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1.  PathMiner: predicting metabolic pathways by heuristic search 
Bioinformatics (Oxford, England)  2003;19(13):1692-1698.
Automated methods for biochemical pathway inference are becoming increasingly important for understanding biological processes in living and synthetic systems. With the availability of data on complete genomes and increasing information about enzyme-catalyzed biochemistry it is becoming feasible to approach this problem computationally. In this paper we present PathMiner, a system for automatic metabolic pathway inference. PathMiner predicts metabolic routes by reasoning over transformations using chemical and biological information.
We build a biochemical state-space using data from known enzyme-catalyzed transformations in Ligand, including, 2917 unique transformations between 3890 different compounds. To predict metabolic pathways we explore this state-space by developing an informed search algorithm. For this purpose we develop a chemically motivated heuristic to guide the search. Since the algorithm does not depend on predefined pathways, it can efficiently identify plausible routes using known biochemical transformations.
The system is available for testing at
PMCID: PMC2709535  PMID: 12967966
2.  A literature-based method for assessing the functional coherence of a gene group 
Bioinformatics (Oxford, England)  2003;19(3):396-401.
Many experimental and algorithmic approaches in biology generate groups of genes that need to be examined for related functional properties. For example, gene expression profiles are frequently organized into clusters of genes that may share functional properties. We evaluate a method, neighbor divergence per gene (NDPG), that uses scientific literature to assess whether a group of genes are functionally related. The method requires only a corpus of documents and an index connecting the documents to genes.
We evaluate NDPG on 2796 functional groups generated by the Gene Ontology consortium in four organisms: mouse, fly, worm and yeast. NDPG finds functional coherence in 96, 92, 82 and 45% of the groups (at 99.9% specificity) in yeast, mouse, fly and worm respectively.
PMCID: PMC2669934  PMID: 12584126
3.  FindGDPs: fast identification of primers for labeling microbial transcriptomes for DNA microarray analysis 
Bioinformatics (Oxford, England)  2003;19(13):1718-1719.
FindGDPs is a program that uses a greedy algorithm to quickly identify a set of genome-directed primers that specifically anneal to all of the open reading frames in a genome and that do not exhibit full-length complementarity to the members of another user-supplied set of nucleotide sequences.
The program code is distributed under the GNU General Public License at
PMCID: PMC2390821  PMID: 15593406

Results 1-3 (3)