A DNA-based memory with in vitro learning and associative recall View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2005-06

AUTHORS

Junghuei Chen, Russell Deaton, Yu-Zhen Wang

ABSTRACT

A DNA-based memory was implemented with in vitro learning and associative recall.The learning protocol stored the sequences to which it was exposed, and memories were recalled by sequence content through DNA-to-DNA template annealing reactions. Experiments demonstrated that biological DNA could be learned, that sequences similar to the training DNA were recalled correctly, and that unlike sequences were differentiated. Theoretically, the memory has a pattern separation capability that is very large, and can learn long DNA sequences. The learning and recall protocols are massively parallel, as well as simple, inexpensive, and quick. The memory has several potential applications in detection and classification of biological sequences, as well as a massive storage capacity for non-biological data. More... »

PAGES

83-101

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11047-004-4002-3

DOI

http://dx.doi.org/10.1007/s11047-004-4002-3

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1024042379


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0604", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Genetics", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/06", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Biological Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "University of Delaware", 
          "id": "https://www.grid.ac/institutes/grid.33489.35", 
          "name": [
            "Chemistry and Biochemistry, The University of Delaware, 19716, Newark, DE, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Chen", 
        "givenName": "Junghuei", 
        "id": "sg:person.013246427324.08", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013246427324.08"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Arkansas at Fayetteville", 
          "id": "https://www.grid.ac/institutes/grid.411017.2", 
          "name": [
            "Computer Science and Engineering, The University of Arkansas, 72701, Fayetteville, AR, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Deaton", 
        "givenName": "Russell", 
        "id": "sg:person.0707334604.60", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0707334604.60"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Delaware", 
          "id": "https://www.grid.ac/institutes/grid.33489.35", 
          "name": [
            "Chemistry and Biochemistry, The University of Delaware, 19716, Newark, DE, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Wang", 
        "givenName": "Yu-Zhen", 
        "id": "sg:person.014756500465.66", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014756500465.66"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1126/science.1069528", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006258875"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1073/pnas.95.4.1460", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009187647"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s1381-141x(97)80015-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010710951"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s1381-141x(97)80015-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010710951"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/28998", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015431161", 
          "https://doi.org/10.1038/28998"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/28998", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015431161", 
          "https://doi.org/10.1038/28998"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/3-540-44992-2_15", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017930394", 
          "https://doi.org/10.1007/3-540-44992-2_15"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/3-540-44992-2_15", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017930394", 
          "https://doi.org/10.1007/3-540-44992-2_15"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/3-540-36440-4_22", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022065278", 
          "https://doi.org/10.1007/3-540-36440-4_22"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/3-540-36440-4_22", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022065278", 
          "https://doi.org/10.1007/3-540-36440-4_22"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/3-540-48017-x_22", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023263443", 
          "https://doi.org/10.1007/3-540-48017-x_22"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf01192694", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024191614", 
          "https://doi.org/10.1007/bf01192694"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf01192694", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024191614", 
          "https://doi.org/10.1007/bf01192694"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1073/pnas.97.4.1665", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028246956"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/1968.1972", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038881641"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/pgec.1965.264137", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061435370"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1126/science.1070978", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062446469"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1126/science.295.5557.951", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062576296"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1126/science.7725098", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062649072"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1126/science.7725109", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062649082"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1126/science.7973651", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062650775"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1090/dimacs/054", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1097022576"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1090/dimacs/044", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1097022682"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1090/dimacs/048", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1097022686"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2005-06", 
    "datePublishedReg": "2005-06-01", 
    "description": "A DNA-based memory was implemented with in vitro learning and associative recall.The learning protocol stored the sequences to which it was exposed, and memories were recalled by sequence content through DNA-to-DNA template annealing reactions. Experiments demonstrated that biological DNA could be learned, that sequences similar to the training DNA were recalled correctly, and that unlike sequences were differentiated. Theoretically, the memory has a pattern separation capability that is very large, and can learn long DNA sequences. The learning and recall protocols are massively parallel, as well as simple, inexpensive, and quick. The memory has several potential applications in detection and classification of biological sequences, as well as a massive storage capacity for non-biological data.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s11047-004-4002-3", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1033918", 
        "issn": [
          "1567-7818", 
          "1572-9796"
        ], 
        "name": "Natural Computing", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "2", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "4"
      }
    ], 
    "name": "A DNA-based memory with in vitro learning and associative recall", 
    "pagination": "83-101", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "728ace80fa7376ec166bd9349166d80e370fbb5263ee5279271111c0c5573e62"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s11047-004-4002-3"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1024042379"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s11047-004-4002-3", 
      "https://app.dimensions.ai/details/publication/pub.1024042379"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T11:13", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000353_0000000353/records_45372_00000000.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1007%2Fs11047-004-4002-3"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s11047-004-4002-3'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s11047-004-4002-3'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s11047-004-4002-3'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s11047-004-4002-3'


 

This table displays all metadata directly associated to this object as RDF triples.

140 TRIPLES      21 PREDICATES      46 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s11047-004-4002-3 schema:about anzsrc-for:06
2 anzsrc-for:0604
3 schema:author Nd48bb83bf9554de5a73dbbd02a7ea4e9
4 schema:citation sg:pub.10.1007/3-540-36440-4_22
5 sg:pub.10.1007/3-540-44992-2_15
6 sg:pub.10.1007/3-540-48017-x_22
7 sg:pub.10.1007/bf01192694
8 sg:pub.10.1038/28998
9 https://doi.org/10.1016/s1381-141x(97)80015-9
10 https://doi.org/10.1073/pnas.95.4.1460
11 https://doi.org/10.1073/pnas.97.4.1665
12 https://doi.org/10.1090/dimacs/044
13 https://doi.org/10.1090/dimacs/048
14 https://doi.org/10.1090/dimacs/054
15 https://doi.org/10.1109/pgec.1965.264137
16 https://doi.org/10.1126/science.1069528
17 https://doi.org/10.1126/science.1070978
18 https://doi.org/10.1126/science.295.5557.951
19 https://doi.org/10.1126/science.7725098
20 https://doi.org/10.1126/science.7725109
21 https://doi.org/10.1126/science.7973651
22 https://doi.org/10.1145/1968.1972
23 schema:datePublished 2005-06
24 schema:datePublishedReg 2005-06-01
25 schema:description A DNA-based memory was implemented with in vitro learning and associative recall.The learning protocol stored the sequences to which it was exposed, and memories were recalled by sequence content through DNA-to-DNA template annealing reactions. Experiments demonstrated that biological DNA could be learned, that sequences similar to the training DNA were recalled correctly, and that unlike sequences were differentiated. Theoretically, the memory has a pattern separation capability that is very large, and can learn long DNA sequences. The learning and recall protocols are massively parallel, as well as simple, inexpensive, and quick. The memory has several potential applications in detection and classification of biological sequences, as well as a massive storage capacity for non-biological data.
26 schema:genre research_article
27 schema:inLanguage en
28 schema:isAccessibleForFree false
29 schema:isPartOf N0c878b9bfce24fde8d117807782fb191
30 Ncaac9a29f69a4655b296252928ba441e
31 sg:journal.1033918
32 schema:name A DNA-based memory with in vitro learning and associative recall
33 schema:pagination 83-101
34 schema:productId N4209f496fc9444d89f9af347c8db739c
35 N52fb36c179c34b0fa13350dbc6d8918c
36 N7d7de716baf64fa89737f64d4e567062
37 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024042379
38 https://doi.org/10.1007/s11047-004-4002-3
39 schema:sdDatePublished 2019-04-11T11:13
40 schema:sdLicense https://scigraph.springernature.com/explorer/license/
41 schema:sdPublisher N2fb8f4a9caf14b1183f45adbd858ecb0
42 schema:url http://link.springer.com/10.1007%2Fs11047-004-4002-3
43 sgo:license sg:explorer/license/
44 sgo:sdDataset articles
45 rdf:type schema:ScholarlyArticle
46 N0c878b9bfce24fde8d117807782fb191 schema:issueNumber 2
47 rdf:type schema:PublicationIssue
48 N11174ca4e2b546bebbde5c34618454df rdf:first sg:person.014756500465.66
49 rdf:rest rdf:nil
50 N2fb8f4a9caf14b1183f45adbd858ecb0 schema:name Springer Nature - SN SciGraph project
51 rdf:type schema:Organization
52 N4209f496fc9444d89f9af347c8db739c schema:name doi
53 schema:value 10.1007/s11047-004-4002-3
54 rdf:type schema:PropertyValue
55 N52fb36c179c34b0fa13350dbc6d8918c schema:name readcube_id
56 schema:value 728ace80fa7376ec166bd9349166d80e370fbb5263ee5279271111c0c5573e62
57 rdf:type schema:PropertyValue
58 N7d7de716baf64fa89737f64d4e567062 schema:name dimensions_id
59 schema:value pub.1024042379
60 rdf:type schema:PropertyValue
61 Ncaac9a29f69a4655b296252928ba441e schema:volumeNumber 4
62 rdf:type schema:PublicationVolume
63 Nd48bb83bf9554de5a73dbbd02a7ea4e9 rdf:first sg:person.013246427324.08
64 rdf:rest Nf6b2fc1506204398bc0d542e7d15f679
65 Nf6b2fc1506204398bc0d542e7d15f679 rdf:first sg:person.0707334604.60
66 rdf:rest N11174ca4e2b546bebbde5c34618454df
67 anzsrc-for:06 schema:inDefinedTermSet anzsrc-for:
68 schema:name Biological Sciences
69 rdf:type schema:DefinedTerm
70 anzsrc-for:0604 schema:inDefinedTermSet anzsrc-for:
71 schema:name Genetics
72 rdf:type schema:DefinedTerm
73 sg:journal.1033918 schema:issn 1567-7818
74 1572-9796
75 schema:name Natural Computing
76 rdf:type schema:Periodical
77 sg:person.013246427324.08 schema:affiliation https://www.grid.ac/institutes/grid.33489.35
78 schema:familyName Chen
79 schema:givenName Junghuei
80 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013246427324.08
81 rdf:type schema:Person
82 sg:person.014756500465.66 schema:affiliation https://www.grid.ac/institutes/grid.33489.35
83 schema:familyName Wang
84 schema:givenName Yu-Zhen
85 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014756500465.66
86 rdf:type schema:Person
87 sg:person.0707334604.60 schema:affiliation https://www.grid.ac/institutes/grid.411017.2
88 schema:familyName Deaton
89 schema:givenName Russell
90 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0707334604.60
91 rdf:type schema:Person
92 sg:pub.10.1007/3-540-36440-4_22 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022065278
93 https://doi.org/10.1007/3-540-36440-4_22
94 rdf:type schema:CreativeWork
95 sg:pub.10.1007/3-540-44992-2_15 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017930394
96 https://doi.org/10.1007/3-540-44992-2_15
97 rdf:type schema:CreativeWork
98 sg:pub.10.1007/3-540-48017-x_22 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023263443
99 https://doi.org/10.1007/3-540-48017-x_22
100 rdf:type schema:CreativeWork
101 sg:pub.10.1007/bf01192694 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024191614
102 https://doi.org/10.1007/bf01192694
103 rdf:type schema:CreativeWork
104 sg:pub.10.1038/28998 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015431161
105 https://doi.org/10.1038/28998
106 rdf:type schema:CreativeWork
107 https://doi.org/10.1016/s1381-141x(97)80015-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010710951
108 rdf:type schema:CreativeWork
109 https://doi.org/10.1073/pnas.95.4.1460 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009187647
110 rdf:type schema:CreativeWork
111 https://doi.org/10.1073/pnas.97.4.1665 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028246956
112 rdf:type schema:CreativeWork
113 https://doi.org/10.1090/dimacs/044 schema:sameAs https://app.dimensions.ai/details/publication/pub.1097022682
114 rdf:type schema:CreativeWork
115 https://doi.org/10.1090/dimacs/048 schema:sameAs https://app.dimensions.ai/details/publication/pub.1097022686
116 rdf:type schema:CreativeWork
117 https://doi.org/10.1090/dimacs/054 schema:sameAs https://app.dimensions.ai/details/publication/pub.1097022576
118 rdf:type schema:CreativeWork
119 https://doi.org/10.1109/pgec.1965.264137 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061435370
120 rdf:type schema:CreativeWork
121 https://doi.org/10.1126/science.1069528 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006258875
122 rdf:type schema:CreativeWork
123 https://doi.org/10.1126/science.1070978 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062446469
124 rdf:type schema:CreativeWork
125 https://doi.org/10.1126/science.295.5557.951 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062576296
126 rdf:type schema:CreativeWork
127 https://doi.org/10.1126/science.7725098 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062649072
128 rdf:type schema:CreativeWork
129 https://doi.org/10.1126/science.7725109 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062649082
130 rdf:type schema:CreativeWork
131 https://doi.org/10.1126/science.7973651 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062650775
132 rdf:type schema:CreativeWork
133 https://doi.org/10.1145/1968.1972 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038881641
134 rdf:type schema:CreativeWork
135 https://www.grid.ac/institutes/grid.33489.35 schema:alternateName University of Delaware
136 schema:name Chemistry and Biochemistry, The University of Delaware, 19716, Newark, DE, USA
137 rdf:type schema:Organization
138 https://www.grid.ac/institutes/grid.411017.2 schema:alternateName University of Arkansas at Fayetteville
139 schema:name Computer Science and Engineering, The University of Arkansas, 72701, Fayetteville, AR, USA
140 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...