Conditional variable importance for random forests View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2008-12

AUTHORS

Carolin Strobl, Anne-Laure Boulesteix, Thomas Kneib, Thomas Augustin, Achim Zeileis

ABSTRACT

BACKGROUND: Random forests are becoming increasingly popular in many scientific fields because they can cope with "small n large p" problems, complex interactions and even highly correlated predictor variables. Their variable importance measures have recently been suggested as screening tools for, e.g., gene expression studies. However, these variable importance measures show a bias towards correlated predictor variables. RESULTS: We identify two mechanisms responsible for this finding: (i) A preference for the selection of correlated predictors in the tree building process and (ii) an additional advantage for correlated predictor variables induced by the unconditional permutation scheme that is employed in the computation of the variable importance measure. Based on these considerations we develop a new, conditional permutation scheme for the computation of the variable importance measure. CONCLUSION: The resulting conditional variable importance reflects the true impact of each predictor variable more reliably than the original marginal approach. More... »

PAGES

307

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/1471-2105-9-307

DOI

http://dx.doi.org/10.1186/1471-2105-9-307

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1042870683

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/18620558


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": "Amino Acid Sequence", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Binding Sites", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Biometry", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Computational Biology", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Decision Trees", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Factor Analysis, Statistical", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Major Histocompatibility Complex", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Regression Analysis", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Research Design", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Statistics, Nonparametric", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Ludwig Maximilian University of Munich", 
          "id": "https://www.grid.ac/institutes/grid.5252.0", 
          "name": [
            "Department of Statistics, Ludwig-Maximilians-Universit\u00e4t Munchen, Ludwigstra\u00dfe 33, D-80539, M\u00fcnchen, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Strobl", 
        "givenName": "Carolin", 
        "id": "sg:person.01163740613.59", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01163740613.59"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Sylvia Lawry Centre for Multiple Sclerosis Research", 
          "id": "https://www.grid.ac/institutes/grid.438311.c", 
          "name": [
            "Sylvia Lawry Centre for Multiple Sclerosis Research, Hohenlindener Stra\u00dfe 1, D-81677, M\u00fcnchen, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Boulesteix", 
        "givenName": "Anne-Laure", 
        "id": "sg:person.01342100161.77", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01342100161.77"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Ludwig Maximilian University of Munich", 
          "id": "https://www.grid.ac/institutes/grid.5252.0", 
          "name": [
            "Department of Statistics, Ludwig-Maximilians-Universit\u00e4t Munchen, Ludwigstra\u00dfe 33, D-80539, M\u00fcnchen, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kneib", 
        "givenName": "Thomas", 
        "id": "sg:person.01272020411.15", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01272020411.15"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Ludwig Maximilian University of Munich", 
          "id": "https://www.grid.ac/institutes/grid.5252.0", 
          "name": [
            "Department of Statistics, Ludwig-Maximilians-Universit\u00e4t Munchen, Ludwigstra\u00dfe 33, D-80539, M\u00fcnchen, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Augustin", 
        "givenName": "Thomas", 
        "id": "sg:person.01346302413.76", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01346302413.76"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Vienna University of Economics and Business", 
          "id": "https://www.grid.ac/institutes/grid.15788.33", 
          "name": [
            "Department of Statistics and Mathematics, Wirtschaftsuniversit\u00e4t Wien, Augasse 2 \u2013 6, A-1090, Wien, Austria"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Zeileis", 
        "givenName": "Achim", 
        "id": "sg:person.0622676313.19", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0622676313.19"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/bf00058655", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002929950", 
          "https://doi.org/10.1007/bf00058655"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2156-5-32", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003496686", 
          "https://doi.org/10.1186/1471-2156-5-32"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2105-7-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004156594", 
          "https://doi.org/10.1186/1471-2105-7-3"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2105-7-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004156594", 
          "https://doi.org/10.1186/1471-2105-7-3"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/prot.20865", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004797870"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/prot.20865", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004797870"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/gepi.20041", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1007867684"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0893-6080(01)00127-7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011249939"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/ci034160g", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013785151"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/ci034160g", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013785151"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1023/a:1007515423169", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017116781", 
          "https://doi.org/10.1023/a:1007515423169"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2105-8-25", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019863657", 
          "https://doi.org/10.1186/1471-2105-8-25"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2105-6-205", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021536997", 
          "https://doi.org/10.1186/1471-2105-6-205"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1152/physiolgenomics.00167.2007", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023579308"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1214/aos/1024691079", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024161650"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1023/a:1010933404324", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024739340", 
          "https://doi.org/10.1023/a:1010933404324"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1353/pbm.2005.0045", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025060069"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2202/1557-4679.1008", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032915999"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1214/ss/1009213726", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037292812"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0022-5347(01)63150-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038399190"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/pl00006412", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039309590", 
          "https://doi.org/10.1007/pl00006412"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1023/a:1007607513941", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041829946", 
          "https://doi.org/10.1023/a:1007607513941"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2105-8-328", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046518891", 
          "https://doi.org/10.1186/1471-2105-8-328"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.0006-341x.2001.00632.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046804254"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.csda.2006.12.030", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046845141"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.csda.2007.08.015", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049823578"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1097/00005650-199710001-00011", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1060223456"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1097/00005650-199710001-00011", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1060223456"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1097/00005650-199710001-00011", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1060223456"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1198/016214505000001230", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064198454"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1198/106186004x11417", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064199433"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1198/106186006x133933", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064199533"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1214/aos/1031689014", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064406186"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2008-12", 
    "datePublishedReg": "2008-12-01", 
    "description": "BACKGROUND: Random forests are becoming increasingly popular in many scientific fields because they can cope with \"small n large p\" problems, complex interactions and even highly correlated predictor variables. Their variable importance measures have recently been suggested as screening tools for, e.g., gene expression studies. However, these variable importance measures show a bias towards correlated predictor variables.\nRESULTS: We identify two mechanisms responsible for this finding: (i) A preference for the selection of correlated predictors in the tree building process and (ii) an additional advantage for correlated predictor variables induced by the unconditional permutation scheme that is employed in the computation of the variable importance measure. Based on these considerations we develop a new, conditional permutation scheme for the computation of the variable importance measure.\nCONCLUSION: The resulting conditional variable importance reflects the true impact of each predictor variable more reliably than the original marginal approach.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1186/1471-2105-9-307", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isPartOf": [
      {
        "id": "sg:journal.1023786", 
        "issn": [
          "1471-2105"
        ], 
        "name": "BMC Bioinformatics", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "9"
      }
    ], 
    "name": "Conditional variable importance for random forests", 
    "pagination": "307", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "cc35e6badcf19ff38cbeb478802a451d504bab3face709b75b637f180fd70f7e"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "18620558"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "100965194"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1186/1471-2105-9-307"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1042870683"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1186/1471-2105-9-307", 
      "https://app.dimensions.ai/details/publication/pub.1042870683"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-10T16:41", 
    "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_8669_00000507.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1186%2F1471-2105-9-307"
  }
]
 

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.10.1186/1471-2105-9-307'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1186/1471-2105-9-307'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/1471-2105-9-307'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/1471-2105-9-307'


 

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

236 TRIPLES      21 PREDICATES      67 URIs      31 LITERALS      19 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1186/1471-2105-9-307 schema:about N1defcc022e0e4132a92e275f22defc46
2 N4e93c5ad2d144af1bdf9ef40c4861afa
3 N758c6b9e70fa4d6ba43dfe5344cbffdd
4 N8e346d274d354570ab954b15616d9f38
5 N990f839d7a1a4f60be87d246ac8e3395
6 Nbd70c7077d4345bf97fa8f532d748dec
7 Nd29eafe97003428eb0ac390e2e682861
8 Nd5adfacb7df042e0a71a61a333d4db37
9 Nd78ac4d8654743e8a803912d0dbb9583
10 Nfa3522f3f93941a39c2e6e8a46cef78f
11 anzsrc-for:08
12 anzsrc-for:0801
13 schema:author Ndfd60f2cc4f74affb173f36345483f1f
14 schema:citation sg:pub.10.1007/bf00058655
15 sg:pub.10.1007/pl00006412
16 sg:pub.10.1023/a:1007515423169
17 sg:pub.10.1023/a:1007607513941
18 sg:pub.10.1023/a:1010933404324
19 sg:pub.10.1186/1471-2105-6-205
20 sg:pub.10.1186/1471-2105-7-3
21 sg:pub.10.1186/1471-2105-8-25
22 sg:pub.10.1186/1471-2105-8-328
23 sg:pub.10.1186/1471-2156-5-32
24 https://doi.org/10.1002/gepi.20041
25 https://doi.org/10.1002/prot.20865
26 https://doi.org/10.1016/j.csda.2006.12.030
27 https://doi.org/10.1016/j.csda.2007.08.015
28 https://doi.org/10.1016/s0022-5347(01)63150-1
29 https://doi.org/10.1016/s0893-6080(01)00127-7
30 https://doi.org/10.1021/ci034160g
31 https://doi.org/10.1097/00005650-199710001-00011
32 https://doi.org/10.1111/j.0006-341x.2001.00632.x
33 https://doi.org/10.1152/physiolgenomics.00167.2007
34 https://doi.org/10.1198/016214505000001230
35 https://doi.org/10.1198/106186004x11417
36 https://doi.org/10.1198/106186006x133933
37 https://doi.org/10.1214/aos/1024691079
38 https://doi.org/10.1214/aos/1031689014
39 https://doi.org/10.1214/ss/1009213726
40 https://doi.org/10.1353/pbm.2005.0045
41 https://doi.org/10.2202/1557-4679.1008
42 schema:datePublished 2008-12
43 schema:datePublishedReg 2008-12-01
44 schema:description BACKGROUND: Random forests are becoming increasingly popular in many scientific fields because they can cope with "small n large p" problems, complex interactions and even highly correlated predictor variables. Their variable importance measures have recently been suggested as screening tools for, e.g., gene expression studies. However, these variable importance measures show a bias towards correlated predictor variables. RESULTS: We identify two mechanisms responsible for this finding: (i) A preference for the selection of correlated predictors in the tree building process and (ii) an additional advantage for correlated predictor variables induced by the unconditional permutation scheme that is employed in the computation of the variable importance measure. Based on these considerations we develop a new, conditional permutation scheme for the computation of the variable importance measure. CONCLUSION: The resulting conditional variable importance reflects the true impact of each predictor variable more reliably than the original marginal approach.
45 schema:genre research_article
46 schema:inLanguage en
47 schema:isAccessibleForFree true
48 schema:isPartOf N1841d7ccce87416aa953d5360d6dede6
49 N38e7530fd787480db08778a0330aaa6c
50 sg:journal.1023786
51 schema:name Conditional variable importance for random forests
52 schema:pagination 307
53 schema:productId N18f8adef5a0743bdbadd84c5ff30e659
54 N60f828deb2a249b88253ae0dff831e5e
55 N8af6614297094fbb98a860904850cbb7
56 Nb3a08da86ea34cb4baebc40525f84d04
57 Ndc034750cca84c4aa47f754c74900bd6
58 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042870683
59 https://doi.org/10.1186/1471-2105-9-307
60 schema:sdDatePublished 2019-04-10T16:41
61 schema:sdLicense https://scigraph.springernature.com/explorer/license/
62 schema:sdPublisher N177d6333357140dc89032bdcef837791
63 schema:url http://link.springer.com/10.1186%2F1471-2105-9-307
64 sgo:license sg:explorer/license/
65 sgo:sdDataset articles
66 rdf:type schema:ScholarlyArticle
67 N177d6333357140dc89032bdcef837791 schema:name Springer Nature - SN SciGraph project
68 rdf:type schema:Organization
69 N1841d7ccce87416aa953d5360d6dede6 schema:volumeNumber 9
70 rdf:type schema:PublicationVolume
71 N18f8adef5a0743bdbadd84c5ff30e659 schema:name pubmed_id
72 schema:value 18620558
73 rdf:type schema:PropertyValue
74 N1defcc022e0e4132a92e275f22defc46 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
75 schema:name Amino Acid Sequence
76 rdf:type schema:DefinedTerm
77 N375590f2644a4a7fa10857b87f6661f6 rdf:first sg:person.01346302413.76
78 rdf:rest N9a3e5c5b878c4f9c9c80098409f72c32
79 N38e7530fd787480db08778a0330aaa6c schema:issueNumber 1
80 rdf:type schema:PublicationIssue
81 N4e93c5ad2d144af1bdf9ef40c4861afa schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
82 schema:name Statistics, Nonparametric
83 rdf:type schema:DefinedTerm
84 N5ed6583c4309499db0f95156d57d809f rdf:first sg:person.01342100161.77
85 rdf:rest Nc93b463b05bb4c0b9acbf24cdee3cf50
86 N60f828deb2a249b88253ae0dff831e5e schema:name dimensions_id
87 schema:value pub.1042870683
88 rdf:type schema:PropertyValue
89 N758c6b9e70fa4d6ba43dfe5344cbffdd schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
90 schema:name Biometry
91 rdf:type schema:DefinedTerm
92 N8af6614297094fbb98a860904850cbb7 schema:name readcube_id
93 schema:value cc35e6badcf19ff38cbeb478802a451d504bab3face709b75b637f180fd70f7e
94 rdf:type schema:PropertyValue
95 N8e346d274d354570ab954b15616d9f38 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
96 schema:name Research Design
97 rdf:type schema:DefinedTerm
98 N990f839d7a1a4f60be87d246ac8e3395 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
99 schema:name Factor Analysis, Statistical
100 rdf:type schema:DefinedTerm
101 N9a3e5c5b878c4f9c9c80098409f72c32 rdf:first sg:person.0622676313.19
102 rdf:rest rdf:nil
103 Nb3a08da86ea34cb4baebc40525f84d04 schema:name nlm_unique_id
104 schema:value 100965194
105 rdf:type schema:PropertyValue
106 Nbd70c7077d4345bf97fa8f532d748dec schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
107 schema:name Regression Analysis
108 rdf:type schema:DefinedTerm
109 Nc93b463b05bb4c0b9acbf24cdee3cf50 rdf:first sg:person.01272020411.15
110 rdf:rest N375590f2644a4a7fa10857b87f6661f6
111 Nd29eafe97003428eb0ac390e2e682861 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
112 schema:name Computational Biology
113 rdf:type schema:DefinedTerm
114 Nd5adfacb7df042e0a71a61a333d4db37 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
115 schema:name Binding Sites
116 rdf:type schema:DefinedTerm
117 Nd78ac4d8654743e8a803912d0dbb9583 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
118 schema:name Decision Trees
119 rdf:type schema:DefinedTerm
120 Ndc034750cca84c4aa47f754c74900bd6 schema:name doi
121 schema:value 10.1186/1471-2105-9-307
122 rdf:type schema:PropertyValue
123 Ndfd60f2cc4f74affb173f36345483f1f rdf:first sg:person.01163740613.59
124 rdf:rest N5ed6583c4309499db0f95156d57d809f
125 Nfa3522f3f93941a39c2e6e8a46cef78f schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
126 schema:name Major Histocompatibility Complex
127 rdf:type schema:DefinedTerm
128 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
129 schema:name Information and Computing Sciences
130 rdf:type schema:DefinedTerm
131 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
132 schema:name Artificial Intelligence and Image Processing
133 rdf:type schema:DefinedTerm
134 sg:journal.1023786 schema:issn 1471-2105
135 schema:name BMC Bioinformatics
136 rdf:type schema:Periodical
137 sg:person.01163740613.59 schema:affiliation https://www.grid.ac/institutes/grid.5252.0
138 schema:familyName Strobl
139 schema:givenName Carolin
140 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01163740613.59
141 rdf:type schema:Person
142 sg:person.01272020411.15 schema:affiliation https://www.grid.ac/institutes/grid.5252.0
143 schema:familyName Kneib
144 schema:givenName Thomas
145 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01272020411.15
146 rdf:type schema:Person
147 sg:person.01342100161.77 schema:affiliation https://www.grid.ac/institutes/grid.438311.c
148 schema:familyName Boulesteix
149 schema:givenName Anne-Laure
150 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01342100161.77
151 rdf:type schema:Person
152 sg:person.01346302413.76 schema:affiliation https://www.grid.ac/institutes/grid.5252.0
153 schema:familyName Augustin
154 schema:givenName Thomas
155 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01346302413.76
156 rdf:type schema:Person
157 sg:person.0622676313.19 schema:affiliation https://www.grid.ac/institutes/grid.15788.33
158 schema:familyName Zeileis
159 schema:givenName Achim
160 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0622676313.19
161 rdf:type schema:Person
162 sg:pub.10.1007/bf00058655 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002929950
163 https://doi.org/10.1007/bf00058655
164 rdf:type schema:CreativeWork
165 sg:pub.10.1007/pl00006412 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039309590
166 https://doi.org/10.1007/pl00006412
167 rdf:type schema:CreativeWork
168 sg:pub.10.1023/a:1007515423169 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017116781
169 https://doi.org/10.1023/a:1007515423169
170 rdf:type schema:CreativeWork
171 sg:pub.10.1023/a:1007607513941 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041829946
172 https://doi.org/10.1023/a:1007607513941
173 rdf:type schema:CreativeWork
174 sg:pub.10.1023/a:1010933404324 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024739340
175 https://doi.org/10.1023/a:1010933404324
176 rdf:type schema:CreativeWork
177 sg:pub.10.1186/1471-2105-6-205 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021536997
178 https://doi.org/10.1186/1471-2105-6-205
179 rdf:type schema:CreativeWork
180 sg:pub.10.1186/1471-2105-7-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004156594
181 https://doi.org/10.1186/1471-2105-7-3
182 rdf:type schema:CreativeWork
183 sg:pub.10.1186/1471-2105-8-25 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019863657
184 https://doi.org/10.1186/1471-2105-8-25
185 rdf:type schema:CreativeWork
186 sg:pub.10.1186/1471-2105-8-328 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046518891
187 https://doi.org/10.1186/1471-2105-8-328
188 rdf:type schema:CreativeWork
189 sg:pub.10.1186/1471-2156-5-32 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003496686
190 https://doi.org/10.1186/1471-2156-5-32
191 rdf:type schema:CreativeWork
192 https://doi.org/10.1002/gepi.20041 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007867684
193 rdf:type schema:CreativeWork
194 https://doi.org/10.1002/prot.20865 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004797870
195 rdf:type schema:CreativeWork
196 https://doi.org/10.1016/j.csda.2006.12.030 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046845141
197 rdf:type schema:CreativeWork
198 https://doi.org/10.1016/j.csda.2007.08.015 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049823578
199 rdf:type schema:CreativeWork
200 https://doi.org/10.1016/s0022-5347(01)63150-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038399190
201 rdf:type schema:CreativeWork
202 https://doi.org/10.1016/s0893-6080(01)00127-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011249939
203 rdf:type schema:CreativeWork
204 https://doi.org/10.1021/ci034160g schema:sameAs https://app.dimensions.ai/details/publication/pub.1013785151
205 rdf:type schema:CreativeWork
206 https://doi.org/10.1097/00005650-199710001-00011 schema:sameAs https://app.dimensions.ai/details/publication/pub.1060223456
207 rdf:type schema:CreativeWork
208 https://doi.org/10.1111/j.0006-341x.2001.00632.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1046804254
209 rdf:type schema:CreativeWork
210 https://doi.org/10.1152/physiolgenomics.00167.2007 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023579308
211 rdf:type schema:CreativeWork
212 https://doi.org/10.1198/016214505000001230 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064198454
213 rdf:type schema:CreativeWork
214 https://doi.org/10.1198/106186004x11417 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064199433
215 rdf:type schema:CreativeWork
216 https://doi.org/10.1198/106186006x133933 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064199533
217 rdf:type schema:CreativeWork
218 https://doi.org/10.1214/aos/1024691079 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024161650
219 rdf:type schema:CreativeWork
220 https://doi.org/10.1214/aos/1031689014 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064406186
221 rdf:type schema:CreativeWork
222 https://doi.org/10.1214/ss/1009213726 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037292812
223 rdf:type schema:CreativeWork
224 https://doi.org/10.1353/pbm.2005.0045 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025060069
225 rdf:type schema:CreativeWork
226 https://doi.org/10.2202/1557-4679.1008 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032915999
227 rdf:type schema:CreativeWork
228 https://www.grid.ac/institutes/grid.15788.33 schema:alternateName Vienna University of Economics and Business
229 schema:name Department of Statistics and Mathematics, Wirtschaftsuniversität Wien, Augasse 2 – 6, A-1090, Wien, Austria
230 rdf:type schema:Organization
231 https://www.grid.ac/institutes/grid.438311.c schema:alternateName Sylvia Lawry Centre for Multiple Sclerosis Research
232 schema:name Sylvia Lawry Centre for Multiple Sclerosis Research, Hohenlindener Straße 1, D-81677, München, Germany
233 rdf:type schema:Organization
234 https://www.grid.ac/institutes/grid.5252.0 schema:alternateName Ludwig Maximilian University of Munich
235 schema:name Department of Statistics, Ludwig-Maximilians-Universität Munchen, Ludwigstraße 33, D-80539, München, Germany
236 rdf:type schema:Organization
 




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


...