Structural profiles of human miRNA families from pairwise clustering (2009)
Kaczkowski , B., Torarinsson, E., Reiche, K., Havgaard, J.H., Stadler, P.F., Gorodkin, J.
MicroRNAs (miRNAs) are a group of small, 21 nt long, riboreg-ulators inhibiting gene expression at a post-transcriptional level. Their most distinctive structural feature is the foldback hairpin of...
Sequence assembly. Review (2009)
Scheibye-Alsing, K., Hoffmann, S., Frankel, A., Jensen, P., Stadler, P.F., Mang, Y., ...
Despite the rapidly increasing number of sequenced and re-sequenced genomes, many issues regarding the computational assembly of large-scale sequencing data have remain unresolved. Computational...
Comparative genomics beyond sequence based alignments: RNA structures in the ENCODE regions (2008)
E. Torarinsson, Z. Yao, E. D. Wiklund, J. B. Bramsen, C. Hansen, J. Kjems, ...
1 Recent computational scans for noncoding RNAs (ncRNAs) in multiple organisms have relied on existing multiple sequence alignments. However, as sequence similarity drops, a key signal of RNA...
J. Gorodkin, L. K. Hansen, B. Lautrup, S. A. Solla
A better understanding of pruning methods based on a ranking of weights according to their saliency in a trained network requires further information on the statistical properties of such saliencies....
Universal Distribution of Saliencies for Pruning in Layered Neural Networks (2007)
J. Gorodkin, Lars Kai Hansen, L. K. Hansen, Benny Lautrup, S. A. Solla
A better understanding of pruning methods based on a ranking of weights according to their saliency in a trained network requires further information on the statistical properties of such saliencies....
Semi-automated update and cleanup of structural RNA alignment databases (2001)
Gorodkin, J., Zwieb, C., Knudsen, B.
Summary: We have developed a series of programs which assist in maintenance of structural RNA databases. A main program BLASTs the RNA database against GenBank and automatically extends and realigns...
O. Lund, K. Frim, J. Gorodkin, H. Bohr, J. Bohr, J. Hansen, ...
3To whom correspondence should be addressed We predict interatomic Cα distances by two independent data driven methods. The first method uses statistically derived probability distributions of the...
Displaying the in formation contents of structural RNA alignments: the structure logos (1997)
Gorodkin, J., Heyer, L.J., Brunak, S., Storomo, G.D.
Motivation: We extend the standard ‘Sequence Logo’method of Schneider and Stevens (Nucleic Acids Res., 18, 6097–6100, 1990) to incorporate prior frequencies on the bases, allow ft,r gaps in the...
Protein distance constraints predicted by neural networks and probability density functions (1997)
Lund, O, Frimand, K, Gorodkin, J, Bohr, H, Bohr, J, Hansen, J, ...
Neural networks and cellular automata complexity (1993)
J. Gorodkin, A. Srensen, O. Winther
Abstract. The genotype-phenotype relation for the 256 elementary cellular automata is studied using neural networks. Neural network are trained to learn the mapping from each genotype rule to its...
Finding the most significant common sequence and structure motifs in a set of RNA sequences.
Gorodkin, J, Heyer, L J, Stormo, G D
We present a computational scheme to locally align a collection of RNA sequences using sequence and structure constraints. In addition, the method searches for the resulting alignments with the most...
Finding the most significant common sequence and structure motifs in a set of RNA sequences.
Gorodkin, J, Heyer, L J, Stormo, G D
We present a computational scheme to locally align a collection of RNA sequences using sequence and structure constraints. In addition, the method searches for the resulting alignments with the most...