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BioInform s Patent Roundup: Recent Patents of Interest in Bioinformatics: Apr 19, 2004

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US Patent 6,721,754. System and method for database similarity join. Inventors: John Hurst, Scott Hutton. Assignee: Arena Pharmaceuticals.

Protects methods for information organization in which characteristics regarding entities are inferred from the characteristics of similar entities. This process is referred to as a “fuzzy similarity join,” and is particularly useful for chemical compound analysis in the area of pharmaceutical development, according to the inventors.


US Patent 6,721,663. Method for manipulating protein or DNA sequence data in order to generate complementary peptide ligands. Inventors: Gareth Roberts, Jonathan Heal. Assignee: Proteom.

Protects a method for computationallly analyzing and manipulating DNA and protein sequence data. The method searches sequence data to identify portions of sequences that code for intermolecular surfaces or regions of specific protein targets. A frame size is selected in terms of a number of sequence elements, and the procedure then compares pairs of frames, one from each sequence, to identify intramolecular and intermolecular regions on the basis of relationships according to a predetermined coding scheme.


US Patent 6,714,925. System for identifying patterns in biological data using a distributed network. Inventors: Stephen Barnhill, Isabelle Guyon, Jason Weston. Assignee: Barnhill Technologies (Now Health Discovery).

Protects a knowledge discovery system for biological data that uses a support vector machine in a distributed network environment. A customer transmits training biological data, test data, and live data to a vendor’s server from a remote source, via a distributed network, where it is held in a storage device. The training biological data is then pre-processed to transform and/or expand the biological data points. Live biological data is pre-processed and input into the trained and tested learning machine. The output is then post-processed into a computationally derived alphanumerical classifier for interpretation.


US Patent 6,714,874. Method and system for the assembly of a whole genome using a shotgun data set. Inventors: Gene Myers, Arthur Delcher, Ian Dew, Michael Flanigan, Saul Kravitz, Clark Mobarry, Knut Reinert, Karin Remington, Granger Sutton. Assignee: Applera.

Covers methods and systems for assembling a genome from a shotgun set of DNA fragments. The method is particularly useful in assembling genomes of 10 mebabases to 5 gigabases and which are made up of at least 5 percent repetitive DNA sequences (up to 25 percent repetitive), according to the inventors.


US Patent 6,713,257. Gene discovery using microarrays. Inventors: Daniel Shoemaker, Stewart Scherer, Steven Altschuler, Lani Wu, Christopher Armour. Assignee: Rosetta Inpharmatics.

Protects computer systems for identifying and characterizing genes using microarrays. In particular, the invention describes methods for detecting genes through the use of microarrays to analyze the expression state of the genome.


US Patent 6,708,141. Method for modeling cellular structure and function. Inventors: James Schaff, John Carson, Leslie Loew. Assignee: University of Connecticut.

Protects a method and apparatus for modeling cellular structure, which incorporates a theoretical hypothesis of cellular physiology into a simulation framework that allows direct comparison of simulation results with experimental data. The description of the biological model is kept independent of the solution via an integrated anatomical and physiological modeling language. This framework allows complex heterogeneous intracellular chemical simulations to be built with little or no knowledge of the underlying numerical methods, according to the inventors.


US Patent 6,708,120. Apparatus and method for automated protein design. Inventors: Stephen Mayo, Bassil Dahiyat, Benjamin Gordon, Arthur Street. Assignee: California Institute of Technology.

The invention relates to computer-related methods for quantitative protein design and optimization. The method comprises the steps of receiving a protein backbone structure with variable residue positions, establishing a group of potential rotamers for each of the variable residue positions, and analyzing the interaction of each of the rotamers with all or part of the remainder of the protein backbone structure to generate a set of optimized protein sequences. The methods further comprise classifying each variable residue position as either a core, surface, or boundary residue. The methods may further comprise generating a rank-ordered list of additional optimal sequences from the globally optimal protein sequence. Some or all of the protein sequences from the ordered list may be tested to produce potential energy test results.

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