Protein Fold Discovery Using Stochastic Logic Programs View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2008

AUTHORS

Jianzhong Chen , Lawrence Kelley , Stephen Muggleton , Michael Sternberg

ABSTRACT

This chapter starts with a general introduction to protein folding. We then present a probabilistic method of dealing with multi-class classification, in particular multi-class protein fold prediction, using Stochastic Logic Programs (SLPs). Multi-class prediction attempts to classify an observed datum or example into its proper classification given that it has been tested to have multiple predictions. We apply an SLP parameter estimation algorithm to a previous study in the protein fold prediction area, in which logic programs have been learned by Inductive Logic Programming (ILP) and a large number of multiple predictions have been detected. On the basis of several experiments, we demonstrate that PILP approaches (eg. SLPs) have advantages for solving multi-class (protein fold) prediction problems with the help of learned probabilities. In addition, we show that SLPs outperform ILP plus majority class predictor in both predictive accuracy and result interpretability. More... »

PAGES

244-262

Book

TITLE

Probabilistic Inductive Logic Programming

ISBN

978-3-540-78651-1
978-3-540-78652-8

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-540-78652-8_9

DOI

http://dx.doi.org/10.1007/978-3-540-78652-8_9

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1024447930


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/2008/0104", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Statistics", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/01", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Mathematical Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Imperial College London", 
          "id": "https://www.grid.ac/institutes/grid.7445.2", 
          "name": [
            "Department of Computing, Imperial College London, SW7 2AZ, London, UK"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Chen", 
        "givenName": "Jianzhong", 
        "id": "sg:person.013012363165.39", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013012363165.39"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Imperial College London", 
          "id": "https://www.grid.ac/institutes/grid.7445.2", 
          "name": [
            "Department of Biological Sciences, Imperial College London, SW7 2AZ, London, UK"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kelley", 
        "givenName": "Lawrence", 
        "id": "sg:person.01037232756.19", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01037232756.19"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Imperial College London", 
          "id": "https://www.grid.ac/institutes/grid.7445.2", 
          "name": [
            "Department of Computing, Imperial College London, SW7 2AZ, London, UK"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Muggleton", 
        "givenName": "Stephen", 
        "id": "sg:person.01125137176.85", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01125137176.85"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Imperial College London", 
          "id": "https://www.grid.ac/institutes/grid.7445.2", 
          "name": [
            "Department of Biological Sciences, Imperial College London, SW7 2AZ, London, UK"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Sternberg", 
        "givenName": "Michael", 
        "id": "sg:person.0611736450.97", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0611736450.97"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/11871842_20", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002276260", 
          "https://doi.org/10.1007/11871842_20"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/11871842_20", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002276260", 
          "https://doi.org/10.1007/11871842_20"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1023/a:1010920819831", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003442924", 
          "https://doi.org/10.1023/a:1010920819831"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1006/jmbi.2000.4414", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003630393"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-540-30115-8_21", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004376671", 
          "https://doi.org/10.1007/978-3-540-30115-8_21"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-540-30115-8_21", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004376671", 
          "https://doi.org/10.1007/978-3-540-30115-8_21"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0076-6879(96)66039-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006906574"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1023/a:1007672817406", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015313414", 
          "https://doi.org/10.1023/a:1007672817406"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/nar/gki024", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017971317"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/17.4.349", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024575287"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1023/a:1010924021315", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038586810", 
          "https://doi.org/10.1023/a:1010924021315"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2164-7-190", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039682406", 
          "https://doi.org/10.1186/1471-2164-7-190"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/3-540-36169-3_29", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040688873", 
          "https://doi.org/10.1007/3-540-36169-3_29"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/3-540-36169-3_29", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040688873", 
          "https://doi.org/10.1007/3-540-36169-3_29"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0022-2836(03)00620-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041832335"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0022-2836(03)00620-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041832335"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1098/rstb.2005.1810", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046944457"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1613/jair.1675", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1105579350"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3233/ida-2004-8503", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1107705270"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2008", 
    "datePublishedReg": "2008-01-01", 
    "description": "This chapter starts with a general introduction to protein folding. We then present a probabilistic method of dealing with multi-class classification, in particular multi-class protein fold prediction, using Stochastic Logic Programs (SLPs). Multi-class prediction attempts to classify an observed datum or example into its proper classification given that it has been tested to have multiple predictions. We apply an SLP parameter estimation algorithm to a previous study in the protein fold prediction area, in which logic programs have been learned by Inductive Logic Programming (ILP) and a large number of multiple predictions have been detected. On the basis of several experiments, we demonstrate that PILP approaches (eg. SLPs) have advantages for solving multi-class (protein fold) prediction problems with the help of learned probabilities. In addition, we show that SLPs outperform ILP plus majority class predictor in both predictive accuracy and result interpretability.", 
    "editor": [
      {
        "familyName": "De Raedt", 
        "givenName": "Luc", 
        "type": "Person"
      }, 
      {
        "familyName": "Frasconi", 
        "givenName": "Paolo", 
        "type": "Person"
      }, 
      {
        "familyName": "Kersting", 
        "givenName": "Kristian", 
        "type": "Person"
      }, 
      {
        "familyName": "Muggleton", 
        "givenName": "Stephen", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/978-3-540-78652-8_9", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": {
      "isbn": [
        "978-3-540-78651-1", 
        "978-3-540-78652-8"
      ], 
      "name": "Probabilistic Inductive Logic Programming", 
      "type": "Book"
    }, 
    "name": "Protein Fold Discovery Using Stochastic Logic Programs", 
    "pagination": "244-262", 
    "productId": [
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/978-3-540-78652-8_9"
        ]
      }, 
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "7478b75110ee498fb13403f7115621c4af8429a3ef5d75ee39ad71829e554f8a"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1024447930"
        ]
      }
    ], 
    "publisher": {
      "location": "Berlin, Heidelberg", 
      "name": "Springer Berlin Heidelberg", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/978-3-540-78652-8_9", 
      "https://app.dimensions.ai/details/publication/pub.1024447930"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2019-04-16T05:59", 
    "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/v90/0000000349_0000000349/records_113647_00000000.jsonl", 
    "type": "Chapter", 
    "url": "https://link.springer.com/10.1007%2F978-3-540-78652-8_9"
  }
]
 

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.10.1007/978-3-540-78652-8_9'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-540-78652-8_9'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-540-78652-8_9'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-540-78652-8_9'


 

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

154 TRIPLES      23 PREDICATES      42 URIs      20 LITERALS      8 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/978-3-540-78652-8_9 schema:about anzsrc-for:01
2 anzsrc-for:0104
3 schema:author Nee27a9fb90b14ee59d0815e56c21776d
4 schema:citation sg:pub.10.1007/11871842_20
5 sg:pub.10.1007/3-540-36169-3_29
6 sg:pub.10.1007/978-3-540-30115-8_21
7 sg:pub.10.1023/a:1007672817406
8 sg:pub.10.1023/a:1010920819831
9 sg:pub.10.1023/a:1010924021315
10 sg:pub.10.1186/1471-2164-7-190
11 https://doi.org/10.1006/jmbi.2000.4414
12 https://doi.org/10.1016/s0022-2836(03)00620-x
13 https://doi.org/10.1016/s0076-6879(96)66039-x
14 https://doi.org/10.1093/bioinformatics/17.4.349
15 https://doi.org/10.1093/nar/gki024
16 https://doi.org/10.1098/rstb.2005.1810
17 https://doi.org/10.1613/jair.1675
18 https://doi.org/10.3233/ida-2004-8503
19 schema:datePublished 2008
20 schema:datePublishedReg 2008-01-01
21 schema:description This chapter starts with a general introduction to protein folding. We then present a probabilistic method of dealing with multi-class classification, in particular multi-class protein fold prediction, using Stochastic Logic Programs (SLPs). Multi-class prediction attempts to classify an observed datum or example into its proper classification given that it has been tested to have multiple predictions. We apply an SLP parameter estimation algorithm to a previous study in the protein fold prediction area, in which logic programs have been learned by Inductive Logic Programming (ILP) and a large number of multiple predictions have been detected. On the basis of several experiments, we demonstrate that PILP approaches (eg. SLPs) have advantages for solving multi-class (protein fold) prediction problems with the help of learned probabilities. In addition, we show that SLPs outperform ILP plus majority class predictor in both predictive accuracy and result interpretability.
22 schema:editor N4e40a3eb020c4988a2adfab71c6f6b3f
23 schema:genre chapter
24 schema:inLanguage en
25 schema:isAccessibleForFree false
26 schema:isPartOf Nbe015ca540064501977efb9c4a3c71e2
27 schema:name Protein Fold Discovery Using Stochastic Logic Programs
28 schema:pagination 244-262
29 schema:productId N493de47bccf34e1db0c8589f689d9fb5
30 N6b94db3880a646a990df60e98ab11ac4
31 Nac293bb99e0243378cf5ed4339f9cda7
32 schema:publisher N547894fe58384300a1204166fe7c1eb5
33 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024447930
34 https://doi.org/10.1007/978-3-540-78652-8_9
35 schema:sdDatePublished 2019-04-16T05:59
36 schema:sdLicense https://scigraph.springernature.com/explorer/license/
37 schema:sdPublisher N0d900bcc7be347b390f62ec4c1e06d8c
38 schema:url https://link.springer.com/10.1007%2F978-3-540-78652-8_9
39 sgo:license sg:explorer/license/
40 sgo:sdDataset chapters
41 rdf:type schema:Chapter
42 N0a72b5efced04563b4a23f5417c07b52 schema:familyName Kersting
43 schema:givenName Kristian
44 rdf:type schema:Person
45 N0d900bcc7be347b390f62ec4c1e06d8c schema:name Springer Nature - SN SciGraph project
46 rdf:type schema:Organization
47 N493de47bccf34e1db0c8589f689d9fb5 schema:name doi
48 schema:value 10.1007/978-3-540-78652-8_9
49 rdf:type schema:PropertyValue
50 N4ca69107e8d74cc2a48cbe9d53082fa2 schema:familyName Frasconi
51 schema:givenName Paolo
52 rdf:type schema:Person
53 N4e40a3eb020c4988a2adfab71c6f6b3f rdf:first N78b173b7e3a141ff99aafa7d66b6cb34
54 rdf:rest N6d7982596f5d48439257f23be6653763
55 N547894fe58384300a1204166fe7c1eb5 schema:location Berlin, Heidelberg
56 schema:name Springer Berlin Heidelberg
57 rdf:type schema:Organisation
58 N6b94db3880a646a990df60e98ab11ac4 schema:name readcube_id
59 schema:value 7478b75110ee498fb13403f7115621c4af8429a3ef5d75ee39ad71829e554f8a
60 rdf:type schema:PropertyValue
61 N6d7982596f5d48439257f23be6653763 rdf:first N4ca69107e8d74cc2a48cbe9d53082fa2
62 rdf:rest N94d9891a92bf4ee6b80d3c953d896f30
63 N6f0ce992f5184769b5ad786c4d82e8df rdf:first sg:person.0611736450.97
64 rdf:rest rdf:nil
65 N75cd7b0208a84a40afb156faa9df036a rdf:first N9242f9ccb6854ad1a331ddd71098a07d
66 rdf:rest rdf:nil
67 N78b173b7e3a141ff99aafa7d66b6cb34 schema:familyName De Raedt
68 schema:givenName Luc
69 rdf:type schema:Person
70 N9242f9ccb6854ad1a331ddd71098a07d schema:familyName Muggleton
71 schema:givenName Stephen
72 rdf:type schema:Person
73 N94d9891a92bf4ee6b80d3c953d896f30 rdf:first N0a72b5efced04563b4a23f5417c07b52
74 rdf:rest N75cd7b0208a84a40afb156faa9df036a
75 Nac293bb99e0243378cf5ed4339f9cda7 schema:name dimensions_id
76 schema:value pub.1024447930
77 rdf:type schema:PropertyValue
78 Nbe015ca540064501977efb9c4a3c71e2 schema:isbn 978-3-540-78651-1
79 978-3-540-78652-8
80 schema:name Probabilistic Inductive Logic Programming
81 rdf:type schema:Book
82 Nd03c515c6f654f328f9ca05c0e3850ea rdf:first sg:person.01125137176.85
83 rdf:rest N6f0ce992f5184769b5ad786c4d82e8df
84 Nedab788aa47742c4930d5d9c22ce9958 rdf:first sg:person.01037232756.19
85 rdf:rest Nd03c515c6f654f328f9ca05c0e3850ea
86 Nee27a9fb90b14ee59d0815e56c21776d rdf:first sg:person.013012363165.39
87 rdf:rest Nedab788aa47742c4930d5d9c22ce9958
88 anzsrc-for:01 schema:inDefinedTermSet anzsrc-for:
89 schema:name Mathematical Sciences
90 rdf:type schema:DefinedTerm
91 anzsrc-for:0104 schema:inDefinedTermSet anzsrc-for:
92 schema:name Statistics
93 rdf:type schema:DefinedTerm
94 sg:person.01037232756.19 schema:affiliation https://www.grid.ac/institutes/grid.7445.2
95 schema:familyName Kelley
96 schema:givenName Lawrence
97 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01037232756.19
98 rdf:type schema:Person
99 sg:person.01125137176.85 schema:affiliation https://www.grid.ac/institutes/grid.7445.2
100 schema:familyName Muggleton
101 schema:givenName Stephen
102 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01125137176.85
103 rdf:type schema:Person
104 sg:person.013012363165.39 schema:affiliation https://www.grid.ac/institutes/grid.7445.2
105 schema:familyName Chen
106 schema:givenName Jianzhong
107 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013012363165.39
108 rdf:type schema:Person
109 sg:person.0611736450.97 schema:affiliation https://www.grid.ac/institutes/grid.7445.2
110 schema:familyName Sternberg
111 schema:givenName Michael
112 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0611736450.97
113 rdf:type schema:Person
114 sg:pub.10.1007/11871842_20 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002276260
115 https://doi.org/10.1007/11871842_20
116 rdf:type schema:CreativeWork
117 sg:pub.10.1007/3-540-36169-3_29 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040688873
118 https://doi.org/10.1007/3-540-36169-3_29
119 rdf:type schema:CreativeWork
120 sg:pub.10.1007/978-3-540-30115-8_21 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004376671
121 https://doi.org/10.1007/978-3-540-30115-8_21
122 rdf:type schema:CreativeWork
123 sg:pub.10.1023/a:1007672817406 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015313414
124 https://doi.org/10.1023/a:1007672817406
125 rdf:type schema:CreativeWork
126 sg:pub.10.1023/a:1010920819831 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003442924
127 https://doi.org/10.1023/a:1010920819831
128 rdf:type schema:CreativeWork
129 sg:pub.10.1023/a:1010924021315 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038586810
130 https://doi.org/10.1023/a:1010924021315
131 rdf:type schema:CreativeWork
132 sg:pub.10.1186/1471-2164-7-190 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039682406
133 https://doi.org/10.1186/1471-2164-7-190
134 rdf:type schema:CreativeWork
135 https://doi.org/10.1006/jmbi.2000.4414 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003630393
136 rdf:type schema:CreativeWork
137 https://doi.org/10.1016/s0022-2836(03)00620-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1041832335
138 rdf:type schema:CreativeWork
139 https://doi.org/10.1016/s0076-6879(96)66039-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1006906574
140 rdf:type schema:CreativeWork
141 https://doi.org/10.1093/bioinformatics/17.4.349 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024575287
142 rdf:type schema:CreativeWork
143 https://doi.org/10.1093/nar/gki024 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017971317
144 rdf:type schema:CreativeWork
145 https://doi.org/10.1098/rstb.2005.1810 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046944457
146 rdf:type schema:CreativeWork
147 https://doi.org/10.1613/jair.1675 schema:sameAs https://app.dimensions.ai/details/publication/pub.1105579350
148 rdf:type schema:CreativeWork
149 https://doi.org/10.3233/ida-2004-8503 schema:sameAs https://app.dimensions.ai/details/publication/pub.1107705270
150 rdf:type schema:CreativeWork
151 https://www.grid.ac/institutes/grid.7445.2 schema:alternateName Imperial College London
152 schema:name Department of Biological Sciences, Imperial College London, SW7 2AZ, London, UK
153 Department of Computing, Imperial College London, SW7 2AZ, London, UK
154 rdf:type schema:Organization
 




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


...