Predicting bacterial community assemblages using an artificial neural network approach View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2012-06

AUTHORS

Peter E Larsen, Dawn Field, Jack A Gilbert

ABSTRACT

Understanding the interactions between the Earth's microbiome and the physical, chemical and biological environment is a fundamental goal of microbial ecology. We describe a bioclimatic modeling approach that leverages artificial neural networks to predict microbial community structure as a function of environmental parameters and microbial interactions. This method was better at predicting observed community structure than were any of several single-species models that do not incorporate biotic interactions. The model was used to interpolate and extrapolate community structure over time with an average Bray-Curtis similarity of 89.7. Additionally, community structure was extrapolated geographically to create the first microbial map derived from single-point observations. This method can be generalized to the many microbial ecosystems for which detailed taxonomic data are currently being generated, providing an observation-based modeling technique for predicting microbial taxonomic structure in ecological studies. More... »

PAGES

621

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/nmeth.1975

DOI

http://dx.doi.org/10.1038/nmeth.1975

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1027354204

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/22504588


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/2008/0605", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Microbiology", 
        "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"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Actinomycetales", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Bacteria", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Deltaproteobacteria", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Ecology", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Ecosystem", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Gammaproteobacteria", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Metagenome", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Microbial Interactions", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Models, Biological", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Neural Networks (Computer)", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Seawater", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "name": [
            "Argonne National Laboratory Biosciences Division, Argonne, Illinois, USA."
          ], 
          "type": "Organization"
        }, 
        "familyName": "Larsen", 
        "givenName": "Peter E", 
        "id": "sg:person.01073775742.05", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01073775742.05"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Centre for Ecology and Hydrology", 
          "id": "https://www.grid.ac/institutes/grid.494924.6", 
          "name": [
            "Centre for Ecology and Hydrology, Natural Environment Research Council (NERC), Wallingford, UK."
          ], 
          "type": "Organization"
        }, 
        "familyName": "Field", 
        "givenName": "Dawn", 
        "id": "sg:person.01223211445.99", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01223211445.99"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Chicago", 
          "id": "https://www.grid.ac/institutes/grid.170205.1", 
          "name": [
            "Argonne National Laboratory Biosciences Division, Argonne, Illinois, USA.", 
            "Department of Ecology and Evolution, University of Chicago, Chicago, Illinois, USA."
          ], 
          "type": "Organization"
        }, 
        "familyName": "Gilbert", 
        "givenName": "Jack A", 
        "id": "sg:person.0727626545.37", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0727626545.37"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1016/s0006-3207(00)00139-7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000751199"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.0906-7590.2005.04002.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000837702"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0065-2881(04)47001-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002742928"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1146/annurev-marine-120709-142848", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004914228"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1371/journal.pcbi.0020161", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005584078"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nbt.1823", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005906569", 
          "https://doi.org/10.1038/nbt.1823"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.ecolmodel.2006.07.005", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006957392"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/ismej.2011.24", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1007074967", 
          "https://doi.org/10.1038/ismej.2011.24"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1073/pnas.0605127103", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008547462"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1890/0012-9658(2001)082[2560:cibtsi]2.0.co;2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009759659"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/481145a", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010406789", 
          "https://doi.org/10.1038/481145a"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1371/journal.pone.0021555", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1012774554"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2307/3236568", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013152718"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2307/3236568", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013152718"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2307/3236568", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013152718"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/ismej.2011.162", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013974628", 
          "https://doi.org/10.1038/ismej.2011.162"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1371/journal.pone.0010209", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013976489"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1462-2920.2010.02362.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019508335"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1073/pnas.1000080107", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019627885"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1461-0248.2005.00792.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020875241"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1461-0248.2005.00792.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020875241"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1073/pnas.1101405108", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021593822"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/2042-5783-1-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030642249", 
          "https://doi.org/10.1186/2042-5783-1-4"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0304-3800(02)00194-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034430995"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0304-3800(02)00194-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034430995"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.4269/ajtmh.2011.11-0181", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036371870"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1146/annurev.micro.030608.101423", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036744525"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1196/annals.1439.002", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037372457"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1146/annurev.ecolsys.110308.120159", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038408735"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/ismej.2011.119", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039602901", 
          "https://doi.org/10.1038/ismej.2011.119"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0304-3800(02)00056-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044050266"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1890/07-0298.1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044084626"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/plankt/fbp128", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045790311"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/plankt/fbp128", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045790311"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/ismej.2011.107", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049133492", 
          "https://doi.org/10.1038/ismej.2011.107"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10531-009-9774-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051114094", 
          "https://doi.org/10.1007/s10531-009-9774-4"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10531-009-9774-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051114094", 
          "https://doi.org/10.1007/s10531-009-9774-4"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1086/600087", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1058805769"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1126/science.1165893", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062459011"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2012-06", 
    "datePublishedReg": "2012-06-01", 
    "description": "Understanding the interactions between the Earth's microbiome and the physical, chemical and biological environment is a fundamental goal of microbial ecology. We describe a bioclimatic modeling approach that leverages artificial neural networks to predict microbial community structure as a function of environmental parameters and microbial interactions. This method was better at predicting observed community structure than were any of several single-species models that do not incorporate biotic interactions. The model was used to interpolate and extrapolate community structure over time with an average Bray-Curtis similarity of 89.7. Additionally, community structure was extrapolated geographically to create the first microbial map derived from single-point observations. This method can be generalized to the many microbial ecosystems for which detailed taxonomic data are currently being generated, providing an observation-based modeling technique for predicting microbial taxonomic structure in ecological studies.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1038/nmeth.1975", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.2777826", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": [
      {
        "id": "sg:journal.1033763", 
        "issn": [
          "1548-7091", 
          "1548-7105"
        ], 
        "name": "Nature Methods", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "6", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "9"
      }
    ], 
    "name": "Predicting bacterial community assemblages using an artificial neural network approach", 
    "pagination": "621", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "824e74022fafd9fc077a4830e61315f72510506ec29501e8a011a149f6c6910b"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "22504588"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "101215604"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1038/nmeth.1975"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1027354204"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1038/nmeth.1975", 
      "https://app.dimensions.ai/details/publication/pub.1027354204"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-10T13:57", 
    "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/v90/0000000001_0000000264/records_8660_00000435.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://www.nature.com/articles/nmeth.1975"
  }
]
 

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.10.1038/nmeth.1975'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1038/nmeth.1975'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1038/nmeth.1975'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1038/nmeth.1975'


 

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

242 TRIPLES      21 PREDICATES      73 URIs      32 LITERALS      20 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1038/nmeth.1975 schema:about N0005985b6dde46a581a39b8e6bbed22e
2 N0d543ef4f22d4071949a842f41b171ff
3 N476518850fb94426a553bfc922ab44bc
4 N590065d749044adca333d117abf3080f
5 N5b8e96ea4e534d9b98cdefa3591dd441
6 N9e909e5a80314f4ca18b972940d19ab1
7 Nd0e4ee1185a0490caa61c695174c5b92
8 Nd6894af148474082aa5029234ef0aaaf
9 Ne8c6bf296d334c95bd93fbfb1acb246c
10 Nec846393d1a7493a83b1346938d02481
11 Nf8cd95bf83da4ed793ef6b0414dccb75
12 anzsrc-for:06
13 anzsrc-for:0605
14 schema:author Na0ef87a01d154817ba386931cd3817c9
15 schema:citation sg:pub.10.1007/s10531-009-9774-4
16 sg:pub.10.1038/481145a
17 sg:pub.10.1038/ismej.2011.107
18 sg:pub.10.1038/ismej.2011.119
19 sg:pub.10.1038/ismej.2011.162
20 sg:pub.10.1038/ismej.2011.24
21 sg:pub.10.1038/nbt.1823
22 sg:pub.10.1186/2042-5783-1-4
23 https://doi.org/10.1016/j.ecolmodel.2006.07.005
24 https://doi.org/10.1016/s0006-3207(00)00139-7
25 https://doi.org/10.1016/s0065-2881(04)47001-1
26 https://doi.org/10.1016/s0304-3800(02)00056-x
27 https://doi.org/10.1016/s0304-3800(02)00194-1
28 https://doi.org/10.1073/pnas.0605127103
29 https://doi.org/10.1073/pnas.1000080107
30 https://doi.org/10.1073/pnas.1101405108
31 https://doi.org/10.1086/600087
32 https://doi.org/10.1093/plankt/fbp128
33 https://doi.org/10.1111/j.0906-7590.2005.04002.x
34 https://doi.org/10.1111/j.1461-0248.2005.00792.x
35 https://doi.org/10.1111/j.1462-2920.2010.02362.x
36 https://doi.org/10.1126/science.1165893
37 https://doi.org/10.1146/annurev-marine-120709-142848
38 https://doi.org/10.1146/annurev.ecolsys.110308.120159
39 https://doi.org/10.1146/annurev.micro.030608.101423
40 https://doi.org/10.1196/annals.1439.002
41 https://doi.org/10.1371/journal.pcbi.0020161
42 https://doi.org/10.1371/journal.pone.0010209
43 https://doi.org/10.1371/journal.pone.0021555
44 https://doi.org/10.1890/0012-9658(2001)082[2560:cibtsi]2.0.co;2
45 https://doi.org/10.1890/07-0298.1
46 https://doi.org/10.2307/3236568
47 https://doi.org/10.4269/ajtmh.2011.11-0181
48 schema:datePublished 2012-06
49 schema:datePublishedReg 2012-06-01
50 schema:description Understanding the interactions between the Earth's microbiome and the physical, chemical and biological environment is a fundamental goal of microbial ecology. We describe a bioclimatic modeling approach that leverages artificial neural networks to predict microbial community structure as a function of environmental parameters and microbial interactions. This method was better at predicting observed community structure than were any of several single-species models that do not incorporate biotic interactions. The model was used to interpolate and extrapolate community structure over time with an average Bray-Curtis similarity of 89.7. Additionally, community structure was extrapolated geographically to create the first microbial map derived from single-point observations. This method can be generalized to the many microbial ecosystems for which detailed taxonomic data are currently being generated, providing an observation-based modeling technique for predicting microbial taxonomic structure in ecological studies.
51 schema:genre research_article
52 schema:inLanguage en
53 schema:isAccessibleForFree false
54 schema:isPartOf N10b3ae7b855a440bbe66918d4eba00e7
55 N3825aa54bf724fdf92845b546f6ccbb5
56 sg:journal.1033763
57 schema:name Predicting bacterial community assemblages using an artificial neural network approach
58 schema:pagination 621
59 schema:productId N1ed15e641f244be7b5ea6953568e46a4
60 N47fc9b616fb64a508a9a856608f08bdf
61 N5b128f24945b4ee88dd49cfc81bee47a
62 N65db38fcce1644e28a76dec9cae4afd7
63 N80a15538beda44d6bfa80a1de79b624c
64 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027354204
65 https://doi.org/10.1038/nmeth.1975
66 schema:sdDatePublished 2019-04-10T13:57
67 schema:sdLicense https://scigraph.springernature.com/explorer/license/
68 schema:sdPublisher Nfe51909ff0e148949f1280b0d87a81df
69 schema:url https://www.nature.com/articles/nmeth.1975
70 sgo:license sg:explorer/license/
71 sgo:sdDataset articles
72 rdf:type schema:ScholarlyArticle
73 N0005985b6dde46a581a39b8e6bbed22e schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
74 schema:name Neural Networks (Computer)
75 rdf:type schema:DefinedTerm
76 N0d543ef4f22d4071949a842f41b171ff schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
77 schema:name Deltaproteobacteria
78 rdf:type schema:DefinedTerm
79 N10b3ae7b855a440bbe66918d4eba00e7 schema:volumeNumber 9
80 rdf:type schema:PublicationVolume
81 N1ed15e641f244be7b5ea6953568e46a4 schema:name readcube_id
82 schema:value 824e74022fafd9fc077a4830e61315f72510506ec29501e8a011a149f6c6910b
83 rdf:type schema:PropertyValue
84 N3825aa54bf724fdf92845b546f6ccbb5 schema:issueNumber 6
85 rdf:type schema:PublicationIssue
86 N476518850fb94426a553bfc922ab44bc schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
87 schema:name Ecosystem
88 rdf:type schema:DefinedTerm
89 N47fc9b616fb64a508a9a856608f08bdf schema:name dimensions_id
90 schema:value pub.1027354204
91 rdf:type schema:PropertyValue
92 N590065d749044adca333d117abf3080f schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
93 schema:name Microbial Interactions
94 rdf:type schema:DefinedTerm
95 N59fcb9ab574c48828bc73bf01c2fabf0 rdf:first sg:person.01223211445.99
96 rdf:rest N5b103a0e5fd04efc89fc10621bd4a783
97 N5b103a0e5fd04efc89fc10621bd4a783 rdf:first sg:person.0727626545.37
98 rdf:rest rdf:nil
99 N5b128f24945b4ee88dd49cfc81bee47a schema:name nlm_unique_id
100 schema:value 101215604
101 rdf:type schema:PropertyValue
102 N5b8e96ea4e534d9b98cdefa3591dd441 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
103 schema:name Seawater
104 rdf:type schema:DefinedTerm
105 N65db38fcce1644e28a76dec9cae4afd7 schema:name pubmed_id
106 schema:value 22504588
107 rdf:type schema:PropertyValue
108 N80a15538beda44d6bfa80a1de79b624c schema:name doi
109 schema:value 10.1038/nmeth.1975
110 rdf:type schema:PropertyValue
111 N9e909e5a80314f4ca18b972940d19ab1 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
112 schema:name Actinomycetales
113 rdf:type schema:DefinedTerm
114 Na0ef87a01d154817ba386931cd3817c9 rdf:first sg:person.01073775742.05
115 rdf:rest N59fcb9ab574c48828bc73bf01c2fabf0
116 Nd0e4ee1185a0490caa61c695174c5b92 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
117 schema:name Models, Biological
118 rdf:type schema:DefinedTerm
119 Nd6894af148474082aa5029234ef0aaaf schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
120 schema:name Ecology
121 rdf:type schema:DefinedTerm
122 Ndb6131f470db49508a6e90770edda7f8 schema:name Argonne National Laboratory Biosciences Division, Argonne, Illinois, USA.
123 rdf:type schema:Organization
124 Ne8c6bf296d334c95bd93fbfb1acb246c schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
125 schema:name Metagenome
126 rdf:type schema:DefinedTerm
127 Nec846393d1a7493a83b1346938d02481 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
128 schema:name Gammaproteobacteria
129 rdf:type schema:DefinedTerm
130 Nf8cd95bf83da4ed793ef6b0414dccb75 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
131 schema:name Bacteria
132 rdf:type schema:DefinedTerm
133 Nfe51909ff0e148949f1280b0d87a81df schema:name Springer Nature - SN SciGraph project
134 rdf:type schema:Organization
135 anzsrc-for:06 schema:inDefinedTermSet anzsrc-for:
136 schema:name Biological Sciences
137 rdf:type schema:DefinedTerm
138 anzsrc-for:0605 schema:inDefinedTermSet anzsrc-for:
139 schema:name Microbiology
140 rdf:type schema:DefinedTerm
141 sg:grant.2777826 http://pending.schema.org/fundedItem sg:pub.10.1038/nmeth.1975
142 rdf:type schema:MonetaryGrant
143 sg:journal.1033763 schema:issn 1548-7091
144 1548-7105
145 schema:name Nature Methods
146 rdf:type schema:Periodical
147 sg:person.01073775742.05 schema:affiliation Ndb6131f470db49508a6e90770edda7f8
148 schema:familyName Larsen
149 schema:givenName Peter E
150 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01073775742.05
151 rdf:type schema:Person
152 sg:person.01223211445.99 schema:affiliation https://www.grid.ac/institutes/grid.494924.6
153 schema:familyName Field
154 schema:givenName Dawn
155 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01223211445.99
156 rdf:type schema:Person
157 sg:person.0727626545.37 schema:affiliation https://www.grid.ac/institutes/grid.170205.1
158 schema:familyName Gilbert
159 schema:givenName Jack A
160 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0727626545.37
161 rdf:type schema:Person
162 sg:pub.10.1007/s10531-009-9774-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051114094
163 https://doi.org/10.1007/s10531-009-9774-4
164 rdf:type schema:CreativeWork
165 sg:pub.10.1038/481145a schema:sameAs https://app.dimensions.ai/details/publication/pub.1010406789
166 https://doi.org/10.1038/481145a
167 rdf:type schema:CreativeWork
168 sg:pub.10.1038/ismej.2011.107 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049133492
169 https://doi.org/10.1038/ismej.2011.107
170 rdf:type schema:CreativeWork
171 sg:pub.10.1038/ismej.2011.119 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039602901
172 https://doi.org/10.1038/ismej.2011.119
173 rdf:type schema:CreativeWork
174 sg:pub.10.1038/ismej.2011.162 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013974628
175 https://doi.org/10.1038/ismej.2011.162
176 rdf:type schema:CreativeWork
177 sg:pub.10.1038/ismej.2011.24 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007074967
178 https://doi.org/10.1038/ismej.2011.24
179 rdf:type schema:CreativeWork
180 sg:pub.10.1038/nbt.1823 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005906569
181 https://doi.org/10.1038/nbt.1823
182 rdf:type schema:CreativeWork
183 sg:pub.10.1186/2042-5783-1-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030642249
184 https://doi.org/10.1186/2042-5783-1-4
185 rdf:type schema:CreativeWork
186 https://doi.org/10.1016/j.ecolmodel.2006.07.005 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006957392
187 rdf:type schema:CreativeWork
188 https://doi.org/10.1016/s0006-3207(00)00139-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000751199
189 rdf:type schema:CreativeWork
190 https://doi.org/10.1016/s0065-2881(04)47001-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002742928
191 rdf:type schema:CreativeWork
192 https://doi.org/10.1016/s0304-3800(02)00056-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1044050266
193 rdf:type schema:CreativeWork
194 https://doi.org/10.1016/s0304-3800(02)00194-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034430995
195 rdf:type schema:CreativeWork
196 https://doi.org/10.1073/pnas.0605127103 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008547462
197 rdf:type schema:CreativeWork
198 https://doi.org/10.1073/pnas.1000080107 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019627885
199 rdf:type schema:CreativeWork
200 https://doi.org/10.1073/pnas.1101405108 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021593822
201 rdf:type schema:CreativeWork
202 https://doi.org/10.1086/600087 schema:sameAs https://app.dimensions.ai/details/publication/pub.1058805769
203 rdf:type schema:CreativeWork
204 https://doi.org/10.1093/plankt/fbp128 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045790311
205 rdf:type schema:CreativeWork
206 https://doi.org/10.1111/j.0906-7590.2005.04002.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1000837702
207 rdf:type schema:CreativeWork
208 https://doi.org/10.1111/j.1461-0248.2005.00792.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1020875241
209 rdf:type schema:CreativeWork
210 https://doi.org/10.1111/j.1462-2920.2010.02362.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1019508335
211 rdf:type schema:CreativeWork
212 https://doi.org/10.1126/science.1165893 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062459011
213 rdf:type schema:CreativeWork
214 https://doi.org/10.1146/annurev-marine-120709-142848 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004914228
215 rdf:type schema:CreativeWork
216 https://doi.org/10.1146/annurev.ecolsys.110308.120159 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038408735
217 rdf:type schema:CreativeWork
218 https://doi.org/10.1146/annurev.micro.030608.101423 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036744525
219 rdf:type schema:CreativeWork
220 https://doi.org/10.1196/annals.1439.002 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037372457
221 rdf:type schema:CreativeWork
222 https://doi.org/10.1371/journal.pcbi.0020161 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005584078
223 rdf:type schema:CreativeWork
224 https://doi.org/10.1371/journal.pone.0010209 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013976489
225 rdf:type schema:CreativeWork
226 https://doi.org/10.1371/journal.pone.0021555 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012774554
227 rdf:type schema:CreativeWork
228 https://doi.org/10.1890/0012-9658(2001)082[2560:cibtsi]2.0.co;2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009759659
229 rdf:type schema:CreativeWork
230 https://doi.org/10.1890/07-0298.1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044084626
231 rdf:type schema:CreativeWork
232 https://doi.org/10.2307/3236568 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013152718
233 rdf:type schema:CreativeWork
234 https://doi.org/10.4269/ajtmh.2011.11-0181 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036371870
235 rdf:type schema:CreativeWork
236 https://www.grid.ac/institutes/grid.170205.1 schema:alternateName University of Chicago
237 schema:name Argonne National Laboratory Biosciences Division, Argonne, Illinois, USA.
238 Department of Ecology and Evolution, University of Chicago, Chicago, Illinois, USA.
239 rdf:type schema:Organization
240 https://www.grid.ac/institutes/grid.494924.6 schema:alternateName Centre for Ecology and Hydrology
241 schema:name Centre for Ecology and Hydrology, Natural Environment Research Council (NERC), Wallingford, UK.
242 rdf:type schema:Organization
 




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


...