrey and riddelliine in rat liver. BMC Bioinformatics 2007,8(Suppl 7):S22. 10.1186/1471-2105-8-S7-S22PubMed CentralPubMedGoogle Scholar
  13. Das MK, Dai HK: A survey of DNA motif finding algorithms. BMC Bioinformatics 2007,8(Suppl 7):S21. 10.1186/1471-2105-8-S7-S21PubMed CentralPubMedGoogle Scholar
  14. Winters-Hilt S, Morales E, Amin I, Stoyanov A: Nanopore-based kinetics analysis of individual antibody-channel and antibody-antigen interactions. BMC Bioinformatics 2007,8(Suppl 7):S20. 10.1186/1471-2105-8-S7-S20PubMed CentralPubMedGoogle Scholar
  15. Dozmorov MG, Kyker KD, Saban R, Shankar N, Baghdayan AS, Centola MB, Hurst RE: Systems biology approach for mapping the response of human urothelial cells to infection by Enterococcus faecalis. BMC Bioinformatics 2007,8(Suppl 7):S2. 10.1186/1471-2105-8-S7-S2PubMed CentralPubMedGoogle Scholar
  16. Winters-Hilt S, Baribault C: A novel, fast, HMM-with-Duration implementation – for application with a new, pattern recognition informed, nanopore detector. BMC Bioinformatics 2007,8(Suppl 7):S19. 10.1186/1471-2105-8-S7-S19PubMed CentralPubMedGoogle Scholar
  17. Winters-Hilt S, Merat S: SVM clustering. BMC Bioinformatics 2007,8(Suppl 7):S18. 10.1186/1471-2105-8-S7-S18PubMed CentralPubMedGoogle Scholar
  18. Mete M, Xu X, Fan CY, Shafirstein G: Automatic delineation of malignancy in histopathological head and neck slides. BMC Bioinformatics 2007,8(Suppl 7):S17. 10.1186/1471-2105-8-S7-S17PubMed CentralPubMedGoogle Scholar
  19. Gusev Y, Schmittgen TD, Lerner M, Postier R, Brackett D: Computational analysis of biological functions and pathways collectively targeted by co-expressed microRNAs in cancer. BMC Bioinformatics 2007,8(Suppl 7):S16. 10.1186/1471-2105-8-S7-S16PubMed CentralPubMedGoogle Scholar
  20. Ptitsyn AA, Gimble JM: Analysis of circadian pattern reveals tissue-specific alternative transcription in leptin signaling pathway. BMC Bioinformatics 2007,8(Suppl 7):S15. 10.1186/1471-2105-8-S7-S15PubMed CentralPubMedGoogle Scholar
  21. Churbanov A, Baribault C, Winters-Hilt S: Duration learning for analysis of nanopore ionic current blockades. BMC Bioinformatics 2007,8(Suppl 7):S14. 10.1186/1471-2105-8-S7-S14PubMed CentralPubMedGoogle Scholar
  22. Li P, Zhang C, Perkins EJ, Gong P, Deng Y: Comparison of probabilistic Boolean network and dynamic Bayesian network approaches for inferring gene regulatory networks. BMC Bioinformatics 2007,8(Suppl 7):S13. 10.1186/1471-2105-8-S7-S13PubMed CentralPubMedGoogle Scholar
  23. Landry M, Winters-Hilt S: Analysis of nanopore detector measurements using Machine-Learning methods, with application to single-molecule kinetic analysis. BMC Bioinformatics 2007,8(Suppl 7):S12. 10.1186/1471-2105-8-S7-S12PubMed CentralPubMedGoogle Scholar
  24. Thomson K, Amin I, Morales E, Winters-Hilt S: Preliminary nanopore cheminformatics analysis of aptamer-target binding strength. BMC Bioinformatics 2007,8(Suppl 7):S11. 10.1186/1471-2105-8-S7-S11PubMed CentralPubMedGoogle Scholar
  25. Winters-Hilt S, Davis A, Amin I, Morales E: Nanopore current transduction analysis of protein binding to non-terminal and terminal DNA regions: analysis of transcription factor binding, retroviral DNA terminus dynamics, and retroviral integrase-DNA binding. BMC Bioinformatics 2007,8(Suppl 7):S10. 10.1186/1471-2105-8-S7-S10PubMed CentralPubMedGoogle Scholar
  26. Wilkins D, Gusev Y, Loganantharaj R, Bridges S, Winters-Hilt S, Wren JD: Proceedings of the Fourth Annual Conference of the MidSouth Computational Biology and Bioinformatics Society. BMC Bioinformatics 2007,8(Suppl 7):S1. 10.1186/1471-2105-8-S7-S1PubMed CentralPubMedGoogle Scholar
  27. Mete M, Tang F, Xu X, Yuruk N: A Structural Approach for Finding Functional Modules from Large Biological Networks. BMC Bioinformatics 2008,9(Suppl 9):S19. 10.1186/1471-2105-9-S9-S19PubMed CentralPubMedGoogle Scholar
  28. Dozmorov MG, Kyker KD, Hauser PJ, Saban R, Buethe DD, Dozmorov I, Centola MB, Culkin DJ, Hurst RE: From microarray to biology: An integrated experimental, statistical and in silico analysis of how the extracellular matrix modulates the phenotype of cancer cells. BMC Bioinformatics 2008,9(Suppl 9):S4. 10.1186/1471-2105-9-S9-S4PubMed CentralPubMedGoogle Scholar
  29. Ptitsyn AA, Weil MM, Thamm DH: Systems biology approach to identification of biomarkers for metastatic progression in cancer. BMC Bioinformatics 2008,9(Suppl 9):S8. 10.1186/1471-2105-9-S9-S8PubMed CentralPubMedGoogle Scholar
  30. Ptitsyn AA: Comprehensive analysis of circadian periodic pattern in plant transcriptome. BMC Bioinformatics 2008,9(Suppl 9):S18. 10.1186/1471-2105-9-S9-S18PubMed CentralPubMedGoogle Scholar
  31. Chae M, Shmookler Reis RJ, Thaden JJ: An iterative block-shifting approach to retention time alignment that preserves the shape and area of gas chromatography-mass spectrometry peaks. BMC Bioinformatics 2008,9(Suppl 9):S15. 10.1186/1471-2105-9-S1-S15PubMed CentralPubMedGoogle Scholar
  32. Huan T, Sivachenko AY, Harrison SH, Chen JY: ProteoLens: a Visual Analytic Tool for Multi-scale Database-driven Biological Network Data Mining. BMC Bioinformatics 2008,9(Suppl 9):S5. 10.1186/1471-2105-9-S9-S5PubMed CentralPubMedGoogle Scholar
  33. Bovelstad HM, Nygard S, Storvold HL, Aldrin M, Borgan O, Frigessi A, Lingjaerde OC: Predicting survival from microarray data–a comparative study. Bioinformatics 2007,23(16):2080–2087. 10.1093/bioinformatics/btm305PubMedGoogle Scholar
  34. Burkart MF, Wren JD, Herschkowitz JI, Perou CM, Garner HR: Clustering microarray-derived gene lists through implicit literature relationships. Bioinformatics 2007,23(15):1995–2003. 10.1093/bioinformatics/btm261PubMedGoogle Scholar
  35. Chen X, Hughes TR, Morris Q: RankMotif++: a motif-search algorithm that accounts for relative ranks of K-mers in binding transcription factors. Bioinformatics 2007,23(13):i72–79. 10.1093/bioinformatics/btm224PubMedGoogle Scholar
  36. Dyer MD, Murali TM, Sobral BW: Computational prediction of host-pathogen protein-protein interactions. Bioinformatics 2007,23(13):i159–166. 10.1093/bioinformatics/btm208PubMedGoogle Scholar
  37. Elo LL, Jarvenpaa H, Oresic M, Lahesmaa R, Aittokallio T: Systematic construction of gene coexpression networks with applications to human T helper cell differentiation process. Bioinformatics 2007,23(16):2096–2103. 10.1093/bioinformatics/btm309PubMedGoogle Scholar
  38. Fishel I, Kaufman A, Ruppin E: Meta-analysis of gene expression data: a predictor-based approach. Bioinformatics 2007,23(13):1599–1606. 10.1093/bioinformatics/btm149PubMedGoogle Scholar
  39. Fujita A, Sato JR, Garay-Malpartida HM, Morettin PA, Sogayar MC, Ferreira CE: Time-varying modeling of gene expression regulatory networks using the wavelet dynamic vector autoregressive method. Bioinformatics 2007,23(13):1623–1630. 10.1093/bioinformatics/btm151PubMedGoogle Scholar
  40. Gao S, Wang X: TAPPA: topological analysis of pathway phenotype association. Bioinformatics 2007,23(22):3100–3102. 10.1093/bioinformatics/btm460PubMed CentralPubMedGoogle Scholar
  41. Gyenesei A, Wagner U, Barkow-Oesterreicher S, Stolte E, Schlapbach R: Mining co-regulated gene profiles for the detection of functional associations in gene expression data. Bioinformatics 2007,23(15):1927–1935. 10.1093/bioinformatics/btm276PubMedGoogle Scholar
  42. Hibbs MA, Hess DC, Myers CL, Huttenhower C, Li K, Troyanskaya OG: Exploring the functional landscape of gene expression: directed search of large microarray compendia. Bioinformatics 2007,23(20):2692–2699. 10.1093/bioinformatics/btm403PubMedGoogle Scholar
  43. Lim WK, Wang K, Lefebvre C, Califano A: Comparative analysis of microarray normalization procedures: effects on reverse engineering gene networks. Bioinformatics 2007,23(13):i282–288. 10.1093/bioinformatics/btm201PubMedGoogle Scholar
  44. Martin S, Zhang Z, Martino A, Faulon JL: Boolean dynamics of genetic regulatory networks inferred from microarray time series data. Bioinformatics 2007,23(7):866–874. 10.1093/bioinformatics/btm021PubMedGoogle Scholar
  45. Medina I, Montaner D, Tarraga J, Dopazo J: Prophet, a web-based tool for class prediction using microarray data. Bioinformatics 2007,23(3):390–391. 10.1093/bioinformatics/btl602PubMedGoogle Scholar
  46. Mukhopadhyay ND, Chatterjee S: Causality and pathway search in microarray time series experiment. Bioinformatics 2007,23(4):442–449. 10.1093/bioinformatics/btl598PubMedGoogle Scholar
  47. Schreiber AW, Baumann U: A framework for gene expression analysis. Bioinformatics 2007,23(2):191–197. 10.1093/bioinformatics/btl591PubMedGoogle Scholar
  48. Sean D, Meltzer PS: GEOquery: a bridge between the Gene Expression Omnibus (GEO) and BioConductor. Bioinformatics 2007,23(14):1846–1847. 10.1093/bioinformatics/btm254Google Scholar
  49. Yu Z, Wong HS, Wang H: Graph-based consensus clustering for class discovery from gene expression data. Bioinformatics 2007,23(21):2888–2896. 10.1093/bioinformatics/btm463PubMedGoogle Scholar
  50. Su Z, Hong H, Fang H, Shi L, Perkins R, Tong W: Very Important Pool (VIP) Genes – An Application for Microarray-Based Molecular Signatures. BMC Bioinformatics 2008,9(Suppl 9):S9. 10.1186/1471-2105-9-S9-S9PubMed CentralPubMedGoogle Scholar
  51. Lee T, Desai VG, Velasco C, Shmookler Reis RJ, Delongchamp RR: Testing for treatment effects on gene ontology. BMC Bioinformatics 2008,9(Suppl 9):S20. 10.1186/1471-2105-9-S9-S20PubMed CentralPubMedGoogle Scholar
  52. Rawat A, Deng Y: Novel implementation of conditional co-regulation by graph theory to derive co-expressed genes from microarray data. BMC Bioinformatics 2008,9(Suppl 9):S7. 10.1186/1471-2105-9-S9-S7PubMed CentralPubMedGoogle Scholar
  53. Shi L, Jones WD, Jensen RV, Harris SC, Perkins RG, Goodsaid FM, Guo L, Croner LJ, Boysen C, Fang H, et al.: The Balance of Reproducibility, Sensitivity, and Specificity of Lists of Differentially Expressed Genes in Microarray Studies. BMC Bioinformatics 2008,9(Suppl 9):S10. 10.1186/1471-2105-9-S9-S10PubMed CentralPubMedGoogle Scholar
  54. Frank RL, Kandoth C, Ercal F: Validation of an NSP-based (negative selection pattern) gene family identification strategy. BMC Bioinformatics 2008,9(Suppl 9):S2. 10.1186/1471-2105-9-S9-S2PubMed CentralPubMedGoogle Scholar
  55. Hong H, Su Z, Ge W, Shi L, Perkins R, Fang H, Xu J, Chen JJ, Han T, Kaput J, et al.: Assessing Batch Effects of Genotype Calling Algorithm BRLMM for the Affymetrix GeneChip Human Mapping 500 K Array Set Using 270 HapMap Samples. BMC Bioinformatics 2008,9(Suppl 9):S17. 10.1186/1471-2105-9-S9-S17PubMed CentralPubMedGoogle Scholar
  56. Gamberoni G, Lamma E, Lodo G, Marchesini J, Mascellani N, Rossi S, Storari S, Tagliavini L, Volinia S: Fun&Co: identification of key functional differences in transcriptomes. Bioinformatics 2007,23(20):2725–2732. 10.1093/bioinformatics/btm425PubMedGoogle Scholar
  57. Hertzberg L, Izraeli S, Domany E: STOP: searching for transcription factor motifs using gene expression. Bioinformatics 2007,23(14):1737–1743. 10.1093/bioinformatics/btm249PubMedGoogle Scholar
  58. Hijikata A, Kitamura H, Kimura Y, Yokoyama R, Aiba Y, Bao Y, Fujita S, Hase K, Hori S, Ishii Y, et al.: Construction of an open-access database that integrates cross-reference information from the transcriptome and proteome of immune cells. Bioinformatics 2007,23(21):2934–2941. 10.1093/bioinformatics/btm430PubMedGoogle Scholar
  59. Meng H, Banerjee A, Zhou L: BLISS 2.0: a web-based tool for predicting conserved regulatory modules in distantly-related orthologous sequences. Bioinformatics 2007,23(23):3249–3250. 10.1093/bioinformatics/btm368PubMed CentralPubMedGoogle Scholar
  60. Wang RS, Wang Y, Zhang XS, Chen L: Inferring transcriptional regulatory networks from high-throughput data. Bioinformatics 2007,23(22):3056–3064. 10.1093/bioinformatics/btm465PubMedGoogle Scholar
  61. Yuan S, Li KC: Context-dependent clustering for dynamic cellular state modeling of microarray gene expression. Bioinformatics 2007,23(22):3039–3047. 10.1093/bioinformatics/btm457PubMedGoogle Scholar
  62. Zhu QH, Guo AY, Gao G, Zhong YF, Xu M, Huang M, Luo J: DPTF: a database of poplar transcription factors. Bioinformatics 2007,23(10):1307–1308. 10.1093/bioinformatics/btm113PubMedGoogle Scholar
  63. Montgomery SB, Griffith OL, Sleumer MC, Bergman CM, Bilenky M, Pleasance ED, Prychyna Y, Zhang X, Jones SJ: ORegAnno: an open access database and curation system for literature-derived promoters, transcription factor binding sites and regulatory variation. Bioinformatics 2006,22(5):637–640. 10.1093/bioinformatics/btk027PubMedGoogle Scholar
  64. Narlikar L, Gordan R, Ohler U, Hartemink AJ: Informative priors based on transcription factor structural class improve de novo motif discovery. Bioinformatics 2006,22(14):e384–392. 10.1093/bioinformatics/btl251PubMedGoogle Scholar
  65. Shipra A, Chetan K, Rao MR: CREMOFAC–a database of chromatin remodeling factors. Bioinformatics 2006,22(23):2940–2944. 10.1093/bioinformatics/btl509PubMedGoogle Scholar
  66. Sonnenburg S, Zien A, Ratsch G: ARTS: accurate recognition of transcription starts in human. Bioinformatics 2006,22(14):e472–480. 10.1093/bioinformatics/btl250PubMedGoogle Scholar
  67. Quest D, Dempsey K, Shafiullah M, Bastola D, Ali H: MTAP: The Motif Tool Assessment Platform. BMC Bioinformatics 2008,9(Suppl 9):S6. 10.1186/1471-2105-9-S9-S6PubMed CentralPubMedGoogle Scholar
  68. Kulkarni V, Errami M, Barber R, Garner HR: Exhaustive prediction of disease susceptibility to coding base changes in the human genome. BMC Bioinformatics 2008,9(Suppl 9):S3. 10.1186/1471-2105-9-S9-S3PubMed CentralPubMedGoogle Scholar
  69. Zielinski JS, Bouaynaya N, Schonfeld D, O'Neil W: Time-dependent ARMA modeling of genomic sequences. BMC Bioinformatics 2008,9(Suppl 9):S14. 10.1186/1471-2105-9-S9-S14PubMed CentralPubMedGoogle Scholar
  70. Minguez P, Al-Shahrour F, Montaner D, Dopazo J: Functional profiling of microarray experiments using text-mining derived bioentities. Bioinformatics 2007,23(22):3098–3099. 10.1093/bioinformatics/btm445PubMedGoogle Scholar
  71. Torvik VI, Smalheiser NR: A quantitative model for linking two disparate sets of articles in MEDLINE. Bioinformatics 2007,23(13):1658–1665. 10.1093/bioinformatics/btm161PubMedGoogle Scholar
  72. Xu H, Fan JW, Hripcsak G, Mendonca EA, Markatou M, Friedman C: Gene symbol disambiguation using knowledge-based profiles. Bioinformatics 2007,23(8):1015–1022. 10.1093/bioinformatics/btm056PubMedGoogle Scholar
  73. Chen ES, Hripcsak G, Xu H, Markatou M, Friedman C: Automated acquisition of disease drug knowledge from biomedical and clinical documents: an initial study. J Am Med Inform Assoc 2008,15(1):87–98. 10.1197/jamia.M2401PubMed CentralPubMedGoogle Scholar
  74. Lin Y, Li W, Chen K, Liu Y: A document clustering and ranking system for exploring MEDLINE citations. J Am Med Inform Assoc 2007,14(5):651–661. 10.1197/jamia.M2215PubMed CentralPubMedGoogle Scholar
  75. Liu H, Hu ZZ, Torii M, Wu C, Friedman C: Quantitative assessment of dictionary-based protein named entity tagging. J Am Med Inform Assoc 2006,13(5):497–507. 10.1197/jamia.M2085PubMed CentralPubMedGoogle Scholar
  76. Fan JW, Friedman C: Semantic classification of biomedical concepts using distributional similarity. J Am Med Inform Assoc 2007,14(4):467–477. 10.1197/jamia.M2314PubMed CentralPubMedGoogle Scholar
  77. Huang Y, Lowe HJ: A novel hybrid approach to automated negation detection in clinical radiology reports. J Am Med Inform Assoc 2007,14(3):304–311. 10.1197/jamia.M2284PubMed CentralPubMedGoogle Scholar
  78. Uzuner O, Luo Y, Szolovits P: Evaluating the state-of-the-art in automatic de-identification. J Am Med Inform Assoc 2007,14(5):550–563. 10.1197/jamia.M2444PubMed CentralPubMedGoogle Scholar
  79. Giles CB, Wren JD: Large-scale Directional Relationship Extraction and Resolution. BMC Bioinformatics 2008,9(Suppl 9):S11. 10.1186/1471-2105-9-S9-S11PubMed CentralPubMedGoogle Scholar
  80. Bottoms CA, Xu D: Wanted: Unique names for unique atom positions. PDB-wide analysis of diastereotopic atom names of small molecules containing diphosphate. BMC Bioinformatics 2008,9(Suppl 9):S16. 10.1186/1471-2105-9-S9-S16PubMed CentralPubMedGoogle Scholar
  81. Roux B, Merat S, Winters-Hilt S: Hybrid SVM/HMM Structural Sensors for Stochastic Sequential Data. BMC Bioinformatics 2008,9(Suppl 9):S12. 10.1186/1471-2105-9-S9-S12PubMed CentralPubMedGoogle Scholar
  82. Churbanov A, Winters-Hilt S: Clustering ionic flow blockade toggles with a Mixture of HMMs. BMC Bioinformatics 2008,9(Suppl 9):S13. 10.1186/1471-2105-9-S9-S13PubMed CentralPubMedGoogle Scholar


© Wren et al; licensee BioMed Central Ltd. 2008

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.