Data processing: measuring – calibrating – or testing – Measurement system in a specific environment – Biological or biochemical
Reexamination Certificate
2006-11-07
2006-11-07
Allen, Marianne P. (Department: 1647)
Data processing: measuring, calibrating, or testing
Measurement system in a specific environment
Biological or biochemical
C702S019000
Reexamination Certificate
active
07133781
ABSTRACT:
Disclosed are methods, software, and systems for comparing biopolymer sequences. The model includes at least two different characterizations of states of matching between segments of sequences at defined positions. Examples of states of matching include: similarity and dissimilarity between objects, as well as similarity to a reference, e.g., a reference sequence or a sequence profile. A topology of particular match states can be used to identify classes of sequences, e.g., preprohormone sequences.
REFERENCES:
patent: 5568563 (1996-10-01), Tanaka et al.
patent: 5701256 (1997-12-01), Marr et al.
patent: 5873052 (1999-02-01), Sharaf
patent: 6128587 (2000-10-01), Sjolander
patent: 6314434 (2001-11-01), Shigemi et al.
patent: 6438496 (2002-08-01), Yoshida et al.
Baldi, P. et al., “Hidden Markov Models of Biological Primary Sequence Information”,Proc. Natl. Acad. Sci. USA, vol. 91, pp. 1059-1063; Feb. 1994.
Barrett, C. et al. “Scoring Hidden Markov Models”,CABIOS, vol. 13, No. 2, pp. 191-199; 1997.
Brakch, N. et al. “Favourable Side-Chain Orientation of Cleavage Site Dibasic Residues of Prohormone in Proteolytic Processing by Prohormone Convertase 1/3”,Eur. J. biochem.vol. 267, pp. 1626-1632; 2000.
Brown, M. et al., “Using Dirichlet Mixture Priors to Derive Hidden Markov Models for Protein Families”,Proc. of First Int. Conf. on Intelligent Systems for Molecular Biology, pp. 47-55, Menlo Park, CA, Jul. 1993. AAAI/MIT Press.
Chesneau, V. et al., “N-Arginine Diabasic Convertase (NRD Convertase): A Newcomer to the Family of Processing Endopeptidases”,Biochimicvol. 76, pp. 234-240; Paris, Mar. 1994.
Chou, K-C. et al., “Studies on the Specificity of HIV Protease: An Application of Markov Chain Theory”,Journal of Protein Chemistry, vol. 12, No. 6, pp. 709-724; 1993.
Chou, K-C., “Prediction of Human Immunodeficiency Virus Protease Cleavage Sites in Protein”,Analytical Biochemistryvol. 233, pp. 1-14; 1996.
Chou, K-C. et al., “Predicting Human Immunodeficiency Virus Protease Cleavage Sites in Proteins by a Discriminant Function Method”,Proteins:Structure, Function, and Geneticsvol. 24, pp. 51-72; 1996.
Grice, JA. Et al., “Reduced Space Sequence Alignment”,CABIOS, vol. 13, pp. 45-53, 1997.
Hughey, R. et al., “Hidden Markov Models for Sequence Analysis: Extension and Analysis of the Basic Method”, ReprintCABIOSvol. 12, pp. 95-107, 1996.
Hughey, R. et al., “SAM : Sequence Alignment and Modeling software System”,Technical Report UCSC-CRL-96-22, University of California, Santa Cruz, CA, Jul. 1998.
Hughey, R., “Massively Parallel Biosequence Analysis.”,Technical Report UCSC-CRL-93-14, University of California, Santa Cruz, CA, Apr. 1993.
Jagla, B. et al., “Adaptive Encoding Neural Networks for the Recognition of Human Signal Peptide Cleavage Sites”BIO, vol. 16, No. 3, Mar. 2000.
Karchin, R., “Hidden Markov Models and Protein Sequence Analysis” from http://www.cse.ucsc.edu/research/compbio/ismb99.handouts//KK185FP.html printed from webiste Mar. 14, 2002.
Karplus, K. et al., “Hidden Markov Models for Detecting Remote Protein Homologies”,BIO Informatics. vol. 14, No. 10, pp. 846-856; Oct. 1998.
Karplus, K. et al., “Predicting Protein Structure Using Hidden Markov Models”,Proteins:Structure, Function, and Genetic, Suppl., pp. 134-139; Sep. 1997.
Krogh, A. et al., “Hidden Markov Models in Computational Biology. Applications to Protein Modeling”,J. Mol. Biol.vol. 235, pp. 1501-1531; Feb. 1994.
Krogh, A. et al., Predicting Transmembrane Protein Topology with a Hidden Markov Model: Application to Complete GenomesJournal of Molecular Biologyvol. 305, No. 3, pp. 567-580; 2001.
Ladunga, I., “Large-Scale Predictions of Secretory Proteins from Mammalian genomic and EST sequences”Analytical Biotechnology, pp. 13-18; 2000.
Lockless, SW. et al. “Evolutionarily Conserved Pathways of Energetic Connectivity in Protein Families”,Sciencevol. 286, pp. 295-299; Oct. 1999.
Nielsen, H.et al., “Identification of Prokaryotic and Eukaryotic Signal Peptides and Prediction of Cleavage Sites”,Protein Engineeringvol. 10, No. 1, pp. 1-6; Jan. 1997.
Hielsen, H. et al. “Prediction of Signal Peptides and Signal Anchors by a Hidden Markov Model”,American Association for Artificial Intelligence ISMB, pp. 122-130; 1998.
Nielsen, H. et al. “Machine Learning Approaches for the Prediction of Signal Peptides and Other Protein Sorting Signals”,Protein Engineeringvol. 12, No. 1, pp. 3-9; Jan. 1999.
Paracel, “Hidden Markov Model”, from http://paracel.com/publications/hmm—white—paper.html printed from website Mar. 14, 2002.
Rabiner, LR., “A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition”,Proceedings of the IEEE, vol. 77, No. 2, pp. 257-186; Feb . 1989.
Rholam, M. et al., “Role of Amino Acid Sequences Flanking Dibasic Cleavage Sites in Precursor Proteolytic Processing. The Importance of the First Residue C-terminal of the cleavage site”,Eur. J. Biochem. vol. 277, pp. 707-714; Feb. 1995.
UCSC Comp. Biol. Group, “Sequence Alignment and Modeling System” from http://www.cse.ucsc.edu/research/compbio/sam.html printed from website Mar. 14, 2002.
Bucher, P. et al., “A Flexible Motif Search Technique based on Generalized Profiles,” Computers and Chemistry, vol. 20, pp. 3-24, Jan. 1996.
Eddy, S.R., “Hidden Markov Models,” Current Opinion in Structural Biology, vol. 6, pp. 361-365, 1996.
Eddy, S., “Profile Hidden Markov Models,” Bioinformatics Review, 14(9), pg. 755-763, 1998.
Eddy, et al., “Maximum Discrimination Hidden Markov Models of Sequence Consensus,” J. Computational Biology, 2(1), pp. 9-23, 1995.
Eddy, S.R., “Multiple Alignment Using Hidden Markov Models,” Proc. Third In. Conf. Intelligent Systems for Molecular Biology, AAAI Press, Menlo Park, pp. 114-120, PostScript; 1995.
Grate, et al., “Tutorial Stechastis Modeling Techniques: Understanding and Using Hidden Markov Models,” University of California, Santa Cruz, CA pp. 1-34, Jun. 1996; www.cse.ucsc.edu/research/compbio/sam.html; Tutorial allegedly used at ISMB, Jun. 1996.
Grundy et al., “Meta-MEME. Motif-Based Hidden Markov Models of Protein Familities,” Computer Applications.
Karchin et al., “Weighting Hidden Markov Models for Maximum Discrimination,” Bioinformatics, 14(9), pp. 772-782, 1998.
McClure, et al., “Parameterization studies from the SAM and HMMER methods of hidden Markov Model,” Proc. Fourth Int. Conf. Intelligent Systems to Molecular Biology 155-164 UNLV, Las Vegas, 1996.
Tarnas, et al., “Reduced space hidden Markov model training,” Bioinformatics, 14(5), 401 406, 1998.
Karp Peter D.
Lincoln Patrick Denis
Sonmez Kemal
Toll Lawrence R.
Allen Marianne P.
Patterson & Sheridan LLP
SRI - International
Tong, Esq. Kin-Wah
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