DNA-based analog neural networks

Details DNA-based analog neural networks DNA-based analog neural networks

2007-11-20

2007-11-20

Clow, Lori A. (Department: 1631)

Chemistry: molecular biology and microbiology

Measuring or testing process involving enzymes or...

Involving nucleic acid

C435S091100, C435S091200

Reexamination Certificate

active

09741179

ABSTRACT:
This invention is an oligomer-based analog neural network (ANN) comprising weight and saturation oligomers, the concentrations of which are selected such that activation of the ANN by a set of input oligomers generates a set of output oligomers, the sequences and relative concentrations of which are dependent on the sequences and relative concentrations of the input oligomers. The invention further includes methods for using such an ANN for solving any problems amenable to solution by a trained neural network. A preferred embodiment of the claimed invention is a DNA-based ANN that accepts cDNA molecules as inputs and analyzes the gene expression profile of the cells from which the cDNA is derived. The DNA-based ANN is typically trained with a computer to identify the weights giving accurate mapping of the inputs to the outputs; and the concentrations of weight oligomers of the DNA-based ANN are then selected accordingly.

REFERENCES:
patent: 4716106 (1987-12-01), Chiswell
patent: 5648211 (1997-07-01), Fraiser et al.
patent: 5862304 (1999-01-01), Ravdin et al.
patent: 5933819 (1999-08-01), Skolnick et al.
patent: 5983211 (1999-11-01), Heseltine et al.
patent: 6060240 (2000-05-01), Kamb et al.
patent: 6613508 (2003-09-01), Ness et al.
“DNA Analog Vector Algebra and Physical Constraints on Large-Scale DNA-Based Neural Network Computation,” A.P. Mills, Jr., et al., DIMACS Series in Discrete Mathematics and Theoretical Computer Science, 54, pp. 65-73, 2000.
“Prospects For Large-Scale Neural Network Computation Using DNA Molecules,” International Symposium on Cluster and Nanostructure Interfaces, Richmond, VA, Oct. 25-28, 1999, A.P. Mills, Jr., et al., Bell Labs, Lucent Technologies, 600 Mountain Avenue, Murray Hill, NJ 07974, pp. 685-691, 2000.
“Experimental aspects of DNA neural network computation,” A.P. Mills, Jr., et al., Soft Computing, 5(2001), pp. 10-18, 2001.
“Article for analog vector algebra computation,” Allen P. Mills, Jr., et al., BioSystems, 52, pp. 175-180, 1999.
D. Duggan et al., “Expression profiling using cDNA microarrays,” Nature Genetics Supplement 21:10-14, 1999.
B. Wilamowski “Neural Networks and Fuzzy Systems,” in The Electronics Handbook, edited by J. Whitaker, CRC Press, Inc., Boca Raton, Florida, 1996, pp. 1893-1914.
I. Kovesdi et al., “Applications of neural networks in structure-activity relationships,” Med. Res. Rev. 19(3):249-269, 1999.
M. Honeyman et al., “Neural network-based prediction of candidate T-cell epitopes,” Nature Biotechnology 16:966-969, 1998.
N. Saccone et al., “Mapping genotype to phenotype for linkage analysis,” Genetic Epidemiol. 17 Suppl.:S708, 1999.
Y. Shi et al., “Computational EST database analysis identifies a novel member of the neuropoietic cytokine family,” Biophys. Res. Commun. 262(1):132-138, 1999.
T. Gress et al., “A pancreatic cancer-specific expression profile,” Oncogene 13(8):1819-1830, 1996.
L. Whitney et al., “Analysis of gene expression in multiple sclerosis using cDNA microarrays,” Ann. Neurol. 46(3):425-428, 1999.
J. DeRisi et al., “Use of a cDNA mocroarray to analyze gene expression patterns in human cancer,” Nature Genetics 14:457-460, 1996.
“Neural Networks in Clinical Medicine,” Will Penny, Ph.D., et al., Med. Decis Making 16, pp. 386-398, 1996.
“Neural networks and physical systems with emergent collective computational abilities,” J.J. Hopfield, Proc. Natl. Acad. Sci. USA 79: 2554-2558, Apr. 1982.
“Multilayer Feedforward Networks are Universal Approximators,” Kurt Homik, et al., Neural Networks 2: 359-366, 1989.
“Analog Electronic Neural Networks For Pattern Recognition Applications,” Hans P. Graf, et al., Neural Networks: Concepts, Applications and Implementations I, edited by V. Milutinovic, et al., Prentice Hall, pp. 155-179, 1991.
“Learning Internal Representations by Error Propagation,” D.E. Rumelhart, et al., Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Ch. 8, edited by D.E. Rumelhart, et al., MIT Press, Cambridge, MA, pp. 318-362 1986.
“Expression monitoring by hybridization to high-density oligonucleotide arrays,” David J. Lockhart, et al., Nature Biotechnology 14: 1675-1680, Dec. 1996.
“DNA chips: State-of-the art,” Graham Ramsay, Nature Biotechnology 16: 40-44, Jan. 1998.
“Reliability and Efficiency of a DNA-Based Computation,” R. Deaton, et al., Physical Review Letters 80(2): 417-420, 1998.
“Multicolor molecular beasons for allele discrimination,” Sanjay Tyagi, et al., Nature Biotechnology 16: 49-53, 1998.
“Screening unlabeled DNA targets with randomly ordered fiber-optic gene arrays,” Frank J. Steemers, et al., Nature Biotechnology 18: 91-94, 2000.
“Sensitive Fluorescence-Based Thermodynamic and Kinetic Measurements of DNA Hybridization in Solution,” Larry E. Morrison, et al., Biochemistry 32: 3095-3104, 1993.
“DNA Probes: Applications of the Principles of Nucleic Acid Hybridization,” James G. Wetmur, Critical Reviews in Biochemistry and Molecular Biology, 26(3/4): 227-259, 1991.
“Denaturation and Renaturation of Deoxyribonucleic Acid,” J. Marmur, et al., Progress in Nucleic Acid Research, 1: 231-300, 1963.
“Complex Traits on the Map,” J. Ott, et al., Recent Results in Cancer Research, Springer-Verlag Berlin, 154: 285-291, 1998.
“High-fidelity mRNA amplification for gene profiling,” Ena Wang, et al., Nature Biotechnology 18: 457-459, 2000.
“Interpreting patterns of gene expression with self-organizing maps: Methods and application to hematopoietic differentiation,” Pablo Tamayo, et al., Proc. Natl. Acad. Sci. USA 96: 2907-2912, 1999.
“Analysis of gene expression data using self-organizing maps,”Petri Toronen, et al., FEBS Letters 451: 142-146, 1999.
“Exploring the Metabolic and Genetic Control of Gene Expression on a Genomic Scale,” Joseph L. DeRisi, et al., Science 278: 680-686, 1997.
“Distinctive gene expression patterns in human mammary epithelial cells and breast cancers,” Charles M. Perou, et al., Proc. Natl. Acad. Sci. USA 96: 9212-9217, 1999.
Expression profiling in cancer using cDNA microarrays, Javed Khan, et al., Electrophoresis 20: 223-229, 1999.

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