ModelExplorer - software for visual inspection and inconsistency correction of genome-scale metabolic reconstructions View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2019-12

AUTHORS

Nikolay Martyushenko, Eivind Almaas

ABSTRACT

BACKGROUND: Genome-scale metabolic network reconstructions are low level chemical representations of biological organisms. These models allow the system-level investigation of metabolic phenotypes using a variety of computational approaches. The link between a metabolic network model and an organisms' higher-level behaviour is usually found using a constraint-based analysis approach, such as FBA (Flux Balance Analysis). However, the process of model reconstruction rarely proceeds without error. Often, considerable parts of a model cannot carry flux under any condition. This is termed model inconsistency and is caused by faulty topology and/or stoichiometry of the underlying reconstructed network. While there exist several automated gap-filling tools that may solve some of the inconsistencies, much of the work still needs to be carried out manually. The common "linear list" format of writing biochemical reactions makes it difficult to intuit what is at the root of the inconsistent behaviour. Unfortunately, we have frequently observed that model builders do not correct their models past the abilities of automated tools, leaving many widely used models significantly inconsistent. RESULTS: We have developed the software ModelExplorer, which main purpose is to fill this gap by providing an intuitive and visual framework that allows the user to explore and correct inconsistencies in genome-scale metabolic models. The software will automatically visualize metabolic networks as graphs with distinct separation and delineation of cellular compartments. ModelExplorer highlights reactions and species that are unable to carry flux (blocked), with several different consistency checking modes available. Our software also allows the automatic identification of neighbours and production pathways of any species or reaction. Additionally, the user may focus on any chosen inconsistent part of the model on its own. This facilitates a rapid and visual identification of reactions and species responsible for model inconsistencies. Finally, ModelExplorer lets the user freely edit, add or delete model elements, allowing straight-forward correction of discovered issues. CONCLUSION: Overall, ModelExplorer is currently the fastest real-time metabolic network visualization program available. It implements several consistency checking algorithms, which in combination with its set of tracking tools, gives an efficient and systematic model-correction process. More... »

PAGES

56

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s12859-019-2615-x

DOI

http://dx.doi.org/10.1186/s12859-019-2615-x

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1111757538

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30691403


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/2008/0801", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Artificial Intelligence and Image Processing", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Information and Computing Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Algorithms", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Genome", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Metabolic Networks and Pathways", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Software", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Time Factors", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Norwegian University of Science and Technology", 
          "id": "https://www.grid.ac/institutes/grid.5947.f", 
          "name": [
            "Department of Biotechnology, NTNU - Norwegian University of Science and Technology, N-7491, Trondheim, Norway"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Martyushenko", 
        "givenName": "Nikolay", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Norwegian University of Science and Technology", 
          "id": "https://www.grid.ac/institutes/grid.5947.f", 
          "name": [
            "Department of Biotechnology, NTNU - Norwegian University of Science and Technology, N-7491, Trondheim, Norway", 
            "K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and General Practice, NTNU - Norwegian University of Science and Technology, N-7491, Trondheim, Norway"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Almaas", 
        "givenName": "Eivind", 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1186/1752-0509-7-129", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002097365", 
          "https://doi.org/10.1186/1752-0509-7-129"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1752-0509-7-74", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004574459", 
          "https://doi.org/10.1186/1752-0509-7-74"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nprot.2011.308", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006436824", 
          "https://doi.org/10.1038/nprot.2011.308"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/bts432", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010078228"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1752-0509-7-114", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010670521", 
          "https://doi.org/10.1186/1752-0509-7-114"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1371/journal.pcbi.1002980", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014516343"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1371/journal.pcbi.1004321", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016698970"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2105-8-212", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023395418", 
          "https://doi.org/10.1186/1471-2105-8-212"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2105-8-212", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023395418", 
          "https://doi.org/10.1186/1471-2105-8-212"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2105-8-139", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027470530", 
          "https://doi.org/10.1186/1471-2105-8-139"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2105-8-139", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027470530", 
          "https://doi.org/10.1186/1471-2105-8-139"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1371/journal.pcbi.1003424", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028438551"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/btg015", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037837492"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1042/bst0311472", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037844363"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1042/bst0311472", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037844363"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nrg3643", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038109672", 
          "https://doi.org/10.1038/nrg3643"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/btt758", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039955838"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1371/journal.pcbi.1002575", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041227169"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.copbio.2003.08.001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044054586"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.copbio.2003.08.001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044054586"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2105-11-489", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044763652", 
          "https://doi.org/10.1186/1471-2105-11-489"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nprot.2009.203", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045033347", 
          "https://doi.org/10.1038/nprot.2009.203"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nprot.2009.203", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045033347", 
          "https://doi.org/10.1038/nprot.2009.203"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1038/msb.2013.52", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045669961"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1038/msb.2013.52", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045669961"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.ymben.2012.09.005", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051569219"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1101/gr.1239303", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052744398"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.15252/msb.20156157", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053620327"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1147/rd.471.0057", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1063182692"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/btx588", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1091808348"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-1-4939-7528-0_6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1099629554", 
          "https://doi.org/10.1007/978-1-4939-7528-0_6"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2019-12", 
    "datePublishedReg": "2019-12-01", 
    "description": "BACKGROUND: Genome-scale metabolic network reconstructions are low level chemical representations of biological organisms. These models allow the system-level investigation of metabolic phenotypes using a variety of computational approaches. The link between a metabolic network model and an organisms' higher-level behaviour is usually found using a constraint-based analysis approach, such as FBA (Flux Balance Analysis). However, the process of model reconstruction rarely proceeds without error. Often, considerable parts of a model cannot carry flux under any condition. This is termed model inconsistency and is caused by faulty topology and/or stoichiometry of the underlying reconstructed network. While there exist several automated gap-filling tools that may solve some of the inconsistencies, much of the work still needs to be carried out manually. The common \"linear list\" format of writing biochemical reactions makes it difficult to intuit what is at the root of the inconsistent behaviour. Unfortunately, we have frequently observed that model builders do not correct their models past the abilities of automated tools, leaving many widely used models significantly inconsistent.\nRESULTS: We have developed the software ModelExplorer, which main purpose is to fill this gap by providing an intuitive and visual framework that allows the user to explore and correct inconsistencies in genome-scale metabolic models. The software will automatically visualize metabolic networks as graphs with distinct separation and delineation of cellular compartments. ModelExplorer highlights reactions and species that are unable to carry flux (blocked), with several different consistency checking modes available. Our software also allows the automatic identification of neighbours and production pathways of any species or reaction. Additionally, the user may focus on any chosen inconsistent part of the model on its own. This facilitates a rapid and visual identification of reactions and species responsible for model inconsistencies. Finally, ModelExplorer lets the user freely edit, add or delete model elements, allowing straight-forward correction of discovered issues.\nCONCLUSION: Overall, ModelExplorer is currently the fastest real-time metabolic network visualization program available. It implements several consistency checking algorithms, which in combination with its set of tracking tools, gives an efficient and systematic model-correction process.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1186/s12859-019-2615-x", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.4639967", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": [
      {
        "id": "sg:journal.1023786", 
        "issn": [
          "1471-2105"
        ], 
        "name": "BMC Bioinformatics", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "20"
      }
    ], 
    "name": "ModelExplorer - software for visual inspection and inconsistency correction of genome-scale metabolic reconstructions", 
    "pagination": "56", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "d211ff0e7854d72861e24d4a2bbd29527ef635264dafb5ef970290b5892650f8"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "30691403"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "100965194"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1186/s12859-019-2615-x"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1111757538"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1186/s12859-019-2615-x", 
      "https://app.dimensions.ai/details/publication/pub.1111757538"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T09:53", 
    "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/v90/0000000347_0000000347/records_89793_00000004.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://link.springer.com/10.1186%2Fs12859-019-2615-x"
  }
]
 

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.10.1186/s12859-019-2615-x'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1186/s12859-019-2615-x'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/s12859-019-2615-x'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/s12859-019-2615-x'


 

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

181 TRIPLES      21 PREDICATES      59 URIs      26 LITERALS      14 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1186/s12859-019-2615-x schema:about N302edec2dc434e2b9306fcb6da271df7
2 N8c55929dc81442b1b33fc9bcda4dce17
3 N9226f401aaf34ec4bb4e02f962bbbad6
4 Nae2328592401426ea3a45527ba4ddaa8
5 Naf0c396d5f11404f87377c1d6040f6d6
6 anzsrc-for:08
7 anzsrc-for:0801
8 schema:author N9d7a10c9300a4542b98c71f456688a1d
9 schema:citation sg:pub.10.1007/978-1-4939-7528-0_6
10 sg:pub.10.1038/nprot.2009.203
11 sg:pub.10.1038/nprot.2011.308
12 sg:pub.10.1038/nrg3643
13 sg:pub.10.1186/1471-2105-11-489
14 sg:pub.10.1186/1471-2105-8-139
15 sg:pub.10.1186/1471-2105-8-212
16 sg:pub.10.1186/1752-0509-7-114
17 sg:pub.10.1186/1752-0509-7-129
18 sg:pub.10.1186/1752-0509-7-74
19 https://doi.org/10.1016/j.copbio.2003.08.001
20 https://doi.org/10.1016/j.ymben.2012.09.005
21 https://doi.org/10.1038/msb.2013.52
22 https://doi.org/10.1042/bst0311472
23 https://doi.org/10.1093/bioinformatics/btg015
24 https://doi.org/10.1093/bioinformatics/bts432
25 https://doi.org/10.1093/bioinformatics/btt758
26 https://doi.org/10.1093/bioinformatics/btx588
27 https://doi.org/10.1101/gr.1239303
28 https://doi.org/10.1147/rd.471.0057
29 https://doi.org/10.1371/journal.pcbi.1002575
30 https://doi.org/10.1371/journal.pcbi.1002980
31 https://doi.org/10.1371/journal.pcbi.1003424
32 https://doi.org/10.1371/journal.pcbi.1004321
33 https://doi.org/10.15252/msb.20156157
34 schema:datePublished 2019-12
35 schema:datePublishedReg 2019-12-01
36 schema:description BACKGROUND: Genome-scale metabolic network reconstructions are low level chemical representations of biological organisms. These models allow the system-level investigation of metabolic phenotypes using a variety of computational approaches. The link between a metabolic network model and an organisms' higher-level behaviour is usually found using a constraint-based analysis approach, such as FBA (Flux Balance Analysis). However, the process of model reconstruction rarely proceeds without error. Often, considerable parts of a model cannot carry flux under any condition. This is termed model inconsistency and is caused by faulty topology and/or stoichiometry of the underlying reconstructed network. While there exist several automated gap-filling tools that may solve some of the inconsistencies, much of the work still needs to be carried out manually. The common "linear list" format of writing biochemical reactions makes it difficult to intuit what is at the root of the inconsistent behaviour. Unfortunately, we have frequently observed that model builders do not correct their models past the abilities of automated tools, leaving many widely used models significantly inconsistent. RESULTS: We have developed the software ModelExplorer, which main purpose is to fill this gap by providing an intuitive and visual framework that allows the user to explore and correct inconsistencies in genome-scale metabolic models. The software will automatically visualize metabolic networks as graphs with distinct separation and delineation of cellular compartments. ModelExplorer highlights reactions and species that are unable to carry flux (blocked), with several different consistency checking modes available. Our software also allows the automatic identification of neighbours and production pathways of any species or reaction. Additionally, the user may focus on any chosen inconsistent part of the model on its own. This facilitates a rapid and visual identification of reactions and species responsible for model inconsistencies. Finally, ModelExplorer lets the user freely edit, add or delete model elements, allowing straight-forward correction of discovered issues. CONCLUSION: Overall, ModelExplorer is currently the fastest real-time metabolic network visualization program available. It implements several consistency checking algorithms, which in combination with its set of tracking tools, gives an efficient and systematic model-correction process.
37 schema:genre research_article
38 schema:inLanguage en
39 schema:isAccessibleForFree true
40 schema:isPartOf N12cf49ae4bbc4e439bd04401794df93e
41 Ndb79e6e16cdc4a89b956d4574d1bb05a
42 sg:journal.1023786
43 schema:name ModelExplorer - software for visual inspection and inconsistency correction of genome-scale metabolic reconstructions
44 schema:pagination 56
45 schema:productId N211756b3e14947528576e37533ce5849
46 N94ea7d103ac249968e674f46a4721a53
47 Nf876257eaeef4b2aae2de6de88c81c3d
48 Nf87a52a996e74dc89d6e671ac4b7026f
49 Nf889525b5af049a480946d7fe47c93a7
50 schema:sameAs https://app.dimensions.ai/details/publication/pub.1111757538
51 https://doi.org/10.1186/s12859-019-2615-x
52 schema:sdDatePublished 2019-04-11T09:53
53 schema:sdLicense https://scigraph.springernature.com/explorer/license/
54 schema:sdPublisher N926f51781a984698af46360b2b633bbf
55 schema:url https://link.springer.com/10.1186%2Fs12859-019-2615-x
56 sgo:license sg:explorer/license/
57 sgo:sdDataset articles
58 rdf:type schema:ScholarlyArticle
59 N077e8f256fa84aa7a79a7b8675904d54 rdf:first N3afd1b92239b4deca8bfa50f48754f9a
60 rdf:rest rdf:nil
61 N12cf49ae4bbc4e439bd04401794df93e schema:issueNumber 1
62 rdf:type schema:PublicationIssue
63 N211756b3e14947528576e37533ce5849 schema:name pubmed_id
64 schema:value 30691403
65 rdf:type schema:PropertyValue
66 N302edec2dc434e2b9306fcb6da271df7 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
67 schema:name Algorithms
68 rdf:type schema:DefinedTerm
69 N3afd1b92239b4deca8bfa50f48754f9a schema:affiliation https://www.grid.ac/institutes/grid.5947.f
70 schema:familyName Almaas
71 schema:givenName Eivind
72 rdf:type schema:Person
73 N8c55929dc81442b1b33fc9bcda4dce17 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
74 schema:name Software
75 rdf:type schema:DefinedTerm
76 N9226f401aaf34ec4bb4e02f962bbbad6 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
77 schema:name Metabolic Networks and Pathways
78 rdf:type schema:DefinedTerm
79 N926f51781a984698af46360b2b633bbf schema:name Springer Nature - SN SciGraph project
80 rdf:type schema:Organization
81 N94ea7d103ac249968e674f46a4721a53 schema:name doi
82 schema:value 10.1186/s12859-019-2615-x
83 rdf:type schema:PropertyValue
84 N9d7a10c9300a4542b98c71f456688a1d rdf:first Nb104c5c507a5460b9a1f2bfe0096e1a0
85 rdf:rest N077e8f256fa84aa7a79a7b8675904d54
86 Nae2328592401426ea3a45527ba4ddaa8 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
87 schema:name Time Factors
88 rdf:type schema:DefinedTerm
89 Naf0c396d5f11404f87377c1d6040f6d6 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
90 schema:name Genome
91 rdf:type schema:DefinedTerm
92 Nb104c5c507a5460b9a1f2bfe0096e1a0 schema:affiliation https://www.grid.ac/institutes/grid.5947.f
93 schema:familyName Martyushenko
94 schema:givenName Nikolay
95 rdf:type schema:Person
96 Ndb79e6e16cdc4a89b956d4574d1bb05a schema:volumeNumber 20
97 rdf:type schema:PublicationVolume
98 Nf876257eaeef4b2aae2de6de88c81c3d schema:name nlm_unique_id
99 schema:value 100965194
100 rdf:type schema:PropertyValue
101 Nf87a52a996e74dc89d6e671ac4b7026f schema:name dimensions_id
102 schema:value pub.1111757538
103 rdf:type schema:PropertyValue
104 Nf889525b5af049a480946d7fe47c93a7 schema:name readcube_id
105 schema:value d211ff0e7854d72861e24d4a2bbd29527ef635264dafb5ef970290b5892650f8
106 rdf:type schema:PropertyValue
107 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
108 schema:name Information and Computing Sciences
109 rdf:type schema:DefinedTerm
110 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
111 schema:name Artificial Intelligence and Image Processing
112 rdf:type schema:DefinedTerm
113 sg:grant.4639967 http://pending.schema.org/fundedItem sg:pub.10.1186/s12859-019-2615-x
114 rdf:type schema:MonetaryGrant
115 sg:journal.1023786 schema:issn 1471-2105
116 schema:name BMC Bioinformatics
117 rdf:type schema:Periodical
118 sg:pub.10.1007/978-1-4939-7528-0_6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1099629554
119 https://doi.org/10.1007/978-1-4939-7528-0_6
120 rdf:type schema:CreativeWork
121 sg:pub.10.1038/nprot.2009.203 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045033347
122 https://doi.org/10.1038/nprot.2009.203
123 rdf:type schema:CreativeWork
124 sg:pub.10.1038/nprot.2011.308 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006436824
125 https://doi.org/10.1038/nprot.2011.308
126 rdf:type schema:CreativeWork
127 sg:pub.10.1038/nrg3643 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038109672
128 https://doi.org/10.1038/nrg3643
129 rdf:type schema:CreativeWork
130 sg:pub.10.1186/1471-2105-11-489 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044763652
131 https://doi.org/10.1186/1471-2105-11-489
132 rdf:type schema:CreativeWork
133 sg:pub.10.1186/1471-2105-8-139 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027470530
134 https://doi.org/10.1186/1471-2105-8-139
135 rdf:type schema:CreativeWork
136 sg:pub.10.1186/1471-2105-8-212 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023395418
137 https://doi.org/10.1186/1471-2105-8-212
138 rdf:type schema:CreativeWork
139 sg:pub.10.1186/1752-0509-7-114 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010670521
140 https://doi.org/10.1186/1752-0509-7-114
141 rdf:type schema:CreativeWork
142 sg:pub.10.1186/1752-0509-7-129 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002097365
143 https://doi.org/10.1186/1752-0509-7-129
144 rdf:type schema:CreativeWork
145 sg:pub.10.1186/1752-0509-7-74 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004574459
146 https://doi.org/10.1186/1752-0509-7-74
147 rdf:type schema:CreativeWork
148 https://doi.org/10.1016/j.copbio.2003.08.001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044054586
149 rdf:type schema:CreativeWork
150 https://doi.org/10.1016/j.ymben.2012.09.005 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051569219
151 rdf:type schema:CreativeWork
152 https://doi.org/10.1038/msb.2013.52 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045669961
153 rdf:type schema:CreativeWork
154 https://doi.org/10.1042/bst0311472 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037844363
155 rdf:type schema:CreativeWork
156 https://doi.org/10.1093/bioinformatics/btg015 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037837492
157 rdf:type schema:CreativeWork
158 https://doi.org/10.1093/bioinformatics/bts432 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010078228
159 rdf:type schema:CreativeWork
160 https://doi.org/10.1093/bioinformatics/btt758 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039955838
161 rdf:type schema:CreativeWork
162 https://doi.org/10.1093/bioinformatics/btx588 schema:sameAs https://app.dimensions.ai/details/publication/pub.1091808348
163 rdf:type schema:CreativeWork
164 https://doi.org/10.1101/gr.1239303 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052744398
165 rdf:type schema:CreativeWork
166 https://doi.org/10.1147/rd.471.0057 schema:sameAs https://app.dimensions.ai/details/publication/pub.1063182692
167 rdf:type schema:CreativeWork
168 https://doi.org/10.1371/journal.pcbi.1002575 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041227169
169 rdf:type schema:CreativeWork
170 https://doi.org/10.1371/journal.pcbi.1002980 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014516343
171 rdf:type schema:CreativeWork
172 https://doi.org/10.1371/journal.pcbi.1003424 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028438551
173 rdf:type schema:CreativeWork
174 https://doi.org/10.1371/journal.pcbi.1004321 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016698970
175 rdf:type schema:CreativeWork
176 https://doi.org/10.15252/msb.20156157 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053620327
177 rdf:type schema:CreativeWork
178 https://www.grid.ac/institutes/grid.5947.f schema:alternateName Norwegian University of Science and Technology
179 schema:name Department of Biotechnology, NTNU - Norwegian University of Science and Technology, N-7491, Trondheim, Norway
180 K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and General Practice, NTNU - Norwegian University of Science and Technology, N-7491, Trondheim, Norway
181 rdf:type schema:Organization
 




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


...