- Using multiple alignments to improve gene predictionSamuel S Gross
Department of Computer Science and Engineering, Washington University, St Louis, MO 63130, USA
J Comput Biol 13:379-93. 2006..Analyses of the predictions reveal that N-SCAN's accuracy in both human and fly exceeds that of all previously published whole-genome de novo gene predictors...
- CONTRAST: a discriminative, phylogeny-free approach to multiple informant de novo gene predictionSamuel S Gross
Computer Science Department, Stanford University, Stanford, CA, USA
Genome Biol 8:R269. 2007..CONTRAST predicts exact coding region structures for 65% more human genes than the previous state-of-the-art method, misses 46% fewer exons and displays comparable gains in specificity...
- Begin at the beginning: predicting genes with 5' UTRsRandall H Brown
Laboratory for Computational Genomics, Washington University, St Louis, MO 63130, USA
Genome Res 15:742-7. 2005....
- Automated cellular annotation for high-resolution images of adult Caenorhabditis elegansSarah J Aerni
Department of Computer Science, Stanford University, 353 Serra Mall, Stanford, CA 94305, USA
Bioinformatics 29:i18-26. 2013..However, annotating individual cells in images of adult C.elegans typically requires expertise and significant manual effort. Automation of this task is therefore critical to enabling high-resolution studies of a large number of genes...
- Feasibility of diffusion-NMR surface-to-volume measurements tested by calculations and computer simulationsMark S Conradi
Department of Physics, Washington University, St Louis, MO 63130 4899, USA
J Magn Reson 169:196-202. 2004..Emphasis is placed on the useful range of times t for which NMR determinations of lung S/V are feasible...