DNA sequencing and string learning View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

1996-08

AUTHORS

Tao Jiang, Ming Li

ABSTRACT

In laboratories the majority of large-scale DNA sequencing is done following theshotgun strategy, which is to sequence large amount of relatively short fragments randomly and then heuristically find a shortest common superstring of the fragments [26]. We study mathematical frameworks, under plausible assumptions, suitable for massive automated DNA sequencing and for analyzing DNA sequencing algorithms. We model the DNA sequencing problem as learning a string from its randomly drawn substrings. Under certain restrictions, this may be viewed as string learning in Valiant's distribution-free learning model and in this case we give an efficient learning algorithm and a quantitative bound on how many examples suffice. One major obstacle to our approach turns out to be a quite well-known open question on how to approximate a shortest common superstring of a set of strings, raised by a number of authors in the last 10 years [9], [29], [30]. We give the firstprovably good algorithm which approximates a shortest superstring of lengthn by a superstring of lengthO(n logn). The algorithm works equally well even in the presence of negative examples, i.e., when merging of some strings is prohibited. More... »

PAGES

387-405

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/bf01192694

DOI

http://dx.doi.org/10.1007/bf01192694

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1024191614


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": "McMaster University", 
          "id": "https://www.grid.ac/institutes/grid.25073.33", 
          "name": [
            "Department of Computer Science, McMaster University, L8S 4K1, Hamilton, Ontario, Canada"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Jiang", 
        "givenName": "Tao", 
        "id": "sg:person.011617335564.48", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011617335564.48"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Waterloo", 
          "id": "https://www.grid.ac/institutes/grid.46078.3d", 
          "name": [
            "Department of Computer Science, University of Waterloo, N2L 3G1, Waterloo, Ontario, Canada"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Li", 
        "givenName": "Ming", 
        "id": "sg:person.0621576316.79", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0621576316.79"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/bf00058680", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002803458", 
          "https://doi.org/10.1007/bf00058680"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/76359.76371", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004063675"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0004-3702(88)90002-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006302975"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0004-3702(88)90002-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006302975"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/nar/10.15.4731", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1007734579"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf00116829", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011409678", 
          "https://doi.org/10.1007/bf00116829"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/b978-0-08-094829-4.50006-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011498679"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1012361330", 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-1-4757-3860-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1012361330", 
          "https://doi.org/10.1007/978-1-4757-3860-5"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-1-4757-3860-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1012361330", 
          "https://doi.org/10.1007/978-1-4757-3860-5"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/28395.28426", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1012505442"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0304-3975(93)90167-r", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013727978"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0022-0000(80)90004-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019119572"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0304-3975(88)90167-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019958976"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1098/rsta.1984.0069", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027907269"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/4547.4550", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028096653"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0890-5401(89)90044-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1029662067"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0020-0190(87)90114-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031474319"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0020-0190(87)90114-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031474319"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-662-12405-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036581552", 
          "https://doi.org/10.1007/978-3-662-12405-5"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-662-12405-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036581552", 
          "https://doi.org/10.1007/978-3-662-12405-5"
        ], 
        "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/10.1145/167088.167170", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045330146"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/103418.103455", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046277528"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1126/science.8211178", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062653692"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1137/0222052", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062842455"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1287/moor.4.3.233", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064724457"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/sfcs.1985.22", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1086206112"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "1996-08", 
    "datePublishedReg": "1996-08-01", 
    "description": "In laboratories the majority of large-scale DNA sequencing is done following theshotgun strategy, which is to sequence large amount of relatively short fragments randomly and then heuristically find a shortest common superstring of the fragments [26]. We study mathematical frameworks, under plausible assumptions, suitable for massive automated DNA sequencing and for analyzing DNA sequencing algorithms. We model the DNA sequencing problem as learning a string from its randomly drawn substrings. Under certain restrictions, this may be viewed as string learning in Valiant's distribution-free learning model and in this case we give an efficient learning algorithm and a quantitative bound on how many examples suffice. One major obstacle to our approach turns out to be a quite well-known open question on how to approximate a shortest common superstring of a set of strings, raised by a number of authors in the last 10 years [9], [29], [30]. We give the firstprovably good algorithm which approximates a shortest superstring of lengthn by a superstring of lengthO(n logn). The algorithm works equally well even in the presence of negative examples, i.e., when merging of some strings is prohibited.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/bf01192694", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isPartOf": [
      {
        "id": "sg:journal.1317508", 
        "issn": [
          "0025-5661"
        ], 
        "name": "Mathematical Systems Theory", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "4", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "29"
      }
    ], 
    "name": "DNA sequencing and string learning", 
    "pagination": "387-405", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "734080b7046dc73fc1106f6a8dff2ca62f247ce3c81339b4767fae4c7ae250b6"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/bf01192694"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1024191614"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/bf01192694", 
      "https://app.dimensions.ai/details/publication/pub.1024191614"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T13:28", 
    "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/v90/0000000370_0000000370/records_46741_00000001.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1007/BF01192694"
  }
]
 

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/bf01192694'

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/bf01192694'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/bf01192694'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/bf01192694'


 

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

145 TRIPLES      21 PREDICATES      51 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/bf01192694 schema:about anzsrc-for:06
2 anzsrc-for:0604
3 schema:author N4fdcde90ac094f97a889d8397df1fc4b
4 schema:citation sg:pub.10.1007/978-1-4757-3860-5
5 sg:pub.10.1007/978-3-662-12405-5
6 sg:pub.10.1007/bf00058680
7 sg:pub.10.1007/bf00116829
8 https://app.dimensions.ai/details/publication/pub.1012361330
9 https://doi.org/10.1016/0004-3702(88)90002-1
10 https://doi.org/10.1016/0020-0190(87)90114-1
11 https://doi.org/10.1016/0022-0000(80)90004-5
12 https://doi.org/10.1016/0304-3975(88)90167-3
13 https://doi.org/10.1016/0304-3975(93)90167-r
14 https://doi.org/10.1016/0890-5401(89)90044-8
15 https://doi.org/10.1016/b978-0-08-094829-4.50006-4
16 https://doi.org/10.1093/nar/10.15.4731
17 https://doi.org/10.1098/rsta.1984.0069
18 https://doi.org/10.1109/sfcs.1985.22
19 https://doi.org/10.1126/science.8211178
20 https://doi.org/10.1137/0222052
21 https://doi.org/10.1145/103418.103455
22 https://doi.org/10.1145/167088.167170
23 https://doi.org/10.1145/1968.1972
24 https://doi.org/10.1145/28395.28426
25 https://doi.org/10.1145/4547.4550
26 https://doi.org/10.1145/76359.76371
27 https://doi.org/10.1287/moor.4.3.233
28 schema:datePublished 1996-08
29 schema:datePublishedReg 1996-08-01
30 schema:description In laboratories the majority of large-scale DNA sequencing is done following theshotgun strategy, which is to sequence large amount of relatively short fragments randomly and then heuristically find a shortest common superstring of the fragments [26]. We study mathematical frameworks, under plausible assumptions, suitable for massive automated DNA sequencing and for analyzing DNA sequencing algorithms. We model the DNA sequencing problem as learning a string from its randomly drawn substrings. Under certain restrictions, this may be viewed as string learning in Valiant's distribution-free learning model and in this case we give an efficient learning algorithm and a quantitative bound on how many examples suffice. One major obstacle to our approach turns out to be a quite well-known open question on how to approximate a shortest common superstring of a set of strings, raised by a number of authors in the last 10 years [9], [29], [30]. We give the firstprovably good algorithm which approximates a shortest superstring of lengthn by a superstring of lengthO(n logn). The algorithm works equally well even in the presence of negative examples, i.e., when merging of some strings is prohibited.
31 schema:genre research_article
32 schema:inLanguage en
33 schema:isAccessibleForFree true
34 schema:isPartOf N2d91860f5a3543998ec63084bc4dbfce
35 N9c4d84e7aff7471ea732fe032ba6dc2e
36 sg:journal.1317508
37 schema:name DNA sequencing and string learning
38 schema:pagination 387-405
39 schema:productId N133fc45761ee408398c3ca175b1f814d
40 Nb2a2ad66a94d4c7491aeda27ba5bfce6
41 Nbeea3520a78a4994b7f532aadaaff730
42 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024191614
43 https://doi.org/10.1007/bf01192694
44 schema:sdDatePublished 2019-04-11T13:28
45 schema:sdLicense https://scigraph.springernature.com/explorer/license/
46 schema:sdPublisher N309d16367d9f4b27ab46cfc1c86f24ce
47 schema:url http://link.springer.com/10.1007/BF01192694
48 sgo:license sg:explorer/license/
49 sgo:sdDataset articles
50 rdf:type schema:ScholarlyArticle
51 N133fc45761ee408398c3ca175b1f814d schema:name dimensions_id
52 schema:value pub.1024191614
53 rdf:type schema:PropertyValue
54 N2d91860f5a3543998ec63084bc4dbfce schema:issueNumber 4
55 rdf:type schema:PublicationIssue
56 N309d16367d9f4b27ab46cfc1c86f24ce schema:name Springer Nature - SN SciGraph project
57 rdf:type schema:Organization
58 N4fdcde90ac094f97a889d8397df1fc4b rdf:first sg:person.011617335564.48
59 rdf:rest N8780a1ccf2d04f35a8f0e9ae94153f35
60 N8780a1ccf2d04f35a8f0e9ae94153f35 rdf:first sg:person.0621576316.79
61 rdf:rest rdf:nil
62 N9c4d84e7aff7471ea732fe032ba6dc2e schema:volumeNumber 29
63 rdf:type schema:PublicationVolume
64 Nb2a2ad66a94d4c7491aeda27ba5bfce6 schema:name readcube_id
65 schema:value 734080b7046dc73fc1106f6a8dff2ca62f247ce3c81339b4767fae4c7ae250b6
66 rdf:type schema:PropertyValue
67 Nbeea3520a78a4994b7f532aadaaff730 schema:name doi
68 schema:value 10.1007/bf01192694
69 rdf:type schema:PropertyValue
70 anzsrc-for:06 schema:inDefinedTermSet anzsrc-for:
71 schema:name Biological Sciences
72 rdf:type schema:DefinedTerm
73 anzsrc-for:0604 schema:inDefinedTermSet anzsrc-for:
74 schema:name Genetics
75 rdf:type schema:DefinedTerm
76 sg:journal.1317508 schema:issn 0025-5661
77 schema:name Mathematical Systems Theory
78 rdf:type schema:Periodical
79 sg:person.011617335564.48 schema:affiliation https://www.grid.ac/institutes/grid.25073.33
80 schema:familyName Jiang
81 schema:givenName Tao
82 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011617335564.48
83 rdf:type schema:Person
84 sg:person.0621576316.79 schema:affiliation https://www.grid.ac/institutes/grid.46078.3d
85 schema:familyName Li
86 schema:givenName Ming
87 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0621576316.79
88 rdf:type schema:Person
89 sg:pub.10.1007/978-1-4757-3860-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012361330
90 https://doi.org/10.1007/978-1-4757-3860-5
91 rdf:type schema:CreativeWork
92 sg:pub.10.1007/978-3-662-12405-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036581552
93 https://doi.org/10.1007/978-3-662-12405-5
94 rdf:type schema:CreativeWork
95 sg:pub.10.1007/bf00058680 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002803458
96 https://doi.org/10.1007/bf00058680
97 rdf:type schema:CreativeWork
98 sg:pub.10.1007/bf00116829 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011409678
99 https://doi.org/10.1007/bf00116829
100 rdf:type schema:CreativeWork
101 https://app.dimensions.ai/details/publication/pub.1012361330 schema:CreativeWork
102 https://doi.org/10.1016/0004-3702(88)90002-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006302975
103 rdf:type schema:CreativeWork
104 https://doi.org/10.1016/0020-0190(87)90114-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031474319
105 rdf:type schema:CreativeWork
106 https://doi.org/10.1016/0022-0000(80)90004-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019119572
107 rdf:type schema:CreativeWork
108 https://doi.org/10.1016/0304-3975(88)90167-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019958976
109 rdf:type schema:CreativeWork
110 https://doi.org/10.1016/0304-3975(93)90167-r schema:sameAs https://app.dimensions.ai/details/publication/pub.1013727978
111 rdf:type schema:CreativeWork
112 https://doi.org/10.1016/0890-5401(89)90044-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029662067
113 rdf:type schema:CreativeWork
114 https://doi.org/10.1016/b978-0-08-094829-4.50006-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011498679
115 rdf:type schema:CreativeWork
116 https://doi.org/10.1093/nar/10.15.4731 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007734579
117 rdf:type schema:CreativeWork
118 https://doi.org/10.1098/rsta.1984.0069 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027907269
119 rdf:type schema:CreativeWork
120 https://doi.org/10.1109/sfcs.1985.22 schema:sameAs https://app.dimensions.ai/details/publication/pub.1086206112
121 rdf:type schema:CreativeWork
122 https://doi.org/10.1126/science.8211178 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062653692
123 rdf:type schema:CreativeWork
124 https://doi.org/10.1137/0222052 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062842455
125 rdf:type schema:CreativeWork
126 https://doi.org/10.1145/103418.103455 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046277528
127 rdf:type schema:CreativeWork
128 https://doi.org/10.1145/167088.167170 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045330146
129 rdf:type schema:CreativeWork
130 https://doi.org/10.1145/1968.1972 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038881641
131 rdf:type schema:CreativeWork
132 https://doi.org/10.1145/28395.28426 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012505442
133 rdf:type schema:CreativeWork
134 https://doi.org/10.1145/4547.4550 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028096653
135 rdf:type schema:CreativeWork
136 https://doi.org/10.1145/76359.76371 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004063675
137 rdf:type schema:CreativeWork
138 https://doi.org/10.1287/moor.4.3.233 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064724457
139 rdf:type schema:CreativeWork
140 https://www.grid.ac/institutes/grid.25073.33 schema:alternateName McMaster University
141 schema:name Department of Computer Science, McMaster University, L8S 4K1, Hamilton, Ontario, Canada
142 rdf:type schema:Organization
143 https://www.grid.ac/institutes/grid.46078.3d schema:alternateName University of Waterloo
144 schema:name Department of Computer Science, University of Waterloo, N2L 3G1, Waterloo, Ontario, Canada
145 rdf:type schema:Organization
 




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


...