Statistical implications of pooling RNA samples for microarray experiments View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2003-12

AUTHORS

Xuejun Peng, Constance L Wood, Eric M Blalock, Kuey Chu Chen, Philip W Landfield, Arnold J Stromberg

ABSTRACT

BACKGROUND: Microarray technology has become a very important tool for studying gene expression profiles under various conditions. Biologists often pool RNA samples extracted from different subjects onto a single microarray chip to help defray the cost of microarray experiments as well as to correct for the technical difficulty in getting sufficient RNA from a single subject. However, the statistical, technical and financial implications of pooling have not been explicitly investigated. RESULTS: Modeling the resulting gene expression from sample pooling as a mixture of individual responses, we derived expressions for the experimental error and provided both upper and lower bounds for its value in terms of the variability among individuals and the number of RNA samples pooled. Using "virtual" pooling of data from real experiments and computer simulations, we investigated the statistical properties of RNA sample pooling. Our study reveals that pooling biological samples appropriately is statistically valid and efficient for microarray experiments. Furthermore, optimal pooling design(s) can be found to meet statistical requirements while minimizing total cost. CONCLUSIONS: Appropriate RNA pooling can provide equivalent power and improve efficiency and cost-effectiveness for microarray experiments with a modest increase in total number of subjects. Pooling schemes in terms of replicates of subjects and arrays can be compared before experiments are conducted. More... »

PAGES

26

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/1471-2105-4-26

DOI

http://dx.doi.org/10.1186/1471-2105-4-26

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1012892677

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/12823867


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0104", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Statistics", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/01", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Mathematical Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Computational Biology", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Computer Simulation", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Empirical Research", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Gene Expression Profiling", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Models, Statistical", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Oligonucleotide Array Sequence Analysis", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Pilot Projects", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "RNA", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Research Design", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Sample Size", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "University of Kentucky", 
          "id": "https://www.grid.ac/institutes/grid.266539.d", 
          "name": [
            "Department of Statistics, University of Kentucky, 40506, Lexington, KY, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Peng", 
        "givenName": "Xuejun", 
        "id": "sg:person.01261757416.08", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01261757416.08"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Kentucky", 
          "id": "https://www.grid.ac/institutes/grid.266539.d", 
          "name": [
            "Department of Statistics, University of Kentucky, 40506, Lexington, KY, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Wood", 
        "givenName": "Constance L", 
        "id": "sg:person.0610253602.63", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0610253602.63"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Kentucky", 
          "id": "https://www.grid.ac/institutes/grid.266539.d", 
          "name": [
            "Department of Molecular and Biomedical Pharmacology, University of Kentucky, 40536, Lexington, KY, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Blalock", 
        "givenName": "Eric M", 
        "id": "sg:person.01326653354.93", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01326653354.93"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Kentucky", 
          "id": "https://www.grid.ac/institutes/grid.266539.d", 
          "name": [
            "Department of Molecular and Biomedical Pharmacology, University of Kentucky, 40536, Lexington, KY, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Chen", 
        "givenName": "Kuey Chu", 
        "id": "sg:person.01136101360.42", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01136101360.42"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Kentucky", 
          "id": "https://www.grid.ac/institutes/grid.266539.d", 
          "name": [
            "Department of Molecular and Biomedical Pharmacology, University of Kentucky, 40536, Lexington, KY, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Landfield", 
        "givenName": "Philip W", 
        "id": "sg:person.01052642563.08", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01052642563.08"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Kentucky", 
          "id": "https://www.grid.ac/institutes/grid.266539.d", 
          "name": [
            "Department of Statistics, University of Kentucky, 40506, Lexington, KY, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Stromberg", 
        "givenName": "Arnold J", 
        "id": "sg:person.07364774577.25", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07364774577.25"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1093/jnci/94.7.513", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020029575"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0168-9525(02)02665-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020073866"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2105-3-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023798762", 
          "https://doi.org/10.1186/1471-2105-3-4"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2105-3-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023798762", 
          "https://doi.org/10.1186/1471-2105-3-4"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/18.suppl_1.s105", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031487565"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2105-3-23", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032404074", 
          "https://doi.org/10.1186/1471-2105-3-23"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0960-9822(02)00808-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040345954"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/gerona/56.2.b52", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051418203"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1073/pnas.97.18.9834", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051886548"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/gb-2002-3-5-research0022", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052162147", 
          "https://doi.org/10.1186/gb-2002-3-5-research0022"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/00401706.1995.10484302", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1058287060"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1198/tech.2003.s34", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064202034"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1523/jneurosci.23-09-03807.2003", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1075282766"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/9780470544341.ch5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1086097181"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2003-12", 
    "datePublishedReg": "2003-12-01", 
    "description": "BACKGROUND: Microarray technology has become a very important tool for studying gene expression profiles under various conditions. Biologists often pool RNA samples extracted from different subjects onto a single microarray chip to help defray the cost of microarray experiments as well as to correct for the technical difficulty in getting sufficient RNA from a single subject. However, the statistical, technical and financial implications of pooling have not been explicitly investigated.\nRESULTS: Modeling the resulting gene expression from sample pooling as a mixture of individual responses, we derived expressions for the experimental error and provided both upper and lower bounds for its value in terms of the variability among individuals and the number of RNA samples pooled. Using \"virtual\" pooling of data from real experiments and computer simulations, we investigated the statistical properties of RNA sample pooling. Our study reveals that pooling biological samples appropriately is statistically valid and efficient for microarray experiments. Furthermore, optimal pooling design(s) can be found to meet statistical requirements while minimizing total cost.\nCONCLUSIONS: Appropriate RNA pooling can provide equivalent power and improve efficiency and cost-effectiveness for microarray experiments with a modest increase in total number of subjects. Pooling schemes in terms of replicates of subjects and arrays can be compared before experiments are conducted.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1186/1471-2105-4-26", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.2444922", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.2438434", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.2434689", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.3023367", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.2633449", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": [
      {
        "id": "sg:journal.1023786", 
        "issn": [
          "1471-2105"
        ], 
        "name": "BMC Bioinformatics", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "4"
      }
    ], 
    "name": "Statistical implications of pooling RNA samples for microarray experiments", 
    "pagination": "26", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "3d6b85a9d2c24c26995afbacb5a80abcb59cb297572aabdb0809de243db89f64"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "12823867"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "100965194"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1186/1471-2105-4-26"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1012892677"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1186/1471-2105-4-26", 
      "https://app.dimensions.ai/details/publication/pub.1012892677"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T00:14", 
    "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_8695_00000504.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1186%2F1471-2105-4-26"
  }
]
 

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.10.1186/1471-2105-4-26'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1186/1471-2105-4-26'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/1471-2105-4-26'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/1471-2105-4-26'


 

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

196 TRIPLES      21 PREDICATES      52 URIs      31 LITERALS      19 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1186/1471-2105-4-26 schema:about N02bc173e312c4a78a75be5553fed1172
2 N081767aa798c4ccc9f2b263cff9fb1e9
3 N2d61bea245064174ac9fec80c8be4564
4 N34859ac0fb0642ebb60da16e077734a9
5 N5e373e0d72d746b2a753df3f3145e108
6 N618c3a7980224e5bb34e9e92db7ce60a
7 N7b532bf5790042c2b1fd31cfc49ac163
8 Nadaefcbd16114bfcac617808913c24b5
9 Nb0c4faddcd3e40d9a7a4ba97b33c4182
10 Nf23c8b436d6b4cd1a3129f44636e989e
11 anzsrc-for:01
12 anzsrc-for:0104
13 schema:author N25afccc558e64890bd1c4677c1e17446
14 schema:citation sg:pub.10.1186/1471-2105-3-23
15 sg:pub.10.1186/1471-2105-3-4
16 sg:pub.10.1186/gb-2002-3-5-research0022
17 https://doi.org/10.1016/s0168-9525(02)02665-3
18 https://doi.org/10.1016/s0960-9822(02)00808-4
19 https://doi.org/10.1073/pnas.97.18.9834
20 https://doi.org/10.1080/00401706.1995.10484302
21 https://doi.org/10.1093/bioinformatics/18.suppl_1.s105
22 https://doi.org/10.1093/gerona/56.2.b52
23 https://doi.org/10.1093/jnci/94.7.513
24 https://doi.org/10.1109/9780470544341.ch5
25 https://doi.org/10.1198/tech.2003.s34
26 https://doi.org/10.1523/jneurosci.23-09-03807.2003
27 schema:datePublished 2003-12
28 schema:datePublishedReg 2003-12-01
29 schema:description BACKGROUND: Microarray technology has become a very important tool for studying gene expression profiles under various conditions. Biologists often pool RNA samples extracted from different subjects onto a single microarray chip to help defray the cost of microarray experiments as well as to correct for the technical difficulty in getting sufficient RNA from a single subject. However, the statistical, technical and financial implications of pooling have not been explicitly investigated. RESULTS: Modeling the resulting gene expression from sample pooling as a mixture of individual responses, we derived expressions for the experimental error and provided both upper and lower bounds for its value in terms of the variability among individuals and the number of RNA samples pooled. Using "virtual" pooling of data from real experiments and computer simulations, we investigated the statistical properties of RNA sample pooling. Our study reveals that pooling biological samples appropriately is statistically valid and efficient for microarray experiments. Furthermore, optimal pooling design(s) can be found to meet statistical requirements while minimizing total cost. CONCLUSIONS: Appropriate RNA pooling can provide equivalent power and improve efficiency and cost-effectiveness for microarray experiments with a modest increase in total number of subjects. Pooling schemes in terms of replicates of subjects and arrays can be compared before experiments are conducted.
30 schema:genre research_article
31 schema:inLanguage en
32 schema:isAccessibleForFree true
33 schema:isPartOf Na348936911504f24afb9395f3d14e34d
34 Nf2bae87db33e4509a77554963e700de5
35 sg:journal.1023786
36 schema:name Statistical implications of pooling RNA samples for microarray experiments
37 schema:pagination 26
38 schema:productId N2955a60ca557459e94ab912490712f51
39 N3b828bc0c3da4d628c73c10c390957a0
40 N4d24547cc96843b583d3574e4d0dbec2
41 N688a45a06d164a9cbda8f1427f4d3aab
42 Nfc8b191a3e5847d982583de6870be9cf
43 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012892677
44 https://doi.org/10.1186/1471-2105-4-26
45 schema:sdDatePublished 2019-04-11T00:14
46 schema:sdLicense https://scigraph.springernature.com/explorer/license/
47 schema:sdPublisher N24e4c1607fca4bf7897786608e67d32a
48 schema:url http://link.springer.com/10.1186%2F1471-2105-4-26
49 sgo:license sg:explorer/license/
50 sgo:sdDataset articles
51 rdf:type schema:ScholarlyArticle
52 N02bc173e312c4a78a75be5553fed1172 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
53 schema:name Sample Size
54 rdf:type schema:DefinedTerm
55 N081767aa798c4ccc9f2b263cff9fb1e9 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
56 schema:name RNA
57 rdf:type schema:DefinedTerm
58 N24e4c1607fca4bf7897786608e67d32a schema:name Springer Nature - SN SciGraph project
59 rdf:type schema:Organization
60 N25afccc558e64890bd1c4677c1e17446 rdf:first sg:person.01261757416.08
61 rdf:rest N87c5ae8d74fb49d5a087ab3133d88da9
62 N2955a60ca557459e94ab912490712f51 schema:name readcube_id
63 schema:value 3d6b85a9d2c24c26995afbacb5a80abcb59cb297572aabdb0809de243db89f64
64 rdf:type schema:PropertyValue
65 N2d61bea245064174ac9fec80c8be4564 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
66 schema:name Computer Simulation
67 rdf:type schema:DefinedTerm
68 N34859ac0fb0642ebb60da16e077734a9 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
69 schema:name Oligonucleotide Array Sequence Analysis
70 rdf:type schema:DefinedTerm
71 N3b828bc0c3da4d628c73c10c390957a0 schema:name dimensions_id
72 schema:value pub.1012892677
73 rdf:type schema:PropertyValue
74 N4d24547cc96843b583d3574e4d0dbec2 schema:name doi
75 schema:value 10.1186/1471-2105-4-26
76 rdf:type schema:PropertyValue
77 N5e373e0d72d746b2a753df3f3145e108 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
78 schema:name Computational Biology
79 rdf:type schema:DefinedTerm
80 N618c3a7980224e5bb34e9e92db7ce60a schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
81 schema:name Gene Expression Profiling
82 rdf:type schema:DefinedTerm
83 N64d75a2a7a2b4e4499cc523a011f8323 rdf:first sg:person.07364774577.25
84 rdf:rest rdf:nil
85 N688a45a06d164a9cbda8f1427f4d3aab schema:name nlm_unique_id
86 schema:value 100965194
87 rdf:type schema:PropertyValue
88 N7b532bf5790042c2b1fd31cfc49ac163 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
89 schema:name Research Design
90 rdf:type schema:DefinedTerm
91 N8007873426a844dba6f2849ddb7f2a7a rdf:first sg:person.01052642563.08
92 rdf:rest N64d75a2a7a2b4e4499cc523a011f8323
93 N87c5ae8d74fb49d5a087ab3133d88da9 rdf:first sg:person.0610253602.63
94 rdf:rest Nee2c50fae19b4d00bbf3822b2033b791
95 N9c61459e98d846e688cc2b066840dd19 rdf:first sg:person.01136101360.42
96 rdf:rest N8007873426a844dba6f2849ddb7f2a7a
97 Na348936911504f24afb9395f3d14e34d schema:volumeNumber 4
98 rdf:type schema:PublicationVolume
99 Nadaefcbd16114bfcac617808913c24b5 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
100 schema:name Pilot Projects
101 rdf:type schema:DefinedTerm
102 Nb0c4faddcd3e40d9a7a4ba97b33c4182 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
103 schema:name Empirical Research
104 rdf:type schema:DefinedTerm
105 Nee2c50fae19b4d00bbf3822b2033b791 rdf:first sg:person.01326653354.93
106 rdf:rest N9c61459e98d846e688cc2b066840dd19
107 Nf23c8b436d6b4cd1a3129f44636e989e schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
108 schema:name Models, Statistical
109 rdf:type schema:DefinedTerm
110 Nf2bae87db33e4509a77554963e700de5 schema:issueNumber 1
111 rdf:type schema:PublicationIssue
112 Nfc8b191a3e5847d982583de6870be9cf schema:name pubmed_id
113 schema:value 12823867
114 rdf:type schema:PropertyValue
115 anzsrc-for:01 schema:inDefinedTermSet anzsrc-for:
116 schema:name Mathematical Sciences
117 rdf:type schema:DefinedTerm
118 anzsrc-for:0104 schema:inDefinedTermSet anzsrc-for:
119 schema:name Statistics
120 rdf:type schema:DefinedTerm
121 sg:grant.2434689 http://pending.schema.org/fundedItem sg:pub.10.1186/1471-2105-4-26
122 rdf:type schema:MonetaryGrant
123 sg:grant.2438434 http://pending.schema.org/fundedItem sg:pub.10.1186/1471-2105-4-26
124 rdf:type schema:MonetaryGrant
125 sg:grant.2444922 http://pending.schema.org/fundedItem sg:pub.10.1186/1471-2105-4-26
126 rdf:type schema:MonetaryGrant
127 sg:grant.2633449 http://pending.schema.org/fundedItem sg:pub.10.1186/1471-2105-4-26
128 rdf:type schema:MonetaryGrant
129 sg:grant.3023367 http://pending.schema.org/fundedItem sg:pub.10.1186/1471-2105-4-26
130 rdf:type schema:MonetaryGrant
131 sg:journal.1023786 schema:issn 1471-2105
132 schema:name BMC Bioinformatics
133 rdf:type schema:Periodical
134 sg:person.01052642563.08 schema:affiliation https://www.grid.ac/institutes/grid.266539.d
135 schema:familyName Landfield
136 schema:givenName Philip W
137 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01052642563.08
138 rdf:type schema:Person
139 sg:person.01136101360.42 schema:affiliation https://www.grid.ac/institutes/grid.266539.d
140 schema:familyName Chen
141 schema:givenName Kuey Chu
142 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01136101360.42
143 rdf:type schema:Person
144 sg:person.01261757416.08 schema:affiliation https://www.grid.ac/institutes/grid.266539.d
145 schema:familyName Peng
146 schema:givenName Xuejun
147 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01261757416.08
148 rdf:type schema:Person
149 sg:person.01326653354.93 schema:affiliation https://www.grid.ac/institutes/grid.266539.d
150 schema:familyName Blalock
151 schema:givenName Eric M
152 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01326653354.93
153 rdf:type schema:Person
154 sg:person.0610253602.63 schema:affiliation https://www.grid.ac/institutes/grid.266539.d
155 schema:familyName Wood
156 schema:givenName Constance L
157 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0610253602.63
158 rdf:type schema:Person
159 sg:person.07364774577.25 schema:affiliation https://www.grid.ac/institutes/grid.266539.d
160 schema:familyName Stromberg
161 schema:givenName Arnold J
162 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07364774577.25
163 rdf:type schema:Person
164 sg:pub.10.1186/1471-2105-3-23 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032404074
165 https://doi.org/10.1186/1471-2105-3-23
166 rdf:type schema:CreativeWork
167 sg:pub.10.1186/1471-2105-3-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023798762
168 https://doi.org/10.1186/1471-2105-3-4
169 rdf:type schema:CreativeWork
170 sg:pub.10.1186/gb-2002-3-5-research0022 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052162147
171 https://doi.org/10.1186/gb-2002-3-5-research0022
172 rdf:type schema:CreativeWork
173 https://doi.org/10.1016/s0168-9525(02)02665-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020073866
174 rdf:type schema:CreativeWork
175 https://doi.org/10.1016/s0960-9822(02)00808-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040345954
176 rdf:type schema:CreativeWork
177 https://doi.org/10.1073/pnas.97.18.9834 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051886548
178 rdf:type schema:CreativeWork
179 https://doi.org/10.1080/00401706.1995.10484302 schema:sameAs https://app.dimensions.ai/details/publication/pub.1058287060
180 rdf:type schema:CreativeWork
181 https://doi.org/10.1093/bioinformatics/18.suppl_1.s105 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031487565
182 rdf:type schema:CreativeWork
183 https://doi.org/10.1093/gerona/56.2.b52 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051418203
184 rdf:type schema:CreativeWork
185 https://doi.org/10.1093/jnci/94.7.513 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020029575
186 rdf:type schema:CreativeWork
187 https://doi.org/10.1109/9780470544341.ch5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1086097181
188 rdf:type schema:CreativeWork
189 https://doi.org/10.1198/tech.2003.s34 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064202034
190 rdf:type schema:CreativeWork
191 https://doi.org/10.1523/jneurosci.23-09-03807.2003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1075282766
192 rdf:type schema:CreativeWork
193 https://www.grid.ac/institutes/grid.266539.d schema:alternateName University of Kentucky
194 schema:name Department of Molecular and Biomedical Pharmacology, University of Kentucky, 40536, Lexington, KY, USA
195 Department of Statistics, University of Kentucky, 40506, Lexington, KY, USA
196 rdf:type schema:Organization
 




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


...