Chen J, Hsu W, Lee ML, Ng SK. Increasing confidence of protein interactomes using network topological metrics. [Bioinformatics. 2006 Jun 20 (e-pub ahead of print)]: Introduces IRAP, a computational method for "repurification" of highly erroneous experimentally derived protein-protein interaction networks. The method involves an iterative process of removing interactions that are confidently identified as false positives and adding interactions detected as false negatives into the interactomes. Identification of both false positives and false negatives are performed using interaction confidence measures based on network topological metrics. Availability: http://www.comp.nus.edu.sg/~chenjin/fpfn.
Chen XW, Anantha G, Wang X. An effective structure learning method for constructing gene networks. [Bioinformatics 2006 22(11):1367-1374]: Discusses a structure learning method to reconstruct gene networks from observed gene expression data. The proposed method first constructs an undirected network based on mutual information between two nodes and then splits the structure into substructures. "The directional orientations for the edges that connect two nodes are then obtained by optimizing a scoring function for each substructure," according to the paper abstract. According to the authors, the method can identify networks that are close to the optimal structures and it outperforms hill-climbing methods in terms of both computation time and predicted structure accuracy.
Grigoryan G, Zhou F, Lustig SR, Ceder G, Morgan D, et al. Ultra-Fast Evaluation of Protein Energies Directly from Sequence. [PLoS Comput Biol 2(6): e63]: Presents a cluster expansion method for mapping sequences directly to their energies on a pre-specified rigid backbone that is applicable to computational protein design and protein structure prediction. The method can be extended to model "any computable or measurable protein property directly as a function of sequence," according to the abstract.
Li W, Young JS, Jingshan Z. Does Logarithm Transformation of Microarray Data Affect Ranking Order of Differentially Expressed Genes? [ArXiv preprint archive: http://arXiv.org/abs/q-bio/0606018]: Explores whether the common practice of logarithmic transformation of raw microarray data to make the distribution more symmetric and Gaussian-like affects the results of analysis. The authors find that the t-test is more likely to be affected by logarithmic transformation than logistic regression, and the regularized t-test is more affected than the t-test. The top-ranking genes (between 20 and 50, depending on the test) are not affected by the logarithmic transformation, however.
Machne R, Finney A, Muller S, Lu J, Widder S, Flamm C. The SBML ODE Solver Library: a native API for symbolic and fast numerical analysis of reaction networks. [Bioinformatics 2006 22(11):1406-1407]: Describes the SBML ODE Solver Library (SOSlib), a programming library for symbolic and numerical analysis of chemical reaction network models encoded in the Systems Biology Markup Language. Availability: www.tbi.univie.ac.at/~raim/odeSolver/.
Notebaart RA, van Enckevort FH, Francke C, Siezen RJ, Teusink B. Accelerating the reconstruction of genome-scale metabolic networks. [BMC Bioinformatics. 2006 Jun 13;7(1):296 (e-pub ahead of print)]: Introduces a method for accelerating the process of network reconstruction for a query species. The method "exploits the availability of well-curated metabolic networks and uses high-resolution predictions of gene equivalency between species, allowing the transfer of gene-reaction associations from curated networks," according to the abstract. In a comparison with the Pathologic network-reconstruction method, the authors predicted 186 additional genes to be associated to reactions in Lactococcus lactis IL1403.
Saltz J, Oster S, Hastings S, Langella S, Kurc T, Sanchez W, Kher M, Manisundaram A, Shanbhag K, Covitz P. caGrid: design and implementation of the core architecture of the cancer Biomedical Informatics Grid. [Bioinformatics. 2006 Jun 9 (e-pub ahead of print)]: Discusses a grid middleware infrastructure, called caGrid, that provides a standardized framework for the advertising, discovery, and invocation of data and analytical resources for cancer research. Availability: https://cabig.nci.nih.gov/workspaces/Architecture/caGrid/.
Tan CS, Ploner A, Quandt A, Lehtio J, Pawitan Y. Finding regions of significance in SELDI measurements for identifying protein biomarkers. [Bioinformatics 2006 22(12):1515-1523]: Presents an improved method for peak detection using surface-enhanced laser desorption and ionization technology for discovering biomarkers. Currently, scientists need to inspect individual spectra visually in order to verify spectral peaks identified by the current preprocessing method, according to the authors. The new approach, called RS for "regions of significance," reduces the data to a single spectrum of F-statistics capturing significant variability between spectra. Availability: http://www.meb.ki.se/~yudpaw.
Thompson JD, Muller A, Waterhouse A, Procter J, Barton GJ, Plewniak F, Poch O. MACSIMS : multiple alignment of complete sequences information management system. [BMC Bioinformatics 2006, 7:318]: Describes an information management system for protein families. The approach is based on multiple alignments of complete sequences (MACS) and combines "the advantages of knowledge-based and ab initio sequence analysis methods," according to the abstract. The method uses MACS to identify homologous regions and the retrieved data is evaluated and propagated from known to unknown sequences with these reliable regions. For a test set of 100 query proteins, the number of sequence features was increased by 70 percent, compared to the features available in public databases, according to the authors. Availability: http://bips.u-strasbg.fr/MACSIMS/.
Xing Y, Yu T, Wu YN, Roy M, Kim J, Lee C. An expectation-maximization algorithm for probabilistic reconstructions of full-length isoforms from splice graphs. [Nucleic Acids Res. 2006 Jun 6;34(10):3150-60]: Describes a probabilistic formulation for reconstructing full-length transcript isoforms from sequence fragments, and provides an expectation-maximization algorithm for its maximum likelihood solution.