74 results about "Genome database" patented technology

The invention relates to information security technology, in particular to a virtual genome-based cryptosystem (VGC). The cryptosystem is provided with two matched keys, of which one is a virtual genome database (VGDB) consisting of random deoxyribonucleic acid (DNA) sequences and the other one is a position table that virtual genes of the VGDB are randomly distributed in a two-dimensional microarray, namely a virtual DNA microarray chip (VDMC). Any plaintext information can be freely written on the VDMC, namely points for forming the plaintext information are selected from the VDMC microarray. The selected points correspond to the virtual genes in the VGDB; small segments of DNA sequences are randomly selected from the virtual genes; and the uniqueness of the small segments of DNA sequences in the VGDB is determined by using a common tool of the bioinformatics, namely a basic local alignment search tool (BLAST), or other character string search algorithms such as a Knuth-Morris-Pratt (KMP) algorithm and the like. A cipher text is combined by the small segments of DNA sequences. The small segments of DNA sequences need only to perform BLAST on the VGDB during decryption, namely the points for forming the plaintext information can be discovered, and the plaintext information can be restored according to the VDMC. Any non-VGDB sequence can be randomly inserted into the cipher text and does not have any influence on the encryption. Thus, the VGC is an excellent information hiding system. In addition, the VGC key can be updated automatically so as to realize an indecipherable one-time-pad system. The cryptosystem is used for real-time quick secret information communication, digital signature and identity authentication.

A system and method for evolving a deep neural network structure that solves a provided problem includes: a memory storing a candidate supermodule genome database having a pool of candidate supermodules having values for hyperparameters for identifying a plurality of neural network modules in the candidate supermodule and further storing fixed multitask neural networks; a training module that assembles and trains N enhanced fixed multitask neural networks and trains each enhanced fixed multitask neural network using training data; an evaluation module that evaluates a performance of each enhanced fixed multitask neural network using validation data; a competition module that discards supermodules in accordance with assigned fitness values and saves others in an elitist pool; an evolution module that evolves the supermodules in the elitist pool; and a solution harvesting module providing for deployment of a selected one of the enhanced fixed multitask neural networks, instantiated with supermodules selected from the elitist pool.

A method for selectively obtaining a natural variant of an enzyme having activity includes (1) a step of detecting an ORF sequence of a protein having enzyme activity from a genome database including base sequences of metagenomic DNA of environmental microbiota; (2) a step of obtaining at least one PCR clone including the ORF sequence having a full length, a partial sequence of the ORF sequence, or a base sequence encoding an amino acid sequence which is formed by deletion, substitution, or addition of at least one amino acid residue in an amino acid sequence encoded by the ORF sequence, by performing PCR cloning on at least one metagenomic DNA of the environmental microbiota by using a primer designed based on the ORF sequence; (3) a step of determining a base sequence and an amino acid sequence which is encoded by the base sequence for each PCR clone obtained in the step (2); and (4) a step of selecting a natural variant of an enzyme having activity by measuring enzyme activity of proteins encoded by each PCR clone obtained in the step (2).