MathDAMP: a package for differential analysis of metabolite profiles View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2006-12

AUTHORS

Richard Baran, Hayataro Kochi, Natsumi Saito, Makoto Suematsu, Tomoyoshi Soga, Takaaki Nishioka, Martin Robert, Masaru Tomita

ABSTRACT

BACKGROUND: With the advent of metabolomics as a powerful tool for both functional and biomarker discovery, the identification of specific differences between complex metabolite profiles is becoming a major challenge in the data analysis pipeline. The task remains difficult, given the datasets' size, complexity, and common shifts in migration (elution/retention) times between samples analyzed by hyphenated mass spectrometry methods. RESULTS: We present a Mathematica (Wolfram Research, Inc.) package MathDAMP (Mathematica package for Differential Analysis of Metabolite Profiles), which highlights differences between raw datasets acquired by hyphenated mass spectrometry methods by applying arithmetic operations to all corresponding signal intensities on a datapoint-by-datapoint basis. Peak identification and integration is thus bypassed and the results are displayed graphically. To facilitate direct comparisons, the raw datasets are automatically preprocessed and normalized in terms of both migration times and signal intensities. A combination of dynamic programming and global optimization is used for the alignment of the datasets along the migration time dimension. The processed datasets and the results of direct comparisons between them are visualized using density plots (axes represent migration time and m/z values while peaks appear as color-coded spots) providing an intuitive overall view. Various forms of comparisons and statistical tests can be applied to highlight subtle differences. Overlaid electropherograms (chromatograms) corresponding to the vicinities of the candidate differences from any result may be generated in a descending order of significance for visual confirmation. Additionally, a standard library table (a list of m/z values and migration times for known compounds) may be aligned and overlaid on the plots to allow easier identification of metabolites. CONCLUSION: Our tool facilitates the visualization and identification of differences between complex metabolite profiles according to various criteria in an automated fashion and is useful for data-driven discovery of biomarkers and functional genomics. More... »

PAGES

530

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/1471-2105-7-530

DOI

http://dx.doi.org/10.1186/1471-2105-7-530

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1017234046

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/17166258


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": "Biomarkers", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Computational Biology", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Energy Metabolism", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Mathematical Computing", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Slovak Academy of Sciences", 
          "id": "https://www.grid.ac/institutes/grid.419303.c", 
          "name": [
            "Institute for Advanced Biosciences, Keio University, 997-0017, Tsuruoka, Yamagata, Japan", 
            "Present address: Institute of Chemistry, Slovak Academy of Sciences, D\u00fabravsk\u00e1 cesta 9, 845 38, Bratislava, Slovakia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Baran", 
        "givenName": "Richard", 
        "id": "sg:person.0577400024.45", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0577400024.45"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Keio University", 
          "id": "https://www.grid.ac/institutes/grid.26091.3c", 
          "name": [
            "Institute for Advanced Biosciences, Keio University, 997-0017, Tsuruoka, Yamagata, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kochi", 
        "givenName": "Hayataro", 
        "id": "sg:person.01060742466.48", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01060742466.48"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Keio University", 
          "id": "https://www.grid.ac/institutes/grid.26091.3c", 
          "name": [
            "Institute for Advanced Biosciences, Keio University, 997-0017, Tsuruoka, Yamagata, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Saito", 
        "givenName": "Natsumi", 
        "id": "sg:person.016066043302.66", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016066043302.66"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Keio University", 
          "id": "https://www.grid.ac/institutes/grid.26091.3c", 
          "name": [
            "Department of Biochemistry and Integrative Medical Biology, School of Medicine, Keio University, Shinanomachi, Shinjuku-ku, 160-8582, Tokyo, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Suematsu", 
        "givenName": "Makoto", 
        "id": "sg:person.01145347572.01", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01145347572.01"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Keio University", 
          "id": "https://www.grid.ac/institutes/grid.26091.3c", 
          "name": [
            "Institute for Advanced Biosciences, Keio University, 997-0017, Tsuruoka, Yamagata, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Soga", 
        "givenName": "Tomoyoshi", 
        "id": "sg:person.010766270121.69", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010766270121.69"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Keio University", 
          "id": "https://www.grid.ac/institutes/grid.26091.3c", 
          "name": [
            "Institute for Advanced Biosciences, Keio University, 997-0017, Tsuruoka, Yamagata, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Nishioka", 
        "givenName": "Takaaki", 
        "id": "sg:person.012052336321.29", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012052336321.29"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Keio University", 
          "id": "https://www.grid.ac/institutes/grid.26091.3c", 
          "name": [
            "Institute for Advanced Biosciences, Keio University, 997-0017, Tsuruoka, Yamagata, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Robert", 
        "givenName": "Martin", 
        "id": "sg:person.0650441230.99", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0650441230.99"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Keio University", 
          "id": "https://www.grid.ac/institutes/grid.26091.3c", 
          "name": [
            "Institute for Advanced Biosciences, Keio University, 997-0017, Tsuruoka, Yamagata, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Tomita", 
        "givenName": "Masaru", 
        "id": "sg:person.01145134332.16", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01145134332.16"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1093/bioinformatics/btg315", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002703490"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/ac034800e", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004994259"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/ac034800e", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004994259"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0378-4347(01)00527-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015061488"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1104/pp.105.068130", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016214779"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0021-9673(98)00021-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020514502"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0022-4073(00)00021-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028381845"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1016/j.jasms.2003.12.011", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028822273", 
          "https://doi.org/10.1016/j.jasms.2003.12.011"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/btk039", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030759115"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.tibtech.2004.03.007", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037967197"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/ac0605344", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040495455"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/ac0605344", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040495455"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1074/jbc.m601876200", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041240270"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0021-9673(02)00588-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044031254"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.chroma.2005.05.088", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047449606"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/cem.859", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048474449"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/ac051437y", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053369488"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/ac051437y", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053369488"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/ac0354701", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1054995601"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/ac0354701", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1054995601"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/ac0511142", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1054997282"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/ac0511142", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1054997282"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/ac0521596", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1054997731"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/ac0521596", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1054997731"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/ac060245f", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1054998065"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/ac060245f", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1054998065"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/pr0600576", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1056291015"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2006-12", 
    "datePublishedReg": "2006-12-01", 
    "description": "BACKGROUND: With the advent of metabolomics as a powerful tool for both functional and biomarker discovery, the identification of specific differences between complex metabolite profiles is becoming a major challenge in the data analysis pipeline. The task remains difficult, given the datasets' size, complexity, and common shifts in migration (elution/retention) times between samples analyzed by hyphenated mass spectrometry methods.\nRESULTS: We present a Mathematica (Wolfram Research, Inc.) package MathDAMP (Mathematica package for Differential Analysis of Metabolite Profiles), which highlights differences between raw datasets acquired by hyphenated mass spectrometry methods by applying arithmetic operations to all corresponding signal intensities on a datapoint-by-datapoint basis. Peak identification and integration is thus bypassed and the results are displayed graphically. To facilitate direct comparisons, the raw datasets are automatically preprocessed and normalized in terms of both migration times and signal intensities. A combination of dynamic programming and global optimization is used for the alignment of the datasets along the migration time dimension. The processed datasets and the results of direct comparisons between them are visualized using density plots (axes represent migration time and m/z values while peaks appear as color-coded spots) providing an intuitive overall view. Various forms of comparisons and statistical tests can be applied to highlight subtle differences. Overlaid electropherograms (chromatograms) corresponding to the vicinities of the candidate differences from any result may be generated in a descending order of significance for visual confirmation. Additionally, a standard library table (a list of m/z values and migration times for known compounds) may be aligned and overlaid on the plots to allow easier identification of metabolites.\nCONCLUSION: Our tool facilitates the visualization and identification of differences between complex metabolite profiles according to various criteria in an automated fashion and is useful for data-driven discovery of biomarkers and functional genomics.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1186/1471-2105-7-530", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isPartOf": [
      {
        "id": "sg:journal.1023786", 
        "issn": [
          "1471-2105"
        ], 
        "name": "BMC Bioinformatics", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "7"
      }
    ], 
    "name": "MathDAMP: a package for differential analysis of metabolite profiles", 
    "pagination": "530", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "521c66ae8ebd627dbdc845b8409d93eee91dbce92d58c7208a8fc17a34514ba5"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "17166258"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "100965194"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1186/1471-2105-7-530"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1017234046"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1186/1471-2105-7-530", 
      "https://app.dimensions.ai/details/publication/pub.1017234046"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-10T18:18", 
    "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_8675_00000504.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1186%2F1471-2105-7-530"
  }
]
 

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.10.1186/1471-2105-7-530'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1186/1471-2105-7-530'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/1471-2105-7-530'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/1471-2105-7-530'


 

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

199 TRIPLES      21 PREDICATES      53 URIs      25 LITERALS      13 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1186/1471-2105-7-530 schema:about N31c91838902f4948aad693b0f2b3b5be
2 N4ab2744dc9d942a0a00403c9dca5f0b5
3 N7bda0fe36518496da34f70ad50aec6bc
4 Nb076012ade05435f98e3f7ad914a3c7b
5 anzsrc-for:01
6 anzsrc-for:0104
7 schema:author Nad314b5de64f409fa8d3fcb81c27080a
8 schema:citation sg:pub.10.1016/j.jasms.2003.12.011
9 https://doi.org/10.1002/cem.859
10 https://doi.org/10.1016/j.chroma.2005.05.088
11 https://doi.org/10.1016/j.tibtech.2004.03.007
12 https://doi.org/10.1016/s0021-9673(02)00588-5
13 https://doi.org/10.1016/s0021-9673(98)00021-1
14 https://doi.org/10.1016/s0022-4073(00)00021-2
15 https://doi.org/10.1016/s0378-4347(01)00527-8
16 https://doi.org/10.1021/ac034800e
17 https://doi.org/10.1021/ac0354701
18 https://doi.org/10.1021/ac0511142
19 https://doi.org/10.1021/ac051437y
20 https://doi.org/10.1021/ac0521596
21 https://doi.org/10.1021/ac060245f
22 https://doi.org/10.1021/ac0605344
23 https://doi.org/10.1021/pr0600576
24 https://doi.org/10.1074/jbc.m601876200
25 https://doi.org/10.1093/bioinformatics/btg315
26 https://doi.org/10.1093/bioinformatics/btk039
27 https://doi.org/10.1104/pp.105.068130
28 schema:datePublished 2006-12
29 schema:datePublishedReg 2006-12-01
30 schema:description BACKGROUND: With the advent of metabolomics as a powerful tool for both functional and biomarker discovery, the identification of specific differences between complex metabolite profiles is becoming a major challenge in the data analysis pipeline. The task remains difficult, given the datasets' size, complexity, and common shifts in migration (elution/retention) times between samples analyzed by hyphenated mass spectrometry methods. RESULTS: We present a Mathematica (Wolfram Research, Inc.) package MathDAMP (Mathematica package for Differential Analysis of Metabolite Profiles), which highlights differences between raw datasets acquired by hyphenated mass spectrometry methods by applying arithmetic operations to all corresponding signal intensities on a datapoint-by-datapoint basis. Peak identification and integration is thus bypassed and the results are displayed graphically. To facilitate direct comparisons, the raw datasets are automatically preprocessed and normalized in terms of both migration times and signal intensities. A combination of dynamic programming and global optimization is used for the alignment of the datasets along the migration time dimension. The processed datasets and the results of direct comparisons between them are visualized using density plots (axes represent migration time and m/z values while peaks appear as color-coded spots) providing an intuitive overall view. Various forms of comparisons and statistical tests can be applied to highlight subtle differences. Overlaid electropherograms (chromatograms) corresponding to the vicinities of the candidate differences from any result may be generated in a descending order of significance for visual confirmation. Additionally, a standard library table (a list of m/z values and migration times for known compounds) may be aligned and overlaid on the plots to allow easier identification of metabolites. CONCLUSION: Our tool facilitates the visualization and identification of differences between complex metabolite profiles according to various criteria in an automated fashion and is useful for data-driven discovery of biomarkers and functional genomics.
31 schema:genre research_article
32 schema:inLanguage en
33 schema:isAccessibleForFree true
34 schema:isPartOf N0710b841938a41388a30b24537ded994
35 N24abb8b848f04c3296a1c8bdc63a9c81
36 sg:journal.1023786
37 schema:name MathDAMP: a package for differential analysis of metabolite profiles
38 schema:pagination 530
39 schema:productId N4b303994db7247369c9ef875ea134538
40 N960af87721204bf3a90ad5085779061e
41 Nb2e6cab9d4f243e786b5bb67c79da635
42 Nc7f7ca253c574817be3c708dce0fc624
43 Nf87b55525f034356ae580b55eea007fc
44 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017234046
45 https://doi.org/10.1186/1471-2105-7-530
46 schema:sdDatePublished 2019-04-10T18:18
47 schema:sdLicense https://scigraph.springernature.com/explorer/license/
48 schema:sdPublisher N0df2b3bb28364fc3a6ba4bb19cd912d6
49 schema:url http://link.springer.com/10.1186%2F1471-2105-7-530
50 sgo:license sg:explorer/license/
51 sgo:sdDataset articles
52 rdf:type schema:ScholarlyArticle
53 N0710b841938a41388a30b24537ded994 schema:volumeNumber 7
54 rdf:type schema:PublicationVolume
55 N0b368ae4561a4c0f8c27a41689d749af rdf:first sg:person.016066043302.66
56 rdf:rest N83497eb70683486a8e10aa32347966f6
57 N0df2b3bb28364fc3a6ba4bb19cd912d6 schema:name Springer Nature - SN SciGraph project
58 rdf:type schema:Organization
59 N0e782367d84b461e8fba7af47334051c rdf:first sg:person.012052336321.29
60 rdf:rest Nac7334412b1b46c6911a2dbfad0f4e00
61 N24abb8b848f04c3296a1c8bdc63a9c81 schema:issueNumber 1
62 rdf:type schema:PublicationIssue
63 N31c91838902f4948aad693b0f2b3b5be schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
64 schema:name Computational Biology
65 rdf:type schema:DefinedTerm
66 N36579b152fed4d5cbb1100945fac60a7 rdf:first sg:person.01145134332.16
67 rdf:rest rdf:nil
68 N4ab2744dc9d942a0a00403c9dca5f0b5 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
69 schema:name Energy Metabolism
70 rdf:type schema:DefinedTerm
71 N4b303994db7247369c9ef875ea134538 schema:name nlm_unique_id
72 schema:value 100965194
73 rdf:type schema:PropertyValue
74 N7bda0fe36518496da34f70ad50aec6bc schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
75 schema:name Biomarkers
76 rdf:type schema:DefinedTerm
77 N7faec88eb2324076996220390a1e4e8a rdf:first sg:person.01060742466.48
78 rdf:rest N0b368ae4561a4c0f8c27a41689d749af
79 N83497eb70683486a8e10aa32347966f6 rdf:first sg:person.01145347572.01
80 rdf:rest Na39dd9281ee84a49980cc17a63dd4a27
81 N960af87721204bf3a90ad5085779061e schema:name dimensions_id
82 schema:value pub.1017234046
83 rdf:type schema:PropertyValue
84 Na39dd9281ee84a49980cc17a63dd4a27 rdf:first sg:person.010766270121.69
85 rdf:rest N0e782367d84b461e8fba7af47334051c
86 Nac7334412b1b46c6911a2dbfad0f4e00 rdf:first sg:person.0650441230.99
87 rdf:rest N36579b152fed4d5cbb1100945fac60a7
88 Nad314b5de64f409fa8d3fcb81c27080a rdf:first sg:person.0577400024.45
89 rdf:rest N7faec88eb2324076996220390a1e4e8a
90 Nb076012ade05435f98e3f7ad914a3c7b schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
91 schema:name Mathematical Computing
92 rdf:type schema:DefinedTerm
93 Nb2e6cab9d4f243e786b5bb67c79da635 schema:name pubmed_id
94 schema:value 17166258
95 rdf:type schema:PropertyValue
96 Nc7f7ca253c574817be3c708dce0fc624 schema:name doi
97 schema:value 10.1186/1471-2105-7-530
98 rdf:type schema:PropertyValue
99 Nf87b55525f034356ae580b55eea007fc schema:name readcube_id
100 schema:value 521c66ae8ebd627dbdc845b8409d93eee91dbce92d58c7208a8fc17a34514ba5
101 rdf:type schema:PropertyValue
102 anzsrc-for:01 schema:inDefinedTermSet anzsrc-for:
103 schema:name Mathematical Sciences
104 rdf:type schema:DefinedTerm
105 anzsrc-for:0104 schema:inDefinedTermSet anzsrc-for:
106 schema:name Statistics
107 rdf:type schema:DefinedTerm
108 sg:journal.1023786 schema:issn 1471-2105
109 schema:name BMC Bioinformatics
110 rdf:type schema:Periodical
111 sg:person.01060742466.48 schema:affiliation https://www.grid.ac/institutes/grid.26091.3c
112 schema:familyName Kochi
113 schema:givenName Hayataro
114 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01060742466.48
115 rdf:type schema:Person
116 sg:person.010766270121.69 schema:affiliation https://www.grid.ac/institutes/grid.26091.3c
117 schema:familyName Soga
118 schema:givenName Tomoyoshi
119 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010766270121.69
120 rdf:type schema:Person
121 sg:person.01145134332.16 schema:affiliation https://www.grid.ac/institutes/grid.26091.3c
122 schema:familyName Tomita
123 schema:givenName Masaru
124 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01145134332.16
125 rdf:type schema:Person
126 sg:person.01145347572.01 schema:affiliation https://www.grid.ac/institutes/grid.26091.3c
127 schema:familyName Suematsu
128 schema:givenName Makoto
129 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01145347572.01
130 rdf:type schema:Person
131 sg:person.012052336321.29 schema:affiliation https://www.grid.ac/institutes/grid.26091.3c
132 schema:familyName Nishioka
133 schema:givenName Takaaki
134 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012052336321.29
135 rdf:type schema:Person
136 sg:person.016066043302.66 schema:affiliation https://www.grid.ac/institutes/grid.26091.3c
137 schema:familyName Saito
138 schema:givenName Natsumi
139 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016066043302.66
140 rdf:type schema:Person
141 sg:person.0577400024.45 schema:affiliation https://www.grid.ac/institutes/grid.419303.c
142 schema:familyName Baran
143 schema:givenName Richard
144 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0577400024.45
145 rdf:type schema:Person
146 sg:person.0650441230.99 schema:affiliation https://www.grid.ac/institutes/grid.26091.3c
147 schema:familyName Robert
148 schema:givenName Martin
149 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0650441230.99
150 rdf:type schema:Person
151 sg:pub.10.1016/j.jasms.2003.12.011 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028822273
152 https://doi.org/10.1016/j.jasms.2003.12.011
153 rdf:type schema:CreativeWork
154 https://doi.org/10.1002/cem.859 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048474449
155 rdf:type schema:CreativeWork
156 https://doi.org/10.1016/j.chroma.2005.05.088 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047449606
157 rdf:type schema:CreativeWork
158 https://doi.org/10.1016/j.tibtech.2004.03.007 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037967197
159 rdf:type schema:CreativeWork
160 https://doi.org/10.1016/s0021-9673(02)00588-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044031254
161 rdf:type schema:CreativeWork
162 https://doi.org/10.1016/s0021-9673(98)00021-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020514502
163 rdf:type schema:CreativeWork
164 https://doi.org/10.1016/s0022-4073(00)00021-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028381845
165 rdf:type schema:CreativeWork
166 https://doi.org/10.1016/s0378-4347(01)00527-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015061488
167 rdf:type schema:CreativeWork
168 https://doi.org/10.1021/ac034800e schema:sameAs https://app.dimensions.ai/details/publication/pub.1004994259
169 rdf:type schema:CreativeWork
170 https://doi.org/10.1021/ac0354701 schema:sameAs https://app.dimensions.ai/details/publication/pub.1054995601
171 rdf:type schema:CreativeWork
172 https://doi.org/10.1021/ac0511142 schema:sameAs https://app.dimensions.ai/details/publication/pub.1054997282
173 rdf:type schema:CreativeWork
174 https://doi.org/10.1021/ac051437y schema:sameAs https://app.dimensions.ai/details/publication/pub.1053369488
175 rdf:type schema:CreativeWork
176 https://doi.org/10.1021/ac0521596 schema:sameAs https://app.dimensions.ai/details/publication/pub.1054997731
177 rdf:type schema:CreativeWork
178 https://doi.org/10.1021/ac060245f schema:sameAs https://app.dimensions.ai/details/publication/pub.1054998065
179 rdf:type schema:CreativeWork
180 https://doi.org/10.1021/ac0605344 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040495455
181 rdf:type schema:CreativeWork
182 https://doi.org/10.1021/pr0600576 schema:sameAs https://app.dimensions.ai/details/publication/pub.1056291015
183 rdf:type schema:CreativeWork
184 https://doi.org/10.1074/jbc.m601876200 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041240270
185 rdf:type schema:CreativeWork
186 https://doi.org/10.1093/bioinformatics/btg315 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002703490
187 rdf:type schema:CreativeWork
188 https://doi.org/10.1093/bioinformatics/btk039 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030759115
189 rdf:type schema:CreativeWork
190 https://doi.org/10.1104/pp.105.068130 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016214779
191 rdf:type schema:CreativeWork
192 https://www.grid.ac/institutes/grid.26091.3c schema:alternateName Keio University
193 schema:name Department of Biochemistry and Integrative Medical Biology, School of Medicine, Keio University, Shinanomachi, Shinjuku-ku, 160-8582, Tokyo, Japan
194 Institute for Advanced Biosciences, Keio University, 997-0017, Tsuruoka, Yamagata, Japan
195 rdf:type schema:Organization
196 https://www.grid.ac/institutes/grid.419303.c schema:alternateName Slovak Academy of Sciences
197 schema:name Institute for Advanced Biosciences, Keio University, 997-0017, Tsuruoka, Yamagata, Japan
198 Present address: Institute of Chemistry, Slovak Academy of Sciences, Dúbravská cesta 9, 845 38, Bratislava, Slovakia
199 rdf:type schema:Organization
 




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


...