A Fast Algorithm for Large Common Connected Induced Subgraphs View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2017-04-25

AUTHORS

Alessio Conte , Roberto Grossi , Andrea Marino , Lorenzo Tattini , Luca Versari

ABSTRACT

We present a fast algorithm for finding large common subgraphs, which can be exploited for detecting structural and functional relationships between biological macromolecules. Many fast algorithms exist for finding a single maximum common subgraph. We show with an example that this gives limited information, motivating the less studied problem of finding many large common subgraphs covering different areas. As the latter is also hard, we give heuristics that improve performance by several orders of magnitude. As a case study, we validate our findings experimentally on protein graphs with thousands of atoms. More... »

PAGES

62-74

Book

TITLE

Algorithms for Computational Biology

ISBN

978-3-319-58162-0
978-3-319-58163-7

From Grant

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-58163-7_4

DOI

http://dx.doi.org/10.1007/978-3-319-58163-7_4

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1086868821


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/2008/0601", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Biochemistry and Cell Biology", 
        "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 Pisa", 
          "id": "https://www.grid.ac/institutes/grid.5395.a", 
          "name": [
            "Inria, Universit\u00e0 di Pisa and Erable, Pisa, Italy"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Conte", 
        "givenName": "Alessio", 
        "id": "sg:person.013571166511.36", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013571166511.36"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Pisa", 
          "id": "https://www.grid.ac/institutes/grid.5395.a", 
          "name": [
            "Inria, Universit\u00e0 di Pisa and Erable, Pisa, Italy"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Grossi", 
        "givenName": "Roberto", 
        "id": "sg:person.01062373707.91", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01062373707.91"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Pisa", 
          "id": "https://www.grid.ac/institutes/grid.5395.a", 
          "name": [
            "Inria, Universit\u00e0 di Pisa and Erable, Pisa, Italy"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Marino", 
        "givenName": "Andrea", 
        "id": "sg:person.07515532766.37", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07515532766.37"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Institute of Research on Cancer and Aging in Nice", 
          "id": "https://www.grid.ac/institutes/grid.463830.a", 
          "name": [
            "IRCAN, CNRS UMR, 7284, Nice, France"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Tattini", 
        "givenName": "Lorenzo", 
        "id": "sg:person.0736724411.38", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0736724411.38"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Scuola Normale Superiore di Pisa", 
          "id": "https://www.grid.ac/institutes/grid.6093.c", 
          "name": [
            "Scuola Normale Superiore, Pisa, Italy"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Versari", 
        "givenName": "Luca", 
        "id": "sg:person.016171366611.54", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016171366611.54"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1016/s0304-3975(00)00286-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002396737"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/321921.321925", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008268655"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0166-218x(95)00026-n", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013152121"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/11533719_73", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017311354", 
          "https://doi.org/10.1007/11533719_73"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/11533719_73", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017311354", 
          "https://doi.org/10.1007/11533719_73"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/btn307", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025948952"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1006/jmbi.1994.1657", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026428993"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/asi.20140", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027402315"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/spe.588", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027808390"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s1359-6446(02)02411-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032222306"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s1359-6446(02)02411-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032222306"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/1014052.1014123", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035394118"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/spe.4380120103", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039746604"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/3-540-55210-3_198", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040657953", 
          "https://doi.org/10.1007/3-540-55210-3_198"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf02575586", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041808659", 
          "https://doi.org/10.1007/bf02575586"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf02575586", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041808659", 
          "https://doi.org/10.1007/bf02575586"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/362342.362367", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049082651"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/btn186", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050561540"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0167-6377(90)90057-c", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052413170"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0167-6377(90)90057-c", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052413170"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/ci00056a002", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1055400933"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/ci9601675", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1055405148"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/ci9601675", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1055405148"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1089/cmb.1996.3.289", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1059245138"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/comjnl/45.6.631", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1059479450"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1504/ijbra.2013.054688", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1067439501"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.7155/jgaa.00139", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1073626425"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2017-04-25", 
    "datePublishedReg": "2017-04-25", 
    "description": "We present a fast algorithm for finding large common subgraphs, which can be exploited for detecting structural and functional relationships between biological macromolecules. Many fast algorithms exist for finding a single maximum common subgraph. We show with an example that this gives limited information, motivating the less studied problem of finding many large common subgraphs covering different areas. As the latter is also hard, we give heuristics that improve performance by several orders of magnitude. As a case study, we validate our findings experimentally on protein graphs with thousands of atoms.", 
    "editor": [
      {
        "familyName": "Figueiredo", 
        "givenName": "Daniel", 
        "type": "Person"
      }, 
      {
        "familyName": "Mart\u00edn-Vide", 
        "givenName": "Carlos", 
        "type": "Person"
      }, 
      {
        "familyName": "Pratas", 
        "givenName": "Diogo", 
        "type": "Person"
      }, 
      {
        "familyName": "Vega-Rodr\u00edguez", 
        "givenName": "Miguel A.", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/978-3-319-58163-7_4", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.6853177", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": {
      "isbn": [
        "978-3-319-58162-0", 
        "978-3-319-58163-7"
      ], 
      "name": "Algorithms for Computational Biology", 
      "type": "Book"
    }, 
    "name": "A Fast Algorithm for Large Common Connected Induced Subgraphs", 
    "pagination": "62-74", 
    "productId": [
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/978-3-319-58163-7_4"
        ]
      }, 
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "85a9d2a75643ce8eb1038d35a7e148d310c56f77b6ca4ad314372f449e3d5bef"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1086868821"
        ]
      }
    ], 
    "publisher": {
      "location": "Cham", 
      "name": "Springer International Publishing", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/978-3-319-58163-7_4", 
      "https://app.dimensions.ai/details/publication/pub.1086868821"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2019-04-16T05:02", 
    "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/v90/0000000325_0000000325/records_100819_00000000.jsonl", 
    "type": "Chapter", 
    "url": "https://link.springer.com/10.1007%2F978-3-319-58163-7_4"
  }
]
 

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.10.1007/978-3-319-58163-7_4'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-58163-7_4'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-58163-7_4'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-58163-7_4'


 

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

185 TRIPLES      23 PREDICATES      48 URIs      19 LITERALS      8 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/978-3-319-58163-7_4 schema:about anzsrc-for:06
2 anzsrc-for:0601
3 schema:author Nabdb092dae194769acf4c400a5eb8a0d
4 schema:citation sg:pub.10.1007/11533719_73
5 sg:pub.10.1007/3-540-55210-3_198
6 sg:pub.10.1007/bf02575586
7 https://doi.org/10.1002/asi.20140
8 https://doi.org/10.1002/spe.4380120103
9 https://doi.org/10.1002/spe.588
10 https://doi.org/10.1006/jmbi.1994.1657
11 https://doi.org/10.1016/0166-218x(95)00026-n
12 https://doi.org/10.1016/0167-6377(90)90057-c
13 https://doi.org/10.1016/s0304-3975(00)00286-3
14 https://doi.org/10.1016/s1359-6446(02)02411-x
15 https://doi.org/10.1021/ci00056a002
16 https://doi.org/10.1021/ci9601675
17 https://doi.org/10.1089/cmb.1996.3.289
18 https://doi.org/10.1093/bioinformatics/btn186
19 https://doi.org/10.1093/bioinformatics/btn307
20 https://doi.org/10.1093/comjnl/45.6.631
21 https://doi.org/10.1145/1014052.1014123
22 https://doi.org/10.1145/321921.321925
23 https://doi.org/10.1145/362342.362367
24 https://doi.org/10.1504/ijbra.2013.054688
25 https://doi.org/10.7155/jgaa.00139
26 schema:datePublished 2017-04-25
27 schema:datePublishedReg 2017-04-25
28 schema:description We present a fast algorithm for finding large common subgraphs, which can be exploited for detecting structural and functional relationships between biological macromolecules. Many fast algorithms exist for finding a single maximum common subgraph. We show with an example that this gives limited information, motivating the less studied problem of finding many large common subgraphs covering different areas. As the latter is also hard, we give heuristics that improve performance by several orders of magnitude. As a case study, we validate our findings experimentally on protein graphs with thousands of atoms.
29 schema:editor N685b7accf31b4b1087ff248a3010cf8a
30 schema:genre chapter
31 schema:inLanguage en
32 schema:isAccessibleForFree true
33 schema:isPartOf N528c103634bc4a1b8f10ffc575a2f59b
34 schema:name A Fast Algorithm for Large Common Connected Induced Subgraphs
35 schema:pagination 62-74
36 schema:productId N25e1c5dad70d40faa5127167db362ad6
37 N5322abba1a5d4ccb8297e837503dc480
38 Naabac02e09c745039b7b805d9bbd93d9
39 schema:publisher N3f40816492914f83936a30f74c8f43cd
40 schema:sameAs https://app.dimensions.ai/details/publication/pub.1086868821
41 https://doi.org/10.1007/978-3-319-58163-7_4
42 schema:sdDatePublished 2019-04-16T05:02
43 schema:sdLicense https://scigraph.springernature.com/explorer/license/
44 schema:sdPublisher N424294101a4d45a299eebab2cd4bd14c
45 schema:url https://link.springer.com/10.1007%2F978-3-319-58163-7_4
46 sgo:license sg:explorer/license/
47 sgo:sdDataset chapters
48 rdf:type schema:Chapter
49 N20b854a937b24a7885122adfd6c31392 schema:familyName Vega-Rodríguez
50 schema:givenName Miguel A.
51 rdf:type schema:Person
52 N248b9a23b72b437e922552eb4070d089 rdf:first sg:person.0736724411.38
53 rdf:rest N9952b6e575ae4ddd80fc9dbb988d6c2c
54 N25e1c5dad70d40faa5127167db362ad6 schema:name doi
55 schema:value 10.1007/978-3-319-58163-7_4
56 rdf:type schema:PropertyValue
57 N3f40816492914f83936a30f74c8f43cd schema:location Cham
58 schema:name Springer International Publishing
59 rdf:type schema:Organisation
60 N424294101a4d45a299eebab2cd4bd14c schema:name Springer Nature - SN SciGraph project
61 rdf:type schema:Organization
62 N528c103634bc4a1b8f10ffc575a2f59b schema:isbn 978-3-319-58162-0
63 978-3-319-58163-7
64 schema:name Algorithms for Computational Biology
65 rdf:type schema:Book
66 N5322abba1a5d4ccb8297e837503dc480 schema:name dimensions_id
67 schema:value pub.1086868821
68 rdf:type schema:PropertyValue
69 N685b7accf31b4b1087ff248a3010cf8a rdf:first Nd7b25d8ec5fa4d5cb7ac9a22d82e1c00
70 rdf:rest Nbfc38a79729f48828bf299fa1cb72ad6
71 N927f5269d66a49329fe598c1f5f70fb0 rdf:first sg:person.01062373707.91
72 rdf:rest Nc0520f4b8fb843b8a4ce138cb80c990d
73 N954eae1cf84548ee938b9eabd46f2886 rdf:first Nfa8c77c480824e8f8c91f8cccc21b4f5
74 rdf:rest Ne5338d342afb4d6ba690e49752a2528b
75 N9952b6e575ae4ddd80fc9dbb988d6c2c rdf:first sg:person.016171366611.54
76 rdf:rest rdf:nil
77 N9ba11e8439b048f4b4e2fdd74fd6fc1c schema:familyName Martín-Vide
78 schema:givenName Carlos
79 rdf:type schema:Person
80 Naabac02e09c745039b7b805d9bbd93d9 schema:name readcube_id
81 schema:value 85a9d2a75643ce8eb1038d35a7e148d310c56f77b6ca4ad314372f449e3d5bef
82 rdf:type schema:PropertyValue
83 Nabdb092dae194769acf4c400a5eb8a0d rdf:first sg:person.013571166511.36
84 rdf:rest N927f5269d66a49329fe598c1f5f70fb0
85 Nbfc38a79729f48828bf299fa1cb72ad6 rdf:first N9ba11e8439b048f4b4e2fdd74fd6fc1c
86 rdf:rest N954eae1cf84548ee938b9eabd46f2886
87 Nc0520f4b8fb843b8a4ce138cb80c990d rdf:first sg:person.07515532766.37
88 rdf:rest N248b9a23b72b437e922552eb4070d089
89 Nd7b25d8ec5fa4d5cb7ac9a22d82e1c00 schema:familyName Figueiredo
90 schema:givenName Daniel
91 rdf:type schema:Person
92 Ne5338d342afb4d6ba690e49752a2528b rdf:first N20b854a937b24a7885122adfd6c31392
93 rdf:rest rdf:nil
94 Nfa8c77c480824e8f8c91f8cccc21b4f5 schema:familyName Pratas
95 schema:givenName Diogo
96 rdf:type schema:Person
97 anzsrc-for:06 schema:inDefinedTermSet anzsrc-for:
98 schema:name Biological Sciences
99 rdf:type schema:DefinedTerm
100 anzsrc-for:0601 schema:inDefinedTermSet anzsrc-for:
101 schema:name Biochemistry and Cell Biology
102 rdf:type schema:DefinedTerm
103 sg:grant.6853177 http://pending.schema.org/fundedItem sg:pub.10.1007/978-3-319-58163-7_4
104 rdf:type schema:MonetaryGrant
105 sg:person.01062373707.91 schema:affiliation https://www.grid.ac/institutes/grid.5395.a
106 schema:familyName Grossi
107 schema:givenName Roberto
108 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01062373707.91
109 rdf:type schema:Person
110 sg:person.013571166511.36 schema:affiliation https://www.grid.ac/institutes/grid.5395.a
111 schema:familyName Conte
112 schema:givenName Alessio
113 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013571166511.36
114 rdf:type schema:Person
115 sg:person.016171366611.54 schema:affiliation https://www.grid.ac/institutes/grid.6093.c
116 schema:familyName Versari
117 schema:givenName Luca
118 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016171366611.54
119 rdf:type schema:Person
120 sg:person.0736724411.38 schema:affiliation https://www.grid.ac/institutes/grid.463830.a
121 schema:familyName Tattini
122 schema:givenName Lorenzo
123 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0736724411.38
124 rdf:type schema:Person
125 sg:person.07515532766.37 schema:affiliation https://www.grid.ac/institutes/grid.5395.a
126 schema:familyName Marino
127 schema:givenName Andrea
128 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07515532766.37
129 rdf:type schema:Person
130 sg:pub.10.1007/11533719_73 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017311354
131 https://doi.org/10.1007/11533719_73
132 rdf:type schema:CreativeWork
133 sg:pub.10.1007/3-540-55210-3_198 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040657953
134 https://doi.org/10.1007/3-540-55210-3_198
135 rdf:type schema:CreativeWork
136 sg:pub.10.1007/bf02575586 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041808659
137 https://doi.org/10.1007/bf02575586
138 rdf:type schema:CreativeWork
139 https://doi.org/10.1002/asi.20140 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027402315
140 rdf:type schema:CreativeWork
141 https://doi.org/10.1002/spe.4380120103 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039746604
142 rdf:type schema:CreativeWork
143 https://doi.org/10.1002/spe.588 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027808390
144 rdf:type schema:CreativeWork
145 https://doi.org/10.1006/jmbi.1994.1657 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026428993
146 rdf:type schema:CreativeWork
147 https://doi.org/10.1016/0166-218x(95)00026-n schema:sameAs https://app.dimensions.ai/details/publication/pub.1013152121
148 rdf:type schema:CreativeWork
149 https://doi.org/10.1016/0167-6377(90)90057-c schema:sameAs https://app.dimensions.ai/details/publication/pub.1052413170
150 rdf:type schema:CreativeWork
151 https://doi.org/10.1016/s0304-3975(00)00286-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002396737
152 rdf:type schema:CreativeWork
153 https://doi.org/10.1016/s1359-6446(02)02411-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1032222306
154 rdf:type schema:CreativeWork
155 https://doi.org/10.1021/ci00056a002 schema:sameAs https://app.dimensions.ai/details/publication/pub.1055400933
156 rdf:type schema:CreativeWork
157 https://doi.org/10.1021/ci9601675 schema:sameAs https://app.dimensions.ai/details/publication/pub.1055405148
158 rdf:type schema:CreativeWork
159 https://doi.org/10.1089/cmb.1996.3.289 schema:sameAs https://app.dimensions.ai/details/publication/pub.1059245138
160 rdf:type schema:CreativeWork
161 https://doi.org/10.1093/bioinformatics/btn186 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050561540
162 rdf:type schema:CreativeWork
163 https://doi.org/10.1093/bioinformatics/btn307 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025948952
164 rdf:type schema:CreativeWork
165 https://doi.org/10.1093/comjnl/45.6.631 schema:sameAs https://app.dimensions.ai/details/publication/pub.1059479450
166 rdf:type schema:CreativeWork
167 https://doi.org/10.1145/1014052.1014123 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035394118
168 rdf:type schema:CreativeWork
169 https://doi.org/10.1145/321921.321925 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008268655
170 rdf:type schema:CreativeWork
171 https://doi.org/10.1145/362342.362367 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049082651
172 rdf:type schema:CreativeWork
173 https://doi.org/10.1504/ijbra.2013.054688 schema:sameAs https://app.dimensions.ai/details/publication/pub.1067439501
174 rdf:type schema:CreativeWork
175 https://doi.org/10.7155/jgaa.00139 schema:sameAs https://app.dimensions.ai/details/publication/pub.1073626425
176 rdf:type schema:CreativeWork
177 https://www.grid.ac/institutes/grid.463830.a schema:alternateName Institute of Research on Cancer and Aging in Nice
178 schema:name IRCAN, CNRS UMR, 7284, Nice, France
179 rdf:type schema:Organization
180 https://www.grid.ac/institutes/grid.5395.a schema:alternateName University of Pisa
181 schema:name Inria, Università di Pisa and Erable, Pisa, Italy
182 rdf:type schema:Organization
183 https://www.grid.ac/institutes/grid.6093.c schema:alternateName Scuola Normale Superiore di Pisa
184 schema:name Scuola Normale Superiore, Pisa, Italy
185 rdf:type schema:Organization
 




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


...