@article {38421, title = {The partitioned LASSO-patternsearch algorithm with application to gene expression data}, journal = {BMC bioinformaticsBMC Bioinformatics}, volume = {13}, year = {2012}, note = {http://www.ncbi.nlm.nih.gov/pubmed/22587526?dopt=Abstract}, type = {10.1186/1471-2105-13-98}, abstract = {BACKGROUND: In systems biology, the task of reverse engineering gene pathways from data has been limited not just by the curse of dimensionality (the interaction space is huge) but also by systematic error in the data. The gene expression barcode reduces spurious association driven by batch effects and probe effects. The binary nature of the resulting expression calls lends itself perfectly to modern regularization approaches that thrive in high-dimensional settings. RESULTS: The Partitioned LASSO-Patternsearch algorithm is proposed to identify patterns of multiple dichotomous risk factors for outcomes of interest in genomic studies. A partitioning scheme is used to identify promising patterns by solving many LASSO-Patternsearch subproblems in parallel. All variables that survive this stage proceed to an aggregation stage where the most significant patterns are identified by solving a reduced LASSO-Patternsearch problem in just these variables. This approach was applied to genetic data sets with expression levels dichotomized by gene expression bar code. Most of the genes and second-order interactions thus selected and are known to be related to the outcomes. CONCLUSIONS: We demonstrate with simulations and data analyses that the proposed method not only selects variables and patterns more accurately, but also provides smaller models with better prediction accuracy, in comparison to several alternative methodologies.}, keywords = {algorithms, Breast Neoplasms, Computer simulation, Female, Gene expression, Gene Expression Profiling, Genomics, HUMANS, Models, Genetic}, author = {Shi, Weiliang and Wahba, Grace and Irizarry, Rafael A. and H{\'e}ctor Corrada Bravo and Wright, Stephen J.} } @article {49745, title = {A computational statistics approach for estimating the spatial range of morphogen gradients.}, journal = {Development}, volume = {138}, year = {2011}, month = {2011 Nov}, pages = {4867-74}, abstract = {

A crucial issue in studies of morphogen gradients relates to their range: the distance over which they can act as direct regulators of cell signaling, gene expression and cell differentiation. To address this, we present a straightforward statistical framework that can be used in multiple developmental systems. We illustrate the developed approach by providing a point estimate and confidence interval for the spatial range of the graded distribution of nuclear Dorsal, a transcription factor that controls the dorsoventral pattern of the Drosophila embryo.

}, keywords = {Animals, Biostatistics, Cleavage Stage, Ovum, Computational Biology, Computer simulation, Drosophila, Drosophila Proteins, Embryo, Nonmammalian, Gene Expression Regulation, Developmental, Genes, Developmental, Imaging, Three-Dimensional, In Situ Hybridization, Fluorescence, Morphogenesis, Osmolar Concentration, Tissue Distribution}, issn = {1477-9129}, doi = {10.1242/dev.071571}, author = {Kanodia, Jitendra S and Kim, Yoosik and Tomer, Raju and Khan, Zia and Chung, Kwanghun and Storey, John D and Lu, Hang and Keller, Philipp J and Shvartsman, Stanislav Y} } @article {49727, title = {Haem oxygenase is synthetically lethal with the tumour suppressor fumarate hydratase.}, journal = {Nature}, volume = {477}, year = {2011}, month = {2011 Sep 8}, pages = {225-8}, abstract = {

Fumarate hydratase (FH) is an enzyme of the tricarboxylic acid cycle (TCA cycle) that catalyses the hydration of fumarate into malate. Germline mutations of FH are responsible for hereditary leiomyomatosis and renal-cell cancer (HLRCC). It has previously been demonstrated that the absence of FH leads to the accumulation of fumarate, which activates hypoxia-inducible factors (HIFs) at normal oxygen tensions. However, so far no mechanism that explains the ability of cells to survive without a functional TCA cycle has been provided. Here we use newly characterized genetically modified kidney mouse cells in which Fh1 has been deleted, and apply a newly developed computer model of the metabolism of these cells to predict and experimentally validate a linear metabolic pathway beginning with glutamine uptake and ending with bilirubin excretion from Fh1-deficient cells. This pathway, which involves the biosynthesis and degradation of haem, enables Fh1-deficient cells to use the accumulated TCA cycle metabolites and permits partial mitochondrial NADH production. We predicted and confirmed that targeting this pathway would render Fh1-deficient cells non-viable, while sparing wild-type Fh1-containing cells. This work goes beyond identifying a metabolic pathway that is induced in Fh1-deficient cells to demonstrate that inhibition of haem oxygenation is synthetically lethal when combined with Fh1 deficiency, providing a new potential target for treating HLRCC patients.

}, keywords = {Animals, Bilirubin, Cell Line, Cells, Cultured, Citric Acid Cycle, Computer simulation, Fumarate Hydratase, Fumarates, Genes, Lethal, Genes, Tumor Suppressor, Glutamine, Heme, Heme Oxygenase (Decyclizing), Kidney Neoplasms, Leiomyomatosis, Mice, Mitochondria, Mutation, NAD, Neoplastic Syndromes, Hereditary, Skin Neoplasms, Uterine Neoplasms}, issn = {1476-4687}, doi = {10.1038/nature10363}, author = {Frezza, Christian and Zheng, Liang and Folger, Ori and Rajagopalan, Kartik N and MacKenzie, Elaine D and Jerby, Livnat and Micaroni, Massimo and Chaneton, Barbara and Adam, Julie and Hedley, Ann and Kalna, Gabriela and Tomlinson, Ian P M and Pollard, Patrick J and Watson, Dave G and Deberardinis, Ralph J and Shlomi, Tomer and Ruppin, Eytan and Gottlieb, Eyal} } @article {49679, title = {SplicePort--an interactive splice-site analysis tool.}, journal = {Nucleic Acids Res}, volume = {35}, year = {2007}, month = {2007 Jul}, pages = {W285-91}, abstract = {

SplicePort is a web-based tool for splice-site analysis that allows the user to make splice-site predictions for submitted sequences. In addition, the user can also browse the rich catalog of features that underlies these predictions, and which we have found capable of providing high classification accuracy on human splice sites. Feature selection is optimized for human splice sites, but the selected features are likely to be predictive for other mammals as well. With our interactive feature browsing and visualization tool, the user can view and explore subsets of features used in splice-site prediction (either the features that account for the classification of a specific input sequence or the complete collection of features). Selected feature sets can be searched, ranked or displayed easily. The user can group features into clusters and frequency plot WebLogos can be generated for each cluster. The user can browse the identified clusters and their contributing elements, looking for new interesting signals, or can validate previously observed signals. The SplicePort web server can be accessed at http://www.cs.umd.edu/projects/SplicePort and http://www.spliceport.org.

}, keywords = {Base Sequence, Chromosome mapping, Computational Biology, Computer simulation, DNA, Genome, HUMANS, Internet, Models, Genetic, Molecular Sequence Data, Pattern Recognition, Automated, RNA Splice Sites, sequence alignment, Sequence Analysis, DNA, User-Computer Interface}, issn = {1362-4962}, doi = {10.1093/nar/gkm407}, author = {Dogan, Rezarta Islamaj and Getoor, Lise and Wilbur, W John and Mount, Stephen M} } @article {49562, title = {MCMC-based particle filtering for tracking a variable number of interacting targets.}, volume = {27}, year = {2005}, month = {2005 Nov}, pages = {1805-19}, abstract = {

We describe a particle filter that effectively deals with interacting targets--targets that are influenced by the proximity and/or behavior of other targets. The particle filter includes a Markov random field (MRF) motion prior that helps maintain the identity of targets throughout an interaction, significantly reducing tracker failures. We show that this MRF prior can be easily implemented by including an additional interaction factor in the importance weights of the particle filter. However, the computational requirements of the resulting multitarget filter render it unusable for large numbers of targets. Consequently, we replace the traditional importance sampling step in the particle filter with a novel Markov chain Monte Carlo (MCMC) sampling step to obtain a more efficient MCMC-based multitarget filter. We also show how to extend this MCMC-based filter to address a variable number of interacting targets. Finally, we present both qualitative and quantitative experimental results, demonstrating that the resulting particle filters deal efficiently and effectively with complicated target interactions.

}, keywords = {algorithms, Animals, Artificial Intelligence, Computer simulation, HUMANS, Image Enhancement, Image Interpretation, Computer-Assisted, Information Storage and Retrieval, Markov chains, Models, Biological, Models, Statistical, Monte Carlo Method, Motion, Movement, Pattern Recognition, Automated, Subtraction Technique, Video Recording}, issn = {0162-8828}, doi = {10.1109/TPAMI.2005.223}, author = {Khan, Zia and Balch, Tucker and Dellaert, Frank} }