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  1. Data integration is currently one of the main challenges in the biomedical sciences. Often different pieces of information are gathered on the same set of entities (e.g., tissues, culture samples, biomolecules...

    Authors: Katrijn Van Deun, Age K Smilde, Mariët J van der Werf, Henk AL Kiers and Iven Van Mechelen
    Citation: BMC Bioinformatics 2009 10:246
  2. Concurrent peptide fragmentation (i.e. shotgun CID, parallel CID or MSE) has emerged as an alternative to data-dependent acquisition in generating peptide fragmentation data in LC-MS/MS proteomics experiments. Co...

    Authors: Jason WH Wong, Alexander B Schwahn and Kevin M Downard
    Citation: BMC Bioinformatics 2009 10:244
  3. Existing algorithms and methods for forming diverse core subsets currently address either allele representativeness (breeder's preference) or allele richness (taxonomist's preference). The main objective of th...

    Authors: Chris Thachuk, José Crossa, Jorge Franco, Susanne Dreisigacker, Marilyn Warburton and Guy F Davenport
    Citation: BMC Bioinformatics 2009 10:243
  4. Although the use of clustering methods has rapidly become one of the standard computational approaches in the literature of microarray gene expression data analysis, little attention has been paid to uncertain...

    Authors: Richard S Savage, Katherine Heller, Yang Xu, Zoubin Ghahramani, William M Truman, Murray Grant, Katherine J Denby and David L Wild
    Citation: BMC Bioinformatics 2009 10:242
  5. Proteins interact through specific binding interfaces that contain many residues in domains. Protein interactions thus occur on three different levels of a concept hierarchy: whole-proteins, domains, and resid...

    Authors: Kevin Y Yip, Philip M Kim, Drew McDermott and Mark Gerstein
    Citation: BMC Bioinformatics 2009 10:241
  6. Gene set analysis based on Gene Ontology (GO) can be a promising method for the analysis of differential expression patterns. However, current studies that focus on individual GO terms have limited analytical ...

    Authors: Tao Xu, JianLei Gu, Yan Zhou and LinFang Du
    Citation: BMC Bioinformatics 2009 10:240
  7. Transcriptome sequences provide a complement to structural genomic information and provide snapshots of an organism's transcriptional profile. Such sequences also represent an alternative method for characteri...

    Authors: Roberto T Arrial, Roberto C Togawa and Marcelo de M Brigido
    Citation: BMC Bioinformatics 2009 10:239
  8. For many gene structures it is impossible to resolve intensity data uniquely to establish abundances of splice variants. This was empirically noted by Wang et al. in which it was called a "degeneracy problem". Th...

    Authors: Yiyuan She, Earl Hubbell and Hui Wang
    Citation: BMC Bioinformatics 2009 10:237
  9. High-throughput "omics" based data analysis play emerging roles in life sciences and molecular diagnostics. This emphasizes the urgent need for user-friendly windows-based software interfaces that could proces...

    Authors: Christian Schwager, Ute Wirkner, Amir Abdollahi and Peter E Huber
    Citation: BMC Bioinformatics 2009 10:235
  10. Integration of biological knowledge encoded in various lists of functionally related genes has become one of the most important aspects of analyzing genome-wide functional genomics data. In the context of clus...

    Authors: Johannes M Freudenberg, Vineet K Joshi, Zhen Hu and Mario Medvedovic
    Citation: BMC Bioinformatics 2009 10:234
  11. Bisulfite sequencing is a powerful technique to study DNA cytosine methylation. Bisulfite treatment followed by PCR amplification specifically converts unmethylated cytosines to thymine. Coupled with next gene...

    Authors: Yuanxin Xi and Wei Li
    Citation: BMC Bioinformatics 2009 10:232
  12. Efficient and accurate prediction of protein function from sequence is one of the standing problems in Biology. The generalised use of sequence alignments for inferring function promotes the propagation of err...

    Authors: Daniel Faria, António EN Ferreira and André O Falcão
    Citation: BMC Bioinformatics 2009 10:231
  13. Metabolome analysis with GC/MS has meanwhile been established as one of the "omics" techniques. Compound identification is done by comparison of the MS data with compound libraries. Mass spectral libraries in ...

    Authors: Bernhard Thielen, Stephanie Heinen and Dietmar Schomburg
    Citation: BMC Bioinformatics 2009 10:229
  14. Manual curation of experimental data from the biomedical literature is an expensive and time-consuming endeavor. Nevertheless, most biological knowledge bases still rely heavily on manual curation for data ext...

    Authors: Kimberly Van Auken, Joshua Jaffery, Juancarlos Chan, Hans-Michael Müller and Paul W Sternberg
    Citation: BMC Bioinformatics 2009 10:228
  15. Metabolomics experiments using Mass Spectrometry (MS) technology measure the mass to charge ratio (m/z) and intensity of ionised molecules in crude extracts of complex biological samples to generate high dimensio...

    Authors: John Draper, David P Enot, David Parker, Manfred Beckmann, Stuart Snowdon, Wanchang Lin and Hassan Zubair
    Citation: BMC Bioinformatics 2009 10:227
  16. High content live cell imaging experiments are able to track the cellular localisation of labelled proteins in multiple live cells over a time course. Experiments using high content live cell imaging will gene...

    Authors: Daniel Jameson, David A Turner, John Ankers, Stephnie Kennedy, Sheila Ryan, Neil Swainston, Tony Griffiths, David G Spiller, Stephen G Oliver, Michael RH White, Douglas B Kell and Norman W Paton
    Citation: BMC Bioinformatics 2009 10:226
  17. Receiver operating characteristic (ROC) curve is widely used to evaluate virtual screening (VS) studies. However, the method fails to address the "early recognition" problem specific to VS. Although many other...

    Authors: Wei Zhao, Kirk E Hevener, Stephen W White, Richard E Lee and James M Boyett
    Citation: BMC Bioinformatics 2009 10:225
  18. Regulatory motifs describe sets of related transcription factor binding sites (TFBSs) and can be represented as position frequency matrices (PFMs). De novo identification of TFBSs is a crucial problem in compu...

    Authors: Fernando Garcia, Francisco J Lopez, Carlos Cano and Armando Blanco
    Citation: BMC Bioinformatics 2009 10:224
  19. Since the function of a protein is largely dictated by its three dimensional configuration, determining a protein's structure is of fundamental importance to biology. Here we report on a novel approach to dete...

    Authors: James R Green, Michael J Korenberg and Mohammed O Aboul-Magd
    Citation: BMC Bioinformatics 2009 10:222
  20. The Ruby programming language has a lot to offer to any scientist with electronic data to process. Not only is the initial learning curve very shallow, but its reflection and meta-programming capabilities allo...

    Authors: Jan Aerts and Andy Law
    Citation: BMC Bioinformatics 2009 10:221
  21. Phylogenetic studies using expressed sequence tags (EST) are becoming a standard approach to answer evolutionary questions. Such studies are usually based on large sets of newly generated, unannotated, and err...

    Authors: Fabian Schreiber, Kerstin Pick, Dirk Erpenbeck, Gert Wörheide and Burkhard Morgenstern
    Citation: BMC Bioinformatics 2009 10:219
  22. Physical maps have been historically one of the cornerstones of genome sequencing and map-based cloning strategies. They also support marker assisted breeding and EST mapping. The problem of building a high qu...

    Authors: Serdar Bozdag, Timothy J Close and Stefano Lonardi
    Citation: BMC Bioinformatics 2009 10:217
  23. With the rapid development of high-throughput genotyping technologies, efficient methods for identifying linked regions using high-density SNP genotype data have become more and more important. Recently, a det...

    Authors: Lusheng Wang, Zhanyong Wang and Wanling Yang
    Citation: BMC Bioinformatics 2009 10:216
  24. The p53 protein is a master regulator that controls the transcription of many genes in various pathways in response to a variety of stress signals. The extent of this regulation depends in part on the binding ...

    Authors: Sivakumar Gowrisankar and Anil G Jegga
    Citation: BMC Bioinformatics 2009 10:215
  25. One essential step in the massive analysis of transcriptomic profiles is the calculation of the correlation coefficient, a value used to select pairs of genes with similar or inverse transcriptional profiles a...

    Authors: Jérôme Hennetin, Petri Pehkonen and Michel Bellis
    Citation: BMC Bioinformatics 2009 10:214
  26. Regularized regression methods such as principal component or partial least squares regression perform well in learning tasks on high dimensional spectral data, but cannot explicitly eliminate irrelevant featu...

    Authors: Bjoern H Menze, B Michael Kelm, Ralf Masuch, Uwe Himmelreich, Peter Bachert, Wolfgang Petrich and Fred A Hamprecht
    Citation: BMC Bioinformatics 2009 10:213
  27. Most analyses of microarray data are based on point estimates of expression levels and ignore the uncertainty of such estimates. By determining uncertainties from Affymetrix GeneChip data and propagating these...

    Authors: Richard D Pearson, Xuejun Liu, Guido Sanguinetti, Marta Milo, Neil D Lawrence and Magnus Rattray
    Citation: BMC Bioinformatics 2009 10:211
  28. The detection of true significant cases under multiple testing is becoming a fundamental issue when analyzing high-dimensional biological data. Unfortunately, known multitest adjustments reduce their statistic...

    Authors: Antonio Carvajal-Rodríguez, Jacobo de Uña-Alvarez and Emilio Rolán-Alvarez
    Citation: BMC Bioinformatics 2009 10:209
  29. DNA sequence binding motifs for several important transcription factors happen to be self-overlapping. Many of the current regulatory site identification methods do not explicitly take into account the overlap...

    Authors: Amar Drawid, Nupur Gupta, Vijayalakshmi H Nagaraj, Céline Gélinas and Anirvan M Sengupta
    Citation: BMC Bioinformatics 2009 10:208
  30. Progress in the life sciences cannot be made without integrating biomedical knowledge on numerous genes in order to help formulate hypotheses on the genetic mechanisms behind various biological phenomena, incl...

    Authors: Tsutomu Matsunaga, Chikara Yonemori, Etsuji Tomita and Masaaki Muramatsu
    Citation: BMC Bioinformatics 2009 10:205
  31. Genes that play an important role in tumorigenesis are expected to show association between DNA copy number and RNA expression. Optimal power to find such associations can only be achieved if analysing copy nu...

    Authors: Renée X Menezes, Marten Boetzer, Melle Sieswerda, Gert-Jan B van Ommen and Judith M Boer
    Citation: BMC Bioinformatics 2009 10:203
  32. Nuclear localization signals (NLSs) are stretches of residues within a protein that are important for the regulated nuclear import of the protein. Of the many import pathways that exist in yeast, the best char...

    Authors: Alex N Nguyen Ba, Anastassia Pogoutse, Nicholas Provart and Alan M Moses
    Citation: BMC Bioinformatics 2009 10:202
  33. Publicly available datasets of microarray gene expression signals represent an unprecedented opportunity for extracting genomic relevant information and validating biological hypotheses. However, the exploitat...

    Authors: Andrea Bisognin, Alessandro Coppe, Francesco Ferrari, Davide Risso, Chiara Romualdi, Silvio Bicciato and Stefania Bortoluzzi
    Citation: BMC Bioinformatics 2009 10:201
  34. One of the challenges in the analysis of microarray data is to integrate and compare the selected (e.g., differential) gene lists from multiple experiments for common or unique underlying biological themes. A ...

    Authors: Ming Yi, Uma Mudunuri, Anney Che and Robert M Stephens
    Citation: BMC Bioinformatics 2009 10:200
  35. A lot of high-throughput studies produce protein-protein interaction networks (PPINs) with many errors and missing information. Even for genome-wide approaches, there is often a low overlap between PPINs produ...

    Authors: Bill Andreopoulos, Christof Winter, Dirk Labudde and Michael Schroeder
    Citation: BMC Bioinformatics 2009 10:196

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