Proceedings of the 2010 MidSouth Computational Biology and Bioinformatics Society (MCBIOS) Conference
© Wren et al; licensee BioMed Central Ltd. 2010
Published: 07 October 2010
The seventh annual Midsouth Computational Biology and Bioinformatics (MCBIOS) conference took place February 19 and 20, 2010 at Arkansas State University in Jonesboro, AR, presided over by Daniel Berleant, this year’s President of MCBIOS. Keynote speakers were Elaine Ostrander of NIH, renowned for her work on dog genomics, Clayton Naeve, CIO of St. Jude Children's Research Hospital, and Robert Cottingham, Leader of the Computational Biology and Bioinformatics Group, Oak Ridge National Laboratory. The record-breaking attendance exceeded 200 participants which necessitated parallel talk sessions for the first time. Student oral presentation award winners were Heidi Pagan (first place) of Mississippi State University (MSU), Juliet Tang (second place) of MSU, and Aleksandra Markovets (third) of Mississippi Valley State University. In addition, a record number of posters were also presented. Due to the number of posters, student poster awards were given in two categories, Biological Focus and Computational Focus. Winners were N. Platt and V. Chaitankar (1st place), G. Cooper and L. Pillai (2nd), and M. Ammari and C. Gearheart (3rd). Reflecting the integration of these foci in the field, winners in one category frequently scored high in the other category as well. MCBIOS is also pleased to have this year acquired legal status as a non-profit organization.
A total of twenty eight papers describing research presented at the 2010 conference were accepted for publication in these proceedings out of a total of forty three papers submitted for consideration (~65%). The number of papers submitted for consideration was the largest submitted since the inception of MCBIOS with a 34% increase over last year’s proceedings [1–19], and the highest number of papers submitted to date, reflecting a healthy growth in conference participation. All papers were peer-reviewed by at least two reviewers. Our goal for inclusion of papers was to be inclusive, yet rigorous in the peer-review process such that accepted papers are both high quality and reflective of the work presented at the conference. Papers generally fell into five categories:
Bioinformatics was largely born as a field through the need to analyze and understand sequence data. Partly as a result of ultra-high-throughput sequencing technology, the field has seen a strong resurgence recently with new computational methods being developed and applied to downstream applications such as the identification of human mutations [20–25] as well as meta-genomics and whole genome sequencing [26–31]. In these proceedings, several new methods and applications are reported. Hong Fang, et al. describe an expansion of the FDA-developed, ArrayTrackTM genomics tool, into a platform that supports microbial data to enable the rapid detection of food borne pathogenic bacteria during outbreak scenarios . Xu et al. describe new SNP (single nucleotide polymorphism) and QTL (quantitative trait locus) libraries for the ArrayTrack system which provide users with extensive data including links to other resources, and motivated in part by the need to augment gene association study results with biological context . Christopher Bottoms et al. report on the new web-based tool, IView, which graphically displays and makes searchable introgression data by marker name, chromosome number, or map position .
Quest and colleagues working in the Cottingham laboratory address the problem of brittle genome annotation systems with an OWL-based approach that provides several significant advantages . Work reported by Markovets and colleagues on identifying promoter regions showed that a new neural network-based algorithm outperformed the method of stress-induced duplex destabilization . Smits and Ouverney present a software system that assists users in analyzing sequence data to find phylogenetic trees, while avoiding pitfalls associated with this process as commonly practiced .
An examination of substitution rates over evolutionary time by Ulrich Melcher compared viral and non-viral sequences to reveal that the rate of nucleotide interchanges in plant viruses is not constant, impacting phylogeny studies. The evidence suggests that different evolutionary rules may apply to recent divergences than to those linked to distant speciation events .
An in depth analysis of the two forms of Bovine Viral Diarrhea, a worldwide cattle disease, was completed by Mais Ammari et al. using a proteomics approach to evaluate the host protein expression differences. Two gene sets were found, each specific to one disease form which showed significant functional differences . Arun Rawat et al. address the need for additional genomic information for Northern bobwhite, an avian species critical to ecosystem maintenance. Using next generation sequencing of cDNAs from a multi-tissue library, they generated ESTs which were pipelined into a unigene database and made publicly available at a searchable website .
Cyriac Kandoth et al  mined the Arabidopsis thaliana genome using a new method to scan genome sequences for partially symmetric inverted repeats that might represent miRNA precursors. Chao Di et al  carried out a comparative genome analysis of the Prohibitin gene, important functions from diseases to development, finding that the gene family is conserved across different kingdoms falling into five different clades and indicating that different duplication events were involved for gene family expansion, especially the segmental duplications in Arabidopsis. The conserved gene evolution indicated that the study in the model species can be translational to human disease studies. Rowena Kelly et al  have constructed a database with a web-based interface that integrates large datasets on Aspergillus flavus resistance in Maize such as gene expression, proteomic, QTL genetic mapping studies and sequence data from the literature to help identify and select candidate A. flavus resistance genes.
Biological information may be encoded in a one-dimensional sequence, but its manifestation takes place in three-dimensional space in the form of proteins, cells and tissues among other structures. Computational analysis and prediction of these structures is challenging, to say the least, and an active area of recent bioinformatics interest [44–48]. Computational simulations are critical for testing proposed solutions, as well as in rationally engineering more specific binding/activity for drug candidates.
To better understand and improve of the creation of anti-inflammatory drugs through inhibition of lipoxygenase, an essential enzyme in the inflammatory pathway, Shuju Bai et al. report on a method for modeling interactions between 8R-lipoxygenase and its substrate . A mixed reality surgical simulator with a VICON motion tracking system, developed for the rasping procedure in artificial cervical disc replacement surgeries is described by Tansel Halic et al., which can serve as an important training and practice tool for surgeons . The potential of using hydrogen deuterium exchange (HDX) mass spectrometry for analyzing enzymes that degrade cell walls is explored by Uzuner et al.. They report that the HDX mass spectrometry can be a powerful tool for exploring the molecular mechanisms of enzyme functions.
Sinan Kockara et al  and Mutlu Mete et al  examined ways to automatically quantitate and define the extent of malignant melanomas on skin while offering speed and consistency in detection of the lesions borders, elements especially useful in areas without dermatologists. Kockara et al compared density based clustering and Fuzzy C-Means clustering algorithms for border detection in dermoscopy images finding that density based clustering performed best with a low border error, high precision and recall; however, the Fuzzy C-Means clustering algorithm had poor performance especially in border detection. Mete et al proposed two new methods, boundary driven density based clustering based algorithms which performed better delineation with noisy images and an active contour model that gave the best detection with optimum parameters. Denise Koessler et al  developed a tool to predict secondary RNA structures based on the novel approach of using graph-theoretic values as input for a neural network and computes the probability of secondary RNA structure.
There are many areas within biology that are amenable to computational analysis, each of which is usually approached separately. Somewhat recently, the term “systems biology” has emerged to describe interconnected analyses, particularly those that help reduce complexity in these systems, as defined by the number of interconnections between component parts, into a more functional understanding (e.g., input and output). The term “systems biology” is often used broadly and not always consistently, but is an emergent area of high interest in bioinformatics [55–57].
A novel method for identifying subnetwork markers in a human protein-protein interaction network is described by Junjie Su et al.. They report the identification of subnetwork markers that improve upon current gene-based and pathway-based markers in their discriminative power, reproducibility and classification. Elina Tjioe et al. report on the development of a Web-based gene-discovery bioinformatics tool, FAUN (Feature Annotation Using Nonnegative matrix factorization), which identifies implied gene relationships from biomedical literature, enabling hypotheses on functional characterization .
A paper by Vijender Chaitankar, et al., explores the important problem of inferring gene regulatory networks from time sliced microarray data. They claim that mutual information (MI) based approaches have inherent limits to their capabilities in this context which they were able to improve upon with algorithms based on modifications to the mutual information metric .
Kumar and Nanduri present a downloadable database, HPIDB, which serves as a resource of host-pathogen protein-protein interactions integrated non-redundantly from public databases. The authors report on the flexibility of the database for querying and an output format which allows portage to downstream applications .
Microarrays have long been a subject of bioinformatics analysis not only for better understanding transcription, a process affected by both genetic and epigenetic factors [62, 63], but also as a model for large-scale analysis of genetic behavior.
A new non-stationary Dynamic Bayesian Network with a flexible lag choosing mechanism that detects potential genetic regulators and has improved structure prediction is reported by Yi Jia and Jun Huan .
Zhining Wen et al. address two interesting QC questions of whether expired Affymetrix GeneChip microarrays, an expensive resource, are still useful up to four years after expiration and if RNA stored at -80°C for the same period was suitable as template source. In spite of a decrease in sensitivity, the authors found that these arrays generated data consistent with that from unexpired arrays and report favorably on the stability of the RNA .
MCBIOS 2011 will be held in College Station, Texas. The 2010-2011 President of MCBIOS is Ulisses Braga-Neto of the University of Texas A&M, and Susan Bridges of Mississippi State University is now the President-elect. MCBIOS is a regional affiliate of the International Society for Computational Biology (http://www.ISCB.org). For information regarding MCBIOS and our future meetings, see http://www.MCBIOS.org.
All authors served as editors for these proceedings, with JDW serving as Senior Editor. All authors helped write this editorial.
We would like to thank the many anonymous peer reviewers who helped to ensure the quality of these Proceedings, and Dr. Kristi Koelsch for help with manuscript development. EP was supported by the U.S. Army Environmental Quality Technology Research Program. Permission to publish this information was granted by the Chief of Engineers.
This article has been published as part of BMC Bioinformatics Volume 11 Supplement 6, 2010: Proceedings of the Seventh Annual MCBIOS Conference. Bioinformatics: Systems, Biology, Informatics and Computation. The full contents of the supplement are available online at http://www.biomedcentral.com/1471-2105/11?issue=S6.
- Bright LA, et al.: Structural and functional-annotation of an equine whole genome oligoarray. BMC Bioinformatics 2009, 10(Suppl 11):S8. 10.1186/1471-2105-10-S11-S8PubMed CentralView ArticlePubMedGoogle Scholar
- Buza TJ, et al.: Facilitating functional annotation of chicken microarray data. BMC Bioinformatics 2009, 10(Suppl 11):S2. 10.1186/1471-2105-10-S11-S2PubMed CentralView ArticlePubMedGoogle Scholar
- Chavan SS, et al.: NATbox: a network analysis toolbox in R. BMC Bioinformatics 2009, 10(Suppl 11):S14. 10.1186/1471-2105-10-S11-S14PubMed CentralView ArticlePubMedGoogle Scholar
- Chen B, Johnson M: Protein local 3D structure prediction by Super Granule Support Vector Machines (Super GSVM). BMC Bioinformatics 2009, 10(Suppl 11):S15. 10.1186/1471-2105-10-S11-S15PubMed CentralView ArticlePubMedGoogle Scholar
- Chowbina SR, et al.: HPD: an online integrated human pathway database enabling systems biology studies. BMC Bioinformatics 2009, 10(Suppl 11):S5. 10.1186/1471-2105-10-S11-S5PubMed CentralView ArticlePubMedGoogle Scholar
- Gao C, et al.: Graph ranking for exploratory gene data analysis. BMC Bioinformatics 2009, 10(Suppl 11):S19. 10.1186/1471-2105-10-S11-S19PubMed CentralView ArticlePubMedGoogle Scholar
- Garcia-Reyero N, et al.: Site-specific impacts on gene expression and behavior in fathead minnows (Pimephales promelas) exposed in situ to streams adjacent to sewage treatment plants. BMC Bioinformatics 2009, 10(Suppl 11):S11. 10.1186/1471-2105-10-S11-S11PubMed CentralView ArticlePubMedGoogle Scholar
- Jankun-Kelly TJ, Lindeman AD, Bridges SM: Exploratory visual analysis of conserved domains on multiple sequence alignments. BMC Bioinformatics 2009, 10(Suppl 11):S7. 10.1186/1471-2105-10-S11-S7PubMed CentralView ArticlePubMedGoogle Scholar
- Li Z, et al.: Microarray platform consistency is revealed by biologically functional analysis of gene expression profiles. BMC Bioinformatics 2009, 10(Suppl 11):S12. 10.1186/1471-2105-10-S11-S12PubMed CentralView ArticlePubMedGoogle Scholar
- Malone BM, Perkins AD, Bridges SM: Integrating phenotype and gene expression data for predicting gene function. BMC Bioinformatics 2009, 10(Suppl 11):S20. 10.1186/1471-2105-10-S11-S20PubMed CentralView ArticlePubMedGoogle Scholar
- Mete M, et al.: Automatic identification of angiogenesis in double stained images of liver tissue. BMC Bioinformatics 2009, 10(Suppl 11):S13. 10.1186/1471-2105-10-S11-S13PubMed CentralView ArticlePubMedGoogle Scholar
- Pendarvis K, et al.: An automated proteomic data analysis workflow for mass spectrometry. BMC Bioinformatics 2009, 10(Suppl 11):S17. 10.1186/1471-2105-10-S11-S17PubMed CentralView ArticlePubMedGoogle Scholar
- Perkins AD, Langston MA: Threshold selection in gene co-expression networks using spectral graph theory techniques. BMC Bioinformatics 2009, 10(Suppl 11):S4. 10.1186/1471-2105-10-S11-S4PubMed CentralView ArticlePubMedGoogle Scholar
- Ptitsyn A: Computational analysis of gene expression space associated with metastatic cancer. BMC Bioinformatics 2009, 10(Suppl 11):S6. 10.1186/1471-2105-10-S11-S6PubMed CentralView ArticlePubMedGoogle Scholar
- van den Berg BH, et al.: Comparing gene annotation enrichment tools for functional modeling of agricultural microarray data. BMC Bioinformatics 2009, 10(Suppl 11):S9. 10.1186/1471-2105-10-S11-S9View ArticlePubMedGoogle Scholar
- Xu Z, et al.: Comparative genome analysis of lignin biosynthesis gene families across the plant kingdom. BMC Bioinformatics 2009, 10(Suppl 11):S3. 10.1186/1471-2105-10-S11-S3PubMed CentralView ArticlePubMedGoogle Scholar
- Zhang L, et al.: PathBinder--text empirics and automatic extraction of biomolecular interactions. BMC Bioinformatics 2009, 10(Suppl 11):S18. 10.1186/1471-2105-10-S11-S18View ArticleGoogle Scholar
- Zhou X, et al.: Novel software package for cross-platform transcriptome analysis (CPTRA). BMC Bioinformatics 2009, 10(Suppl 11):S16. 10.1186/1471-2105-10-S11-S16PubMed CentralView ArticlePubMedGoogle Scholar
- Zollanvari A, et al.: Analysis and modeling of time-course gene-expression profiles from nanomaterial-exposed primary human epidermal keratinocytes. BMC Bioinformatics 2009, 10(Suppl 11):S10. 10.1186/1471-2105-10-S11-S10PubMed CentralView ArticlePubMedGoogle Scholar
- Holden M, et al.: GSEA-SNP: applying gene set enrichment analysis to SNP data from genome-wide association studies. Bioinformatic 2008, 24(23):2784–5. 10.1093/bioinformatics/btn516View ArticleGoogle Scholar
- Homer N, et al.: Multimarker analysis and imputation of multiple platform pooling-based genome-wide association studies. Bioinformatics 2008, 24(17):1896–902. 10.1093/bioinformatics/btn333PubMed CentralView ArticlePubMedGoogle Scholar
- Pirinen M: Estimating population haplotype frequencies from pooled SNP data using incomplete database information. Bioinformatics 2009, 25(24):3296–302. 10.1093/bioinformatics/btp584View ArticlePubMedGoogle Scholar
- Wegrzyn JL, et al.: PineSAP--sequence alignment and SNP identification pipeline. Bioinformatics 2009, 25(19):2609–10. 10.1093/bioinformatics/btp477PubMed CentralView ArticlePubMedGoogle Scholar
- O'Dushlaine C, et al.: The SNP ratio test: pathway analysis of genome-wide association datasets. Bioinformatics 2009, 25(20):2762–3. 10.1093/bioinformatics/btp448View ArticlePubMedGoogle Scholar
- Manske HM, Kwiatkowski DP: SNP-o-matic. Bioinformatics 2009, 25(18):2434–5. 10.1093/bioinformatics/btp403PubMed CentralView ArticlePubMedGoogle Scholar
- Monzoorul Haque M, et al.: SOrt-ITEMS: Sequence orthology based approach for improved taxonomic estimation of metagenomic sequences. Bioinformatics 2009, 25(14):1722–30. 10.1093/bioinformatics/btp317View ArticlePubMedGoogle Scholar
- Hu GQ, et al.: MetaTISA: Metagenomic Translation Initiation Site Annotator for improving gene start prediction. Bioinformatics 2009, 25(14):1843–5. 10.1093/bioinformatics/btp272View ArticlePubMedGoogle Scholar
- Huang Y, Gilna P, Li W: Identification of ribosomal RNA genes in metagenomic fragments. Bioinformatics 2009, 25(10):1338–40. 10.1093/bioinformatics/btp161PubMed CentralView ArticlePubMedGoogle Scholar
- Kristiansson E, Hugenholtz P, Dalevi D: ShotgunFunctionalizeR: an R-package for functional comparison of metagenomes. Bioinformatics 2009, 25(20):2737–8. 10.1093/bioinformatics/btp508View ArticlePubMedGoogle Scholar
- Kim JH, et al.: HAPLOWSER: a whole-genome haplotype browser for personal genome and metagenome. Bioinformatics 2009, 25(18):2430–1. 10.1093/bioinformatics/btp399PubMed CentralView ArticlePubMedGoogle Scholar
- Mitra S, Klar B, Huson DH: Visual and statistical comparison of metagenomes. Bioinformatics 2009, 25(15):1849–55. 10.1093/bioinformatics/btp341View ArticlePubMedGoogle Scholar
- Fang H, et al.: An FDA Bioinformatics Tool for Microbial Genomics Research on Molecular Characterization of Bacterial Foodborne Pathogens Using Microarrays. BMC Bioinformatics 2010, 11(Suppl 6):S4. 10.1186/1471-2105-11-S6-S4PubMed CentralView ArticlePubMedGoogle Scholar
- Xu J, et al.: Two New ArrayTrack Libraries for Personalized Biomedical Research. BMC Bioinformatics 2010, 11(Suppl 6):S6. 10.1186/1471-2105-11-S6-S6PubMed CentralView ArticlePubMedGoogle Scholar
- Bottoms CA, Flint-Garcia S, McMullen MD: IView: Introgression library visualization and query tool. BMC Bioinformatics 2010, 11(Suppl 6):S28. 10.1186/1471-2105-11-S6-S28PubMed CentralView ArticlePubMedGoogle Scholar
- Quest DJ, et al.: Next Generation Models for Storage and Representation of Microbial Biological Annotation. BMC Bioinformatics 2010, 11(Suppl 6):S15. 10.1186/1471-2105-11-S6-S15PubMed CentralView ArticlePubMedGoogle Scholar
- Bland C, Newsome AS, Markovets AA: Promoter prediction in E. coli based on SIDD profiles and Artificial Neural Networks. BMC Bioinformatics 2010, 11(Suppl 6):S17. 10.1186/1471-2105-11-S6-S17PubMed CentralView ArticlePubMedGoogle Scholar
- Smits SA, Ouverney CC: Phylometrics: a pipeline for inferring phylogenetic trees from a sequence relationship network perspective. BMC Bioinformatics 2010, 11(Suppl 6):S18. 10.1186/1471-2105-11-S6-S18PubMed CentralView ArticlePubMedGoogle Scholar
- Melcher U: Assessing constancy of substitution rates in viruses over evolutionary time. BMC Bioinformatics 2010, 11(Suppl 6):S3. 10.1186/1471-2105-11-S6-S3PubMed CentralView ArticlePubMedGoogle Scholar
- Ammari M, et al.: Analysis of Bovine Viral Diarrhea Viruses-infected monocytes: identification of cytopathic and non-cytopathic biotype differences. BMC Bioinformatics 2010, 11(Suppl 6):S9. 10.1186/1471-2105-11-S6-S9PubMed CentralView ArticlePubMedGoogle Scholar
- Rawat A, et al.: Quail Genomics: a knowledgebase for Northern bobwhite. BMC Bioinformatics 2010, 11(Suppl 6):S13. 10.1186/1471-2105-11-S6-S13PubMed CentralView ArticlePubMedGoogle Scholar
- Kandoth C, Ercal F, Frank RL: A framework for automated enrichment of functionally significant inverted repeats in whole genomes. BMC Bioinformatics 2010, 11(Suppl 6):S20. 10.1186/1471-2105-11-S6-S20PubMed CentralView ArticlePubMedGoogle Scholar
- Di C, et al.: Comparative genome analysis of PHB gene family reveals deep evolutionary origins and diverse gene function. BMC Bioinformatics 2010, 11(Suppl 6):S22. 10.1186/1471-2105-11-S6-S22PubMed CentralView ArticlePubMedGoogle Scholar
- Kelley RY, et al.: Integrated Database for Identifying Candidate Genes for Aspergillus flavus Resistance in Maize. BMC Bioinformatics 2010, 11(Suppl 6):S25. 10.1186/1471-2105-11-S6-S25PubMed CentralView ArticlePubMedGoogle Scholar
- La D, et al.: 3D-SURFER: software for high-throughput protein surface comparison and analysis. Bioinformatics 2009, 25(21):2843–4. 10.1093/bioinformatics/btp542PubMed CentralView ArticlePubMedGoogle Scholar
- Lee S, Blundell TL: BIPA: a database for protein-nucleic acid interaction in 3D structures. Bioinformatics 2009, 25(12):1559–60. 10.1093/bioinformatics/btp243View ArticlePubMedGoogle Scholar
- Preibisch S, Saalfeld S, Tomancak P: Globally optimal stitching of tiled 3D microscopic image acquisitions. Bioinformatics 2009, 25(11):1463–5. 10.1093/bioinformatics/btp184PubMed CentralView ArticlePubMedGoogle Scholar
- Mann M, et al.: CPSP-web-tools: a server for 3D lattice protein studies. Bioinformatics 2009, 25(5):676–7. 10.1093/bioinformatics/btp034PubMed CentralView ArticlePubMedGoogle Scholar
- Stratmann D, van Heijenoort C, Guittet E: NOEnet--use of NOE networks for NMR resonance assignment of proteins with known 3D structure. Bioinformatics 2009, 25(4):474–81. 10.1093/bioinformatics/btn638PubMed CentralView ArticlePubMedGoogle Scholar
- Bai S, Du T, Khosravi E: Applying internal coordinate mechanics to model the interactions between 8R-lipoxygenase and its substrate. BMC Bioinformatics 2010, 11(Suppl 6):S2. 10.1186/1471-2105-11-S6-S2PubMed CentralView ArticlePubMedGoogle Scholar
- Halic T, et al.: Mixed reality simulation of rasping procedure in artificial cervical disc replacement (ACDR) surgery. BMC Bioinformatics 2010, 11(Suppl 6):S11. 10.1186/1471-2105-11-S6-S11PubMed CentralView ArticlePubMedGoogle Scholar
- Uzuner U, et al.: Enzyme Structure Dynamics of Xylanase I from Trichoderma longibrachiatum. BMC Bioinformatics 2010, 11(Suppl 6):S12. 10.1186/1471-2105-11-S6-S12PubMed CentralView ArticlePubMedGoogle Scholar
- Kockara S, et al.: Analysis of density based and fuzzy c-means clustering methods on lesion border extraction in dermoscopy images. BMC Bioinformatics 2010, 11(Suppl 6):S26. 10.1186/1471-2105-11-S6-S26PubMed CentralView ArticlePubMedGoogle Scholar
- Mete M, Sirakov NM: Lesion detection in dermoscopy images with novel density-based and active contour approaches. BMC Bioinformatics 2010, 11(Suppl 6):S23. 10.1186/1471-2105-11-S6-S23PubMed CentralView ArticlePubMedGoogle Scholar
- Koessler D, et al.: A predictive model for secondary RNA structure using graph theory and a neural network. BMC Bioinformatics 2010, 11(Suppl 6):S21. 10.1186/1471-2105-11-S6-S21PubMed CentralView ArticlePubMedGoogle Scholar
- Bois FY: GNU MCSim: Bayesian statistical inference for SBML-coded systems biology models. Bioinformatics 2009, 25(11):1453–4. 10.1093/bioinformatics/btp162View ArticlePubMedGoogle Scholar
- Reyes-Palomares A, et al.: Systems biology metabolic modeling assistant: an ontology-based tool for the integration of metabolic data in kinetic modeling. Bioinformatics 2009, 25(6):834–5. 10.1093/bioinformatics/btp061View ArticlePubMedGoogle Scholar
- Mirschel S, et al.: PROMOT: modular modeling for systems biology. Bioinformatics 2009, 25(5):687–9. 10.1093/bioinformatics/btp029PubMed CentralView ArticlePubMedGoogle Scholar
- Su J, Yoon B, Dougherty ER: Identification of diagnostic subnetwork markers for cancer in human protein-protein interaction network. BMC Bioinformatics 2010, 11(Suppl 6):S8. 10.1186/1471-2105-11-S6-S8PubMed CentralView ArticlePubMedGoogle Scholar
- Tjioe E, Berry MW, Homayouni R: Discovering gene functional relationships using FAUN (Feature Annotation Using Nonnegative matrix factorization). BMC Bioinformatics 2010, 11(Suppl 6):S14. 10.1186/1471-2105-11-S6-S14PubMed CentralView ArticlePubMedGoogle Scholar
- Chaitankar V, et al.: Time lagged information theoretic approaches to the reverse engineering of gene regulatory networks. BMC Bioinformatics 2010, 11(Suppl 6):S19. 10.1186/1471-2105-11-S6-S19PubMed CentralView ArticlePubMedGoogle Scholar
- Kumar R, Nanduri B: HPIDB - a unified resource for host-pathogen interactions. BMC Bioinformatics 2010, 11(Suppl 6):S16. 10.1186/1471-2105-11-S6-S16PubMed CentralView ArticlePubMedGoogle Scholar
- Hughes T, et al.: DNA methylome in human CD4+ T cells identifies transcriptionally repressive and non-repressive methylation peaks. Genes Immun 2010.Google Scholar
- Sawalha AH: Epigenetics and T-cell immunity. Autoimmunity 2008, 41(4):245–52. 10.1080/08916930802024145View ArticlePubMedGoogle Scholar
- Jia Y, Huan J: Constructing Non-Stationary Dynamic Bayesian Networks with a Flexible Lag Choosing Mechanism. BMC Bioinformatics 2010, 11(Suppl 6):S27. 10.1186/1471-2105-11-S6-S27PubMed CentralView ArticlePubMedGoogle Scholar
- Wen Z, et al.: Evaluation of gene expression data generated from expired Affymetrix GeneChip® microarrays using MAQC reference RNA samples. BMC Bioinformatics 2010, 11(Suppl 6):S10. 10.1186/1471-2105-11-S6-S10PubMed CentralView ArticlePubMedGoogle Scholar
- Ding D, et al.: The EDKB: an Established Knowledge Base for Endocrine Disrupting Chemicals. BMC Bioinformatics 2010, 11(Suppl 6):S5. 10.1186/1471-2105-11-S6-S5View ArticleGoogle Scholar
- Ghosh P, et al.: Dynamics of Protofibril Elongation and Association involved in Aβ42 Peptide Aggregation in Alzheimer’s Disease. BMC Bioinformatics 2010, 11(Suppl 6):S24. 10.1186/1471-2105-11-S6-S24PubMed CentralView ArticlePubMedGoogle Scholar
- Manda P, et al.: GOModeler- A tool for hypothesis-testing of functional genomics datasets. BMC Bioinformatics 2010, 11(Suppl 6):S29. 10.1186/1471-2105-11-S6-S29PubMed CentralView ArticlePubMedGoogle Scholar
- Zhou A, Zhang F, Chen J: PEPPI: A Peptidomic Database of Human Protein Isoforms for Proteomics Experiments. BMC Bioinformatics 2010, 11(Suppl 6):S7. 10.1186/1471-2105-11-S4-P7PubMed CentralView ArticlePubMedGoogle Scholar
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