Methods for visual mining of genomic and proteomic data atlases View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2012-04-23

AUTHORS

John Boyle, Richard Kreisberg, Ryan Bressler, Sarah Killcoyne

ABSTRACT

BackgroundAs the volume, complexity and diversity of the information that scientists work with on a daily basis continues to rise, so too does the requirement for new analytic software. The analytic software must solve the dichotomy that exists between the need to allow for a high level of scientific reasoning, and the requirement to have an intuitive and easy to use tool which does not require specialist, and often arduous, training to use. Information visualization provides a solution to this problem, as it allows for direct manipulation and interaction with diverse and complex data. The challenge addressing bioinformatics researches is how to apply this knowledge to data sets that are continually growing in a field that is rapidly changing.ResultsThis paper discusses an approach to the development of visual mining tools capable of supporting the mining of massive data collections used in systems biology research, and also discusses lessons that have been learned providing tools for both local researchers and the wider community. Example tools were developed which are designed to enable the exploration and analyses of both proteomics and genomics based atlases. These atlases represent large repositories of raw and processed experiment data generated to support the identification of biomarkers through mass spectrometry (the PeptideAtlas) and the genomic characterization of cancer (The Cancer Genome Atlas). Specifically the tools are designed to allow for: the visual mining of thousands of mass spectrometry experiments, to assist in designing informed targeted protein assays; and the interactive analysis of hundreds of genomes, to explore the variations across different cancer genomes and cancer types.ConclusionsThe mining of massive repositories of biological data requires the development of new tools and techniques. Visual exploration of the large-scale atlas data sets allows researchers to mine data to find new meaning and make sense at scales from single samples to entire populations. Providing linked task specific views that allow a user to start from points of interest (from diseases to single genes) enables targeted exploration of thousands of spectra and genomes. As the composition of the atlases changes, and our understanding of the biology increase, new tasks will continually arise. It is therefore important to provide the means to make the data available in a suitable manner in as short a time as possible. We have done this through the use of common visualization workflows, into which we rapidly deploy visual tools. These visualizations follow common metaphors where possible to assist users in understanding the displayed data. Rapid development of tools and task specific views allows researchers to mine large-scale data almost as quickly as it is produced. Ultimately these visual tools enable new inferences, new analyses and further refinement of the large scale data being provided in atlases such as PeptideAtlas and The Cancer Genome Atlas. More... »

PAGES

58

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/1471-2105-13-58

DOI

http://dx.doi.org/10.1186/1471-2105-13-58

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1008009070

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/22524279


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/2008/06", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Biological Sciences", 
        "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"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0601", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Biochemistry and Cell Biology", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0604", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Genetics", 
        "type": "DefinedTerm"
      }, 
      {
        "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/2008/0806", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Information Systems", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Colonic Neoplasms", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Data Mining", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Female", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Genomics", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Glioblastoma", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Humans", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Mass Spectrometry", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Neoplasms", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Ovarian Neoplasms", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Proteomics", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Software", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Institute for Systems Biology, 401 Terry Ave N, 98092, Seattle, WA, USA", 
          "id": "http://www.grid.ac/institutes/grid.64212.33", 
          "name": [
            "Institute for Systems Biology, 401 Terry Ave N, 98092, Seattle, WA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Boyle", 
        "givenName": "John", 
        "id": "sg:person.01110033460.10", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01110033460.10"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Institute for Systems Biology, 401 Terry Ave N, 98092, Seattle, WA, USA", 
          "id": "http://www.grid.ac/institutes/grid.64212.33", 
          "name": [
            "Institute for Systems Biology, 401 Terry Ave N, 98092, Seattle, WA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kreisberg", 
        "givenName": "Richard", 
        "id": "sg:person.0736074277.36", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0736074277.36"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Institute for Systems Biology, 401 Terry Ave N, 98092, Seattle, WA, USA", 
          "id": "http://www.grid.ac/institutes/grid.64212.33", 
          "name": [
            "Institute for Systems Biology, 401 Terry Ave N, 98092, Seattle, WA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Bressler", 
        "givenName": "Ryan", 
        "id": "sg:person.01043002526.05", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01043002526.05"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Institute for Systems Biology, 401 Terry Ave N, 98092, Seattle, WA, USA", 
          "id": "http://www.grid.ac/institutes/grid.64212.33", 
          "name": [
            "Institute for Systems Biology, 401 Terry Ave N, 98092, Seattle, WA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Killcoyne", 
        "givenName": "Sarah", 
        "id": "sg:person.01274460411.86", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01274460411.86"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1038/nature07385", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039570773", 
          "https://doi.org/10.1038/nature07385"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1755-8794-3-7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049012532", 
          "https://doi.org/10.1186/1755-8794-3-7"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2105-9-295", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047116914", 
          "https://doi.org/10.1186/1471-2105-9-295"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2105-10-79", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039759522", 
          "https://doi.org/10.1186/1471-2105-10-79"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nbt0410-322", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038433746", 
          "https://doi.org/10.1038/nbt0410-322"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2105-12-78", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019495389", 
          "https://doi.org/10.1186/1471-2105-12-78"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1741-7007-5-33", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004081265", 
          "https://doi.org/10.1186/1741-7007-5-33"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1755-8417-2-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039522645", 
          "https://doi.org/10.1186/1755-8417-2-3"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nmeth.1363", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020581697", 
          "https://doi.org/10.1038/nmeth.1363"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2012-04-23", 
    "datePublishedReg": "2012-04-23", 
    "description": "BackgroundAs the volume, complexity and diversity of the information that scientists work with on a daily basis continues to rise, so too does the requirement for new analytic software. The analytic software must solve the dichotomy that exists between the need to allow for a high level of scientific reasoning, and the requirement to have an intuitive and easy to use tool which does not require specialist, and often arduous, training to use. Information visualization provides a solution to this problem, as it allows for direct manipulation and interaction with diverse and complex data. The challenge addressing bioinformatics researches is how to apply this knowledge to data sets that are continually growing in a field that is rapidly changing.ResultsThis paper discusses an approach to the development of visual mining tools capable of supporting the mining of massive data collections used in systems biology research, and also discusses lessons that have been learned providing tools for both local researchers and the wider community. Example tools were developed which are designed to enable the exploration and analyses of both proteomics and genomics based atlases. These atlases represent large repositories of raw and processed experiment data generated to support the identification of biomarkers through mass spectrometry (the PeptideAtlas) and the genomic characterization of cancer (The Cancer Genome Atlas). Specifically the tools are designed to allow for: the visual mining of thousands of mass spectrometry experiments, to assist in designing informed targeted protein assays; and the interactive analysis of hundreds of genomes, to explore the variations across different cancer genomes and cancer types.ConclusionsThe mining of massive repositories of biological data requires the development of new tools and techniques. Visual exploration of the large-scale atlas data sets allows researchers to mine data to find new meaning and make sense at scales from single samples to entire populations. Providing linked task specific views that allow a user to start from points of interest (from diseases to single genes) enables targeted exploration of thousands of spectra and genomes. As the composition of the atlases changes, and our understanding of the biology increase, new tasks will continually arise. It is therefore important to provide the means to make the data available in a suitable manner in as short a time as possible. We have done this through the use of common visualization workflows, into which we rapidly deploy visual tools. These visualizations follow common metaphors where possible to assist users in understanding the displayed data. Rapid development of tools and task specific views allows researchers to mine large-scale data almost as quickly as it is produced. Ultimately these visual tools enable new inferences, new analyses and further refinement of the large scale data being provided in atlases such as PeptideAtlas and The Cancer Genome Atlas.", 
    "genre": "article", 
    "id": "sg:pub.10.1186/1471-2105-13-58", 
    "inLanguage": "en", 
    "isAccessibleForFree": true, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.2346398", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.2480242", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.2440532", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.2696331", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.2520375", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": [
      {
        "id": "sg:journal.1023786", 
        "issn": [
          "1471-2105"
        ], 
        "name": "BMC Bioinformatics", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "13"
      }
    ], 
    "keywords": [
      "large-scale data", 
      "visual mining", 
      "analytics software", 
      "visual tool", 
      "massive data collection", 
      "data sets", 
      "visualization workflows", 
      "specific view", 
      "information visualization", 
      "mining tools", 
      "points of interest", 
      "exploration of thousands", 
      "complex data", 
      "large repositories", 
      "massive repositories", 
      "mine data", 
      "systems biology research", 
      "bioinformatics research", 
      "example tool", 
      "visual exploration", 
      "direct manipulation", 
      "new tasks", 
      "mining", 
      "interactive analysis", 
      "scale data", 
      "rapid development", 
      "Cancer Genome Atlas", 
      "users", 
      "cancer genomes", 
      "biological data", 
      "repository", 
      "biology research", 
      "genomic characterization", 
      "software", 
      "genome", 
      "suitable manner", 
      "Genome Atlas", 
      "visualization", 
      "data collection", 
      "mass spectrometry experiments", 
      "tool", 
      "requirements", 
      "identification of biomarkers", 
      "atlases", 
      "protein assays", 
      "data atlases", 
      "set", 
      "cancer types", 
      "new tool", 
      "workflow", 
      "researchers", 
      "new inferences", 
      "thousands", 
      "spectrometry experiments", 
      "daily basis", 
      "task", 
      "reasoning", 
      "mass spectrometry", 
      "complexity", 
      "exploration", 
      "genomics", 
      "proteomics", 
      "common metaphors", 
      "PeptideAtlas", 
      "data", 
      "wider community", 
      "experiment data", 
      "diversity", 
      "information", 
      "high levels", 
      "inference", 
      "challenges", 
      "view", 
      "collection", 
      "research", 
      "assays", 
      "single sample", 
      "technique", 
      "hundreds", 
      "development", 
      "solution", 
      "atlas", 
      "identification", 
      "knowledge", 
      "metaphor", 
      "need", 
      "experiments", 
      "further refinement", 
      "population", 
      "method", 
      "analysis", 
      "scientists", 
      "refinement", 
      "interaction", 
      "characterization", 
      "community", 
      "new analysis", 
      "manipulation", 
      "manner", 
      "spectrometry", 
      "entire population", 
      "variation", 
      "interest", 
      "composition", 
      "understanding", 
      "specialists", 
      "point", 
      "time", 
      "lessons", 
      "biomarkers", 
      "field", 
      "use", 
      "cancer", 
      "sense", 
      "basis", 
      "levels", 
      "means", 
      "scientific reasoning", 
      "changes", 
      "new meaning", 
      "types", 
      "meaning", 
      "increase", 
      "local researchers", 
      "volume", 
      "scale", 
      "dichotomy", 
      "samples", 
      "approach", 
      "spectra", 
      "problem", 
      "paper"
    ], 
    "name": "Methods for visual mining of genomic and proteomic data atlases", 
    "pagination": "58", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1008009070"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1186/1471-2105-13-58"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "22524279"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1186/1471-2105-13-58", 
      "https://app.dimensions.ai/details/publication/pub.1008009070"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2022-06-01T22:09", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20220601/entities/gbq_results/article/article_562.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1186/1471-2105-13-58"
  }
]
 

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.10.1186/1471-2105-13-58'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1186/1471-2105-13-58'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/1471-2105-13-58'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/1471-2105-13-58'


 

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

320 TRIPLES      22 PREDICATES      182 URIs      161 LITERALS      18 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1186/1471-2105-13-58 schema:about N14bc93c626324b2392b67cd84f3473c6
2 N2392315f14e7477499a0f3a23be90acf
3 N278e3c4119314be98afb4a947f4be7fb
4 N2bd0e03f0e844fd8881d406a3605064e
5 N4fd3861c96514865b42afd8992f8d1fc
6 N62640bfb90a846d899dfd155f89aeb2f
7 Na02eb12d591c4927aa1c964ff23ce272
8 Nb61b6ef950764bc19d4718e15b551c96
9 Nbeaad5fa5a33404fb19faa316b8ee098
10 Nc6034fb9f2a14ff882595935b347fb21
11 Ncb62e3211b894c0da4a3d8ab29e6bc6d
12 anzsrc-for:06
13 anzsrc-for:0601
14 anzsrc-for:0604
15 anzsrc-for:08
16 anzsrc-for:0801
17 anzsrc-for:0806
18 schema:author N41caa07499234ee0bbde7e1d65540caf
19 schema:citation sg:pub.10.1038/nature07385
20 sg:pub.10.1038/nbt0410-322
21 sg:pub.10.1038/nmeth.1363
22 sg:pub.10.1186/1471-2105-10-79
23 sg:pub.10.1186/1471-2105-12-78
24 sg:pub.10.1186/1471-2105-9-295
25 sg:pub.10.1186/1741-7007-5-33
26 sg:pub.10.1186/1755-8417-2-3
27 sg:pub.10.1186/1755-8794-3-7
28 schema:datePublished 2012-04-23
29 schema:datePublishedReg 2012-04-23
30 schema:description BackgroundAs the volume, complexity and diversity of the information that scientists work with on a daily basis continues to rise, so too does the requirement for new analytic software. The analytic software must solve the dichotomy that exists between the need to allow for a high level of scientific reasoning, and the requirement to have an intuitive and easy to use tool which does not require specialist, and often arduous, training to use. Information visualization provides a solution to this problem, as it allows for direct manipulation and interaction with diverse and complex data. The challenge addressing bioinformatics researches is how to apply this knowledge to data sets that are continually growing in a field that is rapidly changing.ResultsThis paper discusses an approach to the development of visual mining tools capable of supporting the mining of massive data collections used in systems biology research, and also discusses lessons that have been learned providing tools for both local researchers and the wider community. Example tools were developed which are designed to enable the exploration and analyses of both proteomics and genomics based atlases. These atlases represent large repositories of raw and processed experiment data generated to support the identification of biomarkers through mass spectrometry (the PeptideAtlas) and the genomic characterization of cancer (The Cancer Genome Atlas). Specifically the tools are designed to allow for: the visual mining of thousands of mass spectrometry experiments, to assist in designing informed targeted protein assays; and the interactive analysis of hundreds of genomes, to explore the variations across different cancer genomes and cancer types.ConclusionsThe mining of massive repositories of biological data requires the development of new tools and techniques. Visual exploration of the large-scale atlas data sets allows researchers to mine data to find new meaning and make sense at scales from single samples to entire populations. Providing linked task specific views that allow a user to start from points of interest (from diseases to single genes) enables targeted exploration of thousands of spectra and genomes. As the composition of the atlases changes, and our understanding of the biology increase, new tasks will continually arise. It is therefore important to provide the means to make the data available in a suitable manner in as short a time as possible. We have done this through the use of common visualization workflows, into which we rapidly deploy visual tools. These visualizations follow common metaphors where possible to assist users in understanding the displayed data. Rapid development of tools and task specific views allows researchers to mine large-scale data almost as quickly as it is produced. Ultimately these visual tools enable new inferences, new analyses and further refinement of the large scale data being provided in atlases such as PeptideAtlas and The Cancer Genome Atlas.
31 schema:genre article
32 schema:inLanguage en
33 schema:isAccessibleForFree true
34 schema:isPartOf N0370ba7cb0db42c2b12561057362ebd2
35 Nc27a0c4ebcb942d88b3a579a7af0d2dd
36 sg:journal.1023786
37 schema:keywords Cancer Genome Atlas
38 Genome Atlas
39 PeptideAtlas
40 analysis
41 analytics software
42 approach
43 assays
44 atlas
45 atlases
46 basis
47 bioinformatics research
48 biological data
49 biology research
50 biomarkers
51 cancer
52 cancer genomes
53 cancer types
54 challenges
55 changes
56 characterization
57 collection
58 common metaphors
59 community
60 complex data
61 complexity
62 composition
63 daily basis
64 data
65 data atlases
66 data collection
67 data sets
68 development
69 dichotomy
70 direct manipulation
71 diversity
72 entire population
73 example tool
74 experiment data
75 experiments
76 exploration
77 exploration of thousands
78 field
79 further refinement
80 genome
81 genomic characterization
82 genomics
83 high levels
84 hundreds
85 identification
86 identification of biomarkers
87 increase
88 inference
89 information
90 information visualization
91 interaction
92 interactive analysis
93 interest
94 knowledge
95 large repositories
96 large-scale data
97 lessons
98 levels
99 local researchers
100 manipulation
101 manner
102 mass spectrometry
103 mass spectrometry experiments
104 massive data collection
105 massive repositories
106 meaning
107 means
108 metaphor
109 method
110 mine data
111 mining
112 mining tools
113 need
114 new analysis
115 new inferences
116 new meaning
117 new tasks
118 new tool
119 paper
120 point
121 points of interest
122 population
123 problem
124 protein assays
125 proteomics
126 rapid development
127 reasoning
128 refinement
129 repository
130 requirements
131 research
132 researchers
133 samples
134 scale
135 scale data
136 scientific reasoning
137 scientists
138 sense
139 set
140 single sample
141 software
142 solution
143 specialists
144 specific view
145 spectra
146 spectrometry
147 spectrometry experiments
148 suitable manner
149 systems biology research
150 task
151 technique
152 thousands
153 time
154 tool
155 types
156 understanding
157 use
158 users
159 variation
160 view
161 visual exploration
162 visual mining
163 visual tool
164 visualization
165 visualization workflows
166 volume
167 wider community
168 workflow
169 schema:name Methods for visual mining of genomic and proteomic data atlases
170 schema:pagination 58
171 schema:productId N81ede7e44d2b47df83976d65f9f0ed9a
172 Nafad9f0008fd47a8a6d7cc74140fce13
173 Neb91dcf6d1fb467dbc43c7e4d3626a68
174 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008009070
175 https://doi.org/10.1186/1471-2105-13-58
176 schema:sdDatePublished 2022-06-01T22:09
177 schema:sdLicense https://scigraph.springernature.com/explorer/license/
178 schema:sdPublisher N22694442f997492e8c803895d877bcac
179 schema:url https://doi.org/10.1186/1471-2105-13-58
180 sgo:license sg:explorer/license/
181 sgo:sdDataset articles
182 rdf:type schema:ScholarlyArticle
183 N0370ba7cb0db42c2b12561057362ebd2 schema:issueNumber 1
184 rdf:type schema:PublicationIssue
185 N14bc93c626324b2392b67cd84f3473c6 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
186 schema:name Mass Spectrometry
187 rdf:type schema:DefinedTerm
188 N22694442f997492e8c803895d877bcac schema:name Springer Nature - SN SciGraph project
189 rdf:type schema:Organization
190 N2392315f14e7477499a0f3a23be90acf schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
191 schema:name Neoplasms
192 rdf:type schema:DefinedTerm
193 N278e3c4119314be98afb4a947f4be7fb schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
194 schema:name Humans
195 rdf:type schema:DefinedTerm
196 N2bd0e03f0e844fd8881d406a3605064e schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
197 schema:name Proteomics
198 rdf:type schema:DefinedTerm
199 N41caa07499234ee0bbde7e1d65540caf rdf:first sg:person.01110033460.10
200 rdf:rest N5d7eed66016f49afb0f2ab18b45d9b7a
201 N4fd3861c96514865b42afd8992f8d1fc schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
202 schema:name Glioblastoma
203 rdf:type schema:DefinedTerm
204 N5d7eed66016f49afb0f2ab18b45d9b7a rdf:first sg:person.0736074277.36
205 rdf:rest Ne9291bbf80b14fe3aa32bd0b48f99e02
206 N62640bfb90a846d899dfd155f89aeb2f schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
207 schema:name Female
208 rdf:type schema:DefinedTerm
209 N7364b66546b7495395837d584bd53e3d rdf:first sg:person.01274460411.86
210 rdf:rest rdf:nil
211 N81ede7e44d2b47df83976d65f9f0ed9a schema:name pubmed_id
212 schema:value 22524279
213 rdf:type schema:PropertyValue
214 Na02eb12d591c4927aa1c964ff23ce272 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
215 schema:name Ovarian Neoplasms
216 rdf:type schema:DefinedTerm
217 Nafad9f0008fd47a8a6d7cc74140fce13 schema:name doi
218 schema:value 10.1186/1471-2105-13-58
219 rdf:type schema:PropertyValue
220 Nb61b6ef950764bc19d4718e15b551c96 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
221 schema:name Software
222 rdf:type schema:DefinedTerm
223 Nbeaad5fa5a33404fb19faa316b8ee098 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
224 schema:name Genomics
225 rdf:type schema:DefinedTerm
226 Nc27a0c4ebcb942d88b3a579a7af0d2dd schema:volumeNumber 13
227 rdf:type schema:PublicationVolume
228 Nc6034fb9f2a14ff882595935b347fb21 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
229 schema:name Data Mining
230 rdf:type schema:DefinedTerm
231 Ncb62e3211b894c0da4a3d8ab29e6bc6d schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
232 schema:name Colonic Neoplasms
233 rdf:type schema:DefinedTerm
234 Ne9291bbf80b14fe3aa32bd0b48f99e02 rdf:first sg:person.01043002526.05
235 rdf:rest N7364b66546b7495395837d584bd53e3d
236 Neb91dcf6d1fb467dbc43c7e4d3626a68 schema:name dimensions_id
237 schema:value pub.1008009070
238 rdf:type schema:PropertyValue
239 anzsrc-for:06 schema:inDefinedTermSet anzsrc-for:
240 schema:name Biological Sciences
241 rdf:type schema:DefinedTerm
242 anzsrc-for:0601 schema:inDefinedTermSet anzsrc-for:
243 schema:name Biochemistry and Cell Biology
244 rdf:type schema:DefinedTerm
245 anzsrc-for:0604 schema:inDefinedTermSet anzsrc-for:
246 schema:name Genetics
247 rdf:type schema:DefinedTerm
248 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
249 schema:name Information and Computing Sciences
250 rdf:type schema:DefinedTerm
251 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
252 schema:name Artificial Intelligence and Image Processing
253 rdf:type schema:DefinedTerm
254 anzsrc-for:0806 schema:inDefinedTermSet anzsrc-for:
255 schema:name Information Systems
256 rdf:type schema:DefinedTerm
257 sg:grant.2346398 http://pending.schema.org/fundedItem sg:pub.10.1186/1471-2105-13-58
258 rdf:type schema:MonetaryGrant
259 sg:grant.2440532 http://pending.schema.org/fundedItem sg:pub.10.1186/1471-2105-13-58
260 rdf:type schema:MonetaryGrant
261 sg:grant.2480242 http://pending.schema.org/fundedItem sg:pub.10.1186/1471-2105-13-58
262 rdf:type schema:MonetaryGrant
263 sg:grant.2520375 http://pending.schema.org/fundedItem sg:pub.10.1186/1471-2105-13-58
264 rdf:type schema:MonetaryGrant
265 sg:grant.2696331 http://pending.schema.org/fundedItem sg:pub.10.1186/1471-2105-13-58
266 rdf:type schema:MonetaryGrant
267 sg:journal.1023786 schema:issn 1471-2105
268 schema:name BMC Bioinformatics
269 schema:publisher Springer Nature
270 rdf:type schema:Periodical
271 sg:person.01043002526.05 schema:affiliation grid-institutes:grid.64212.33
272 schema:familyName Bressler
273 schema:givenName Ryan
274 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01043002526.05
275 rdf:type schema:Person
276 sg:person.01110033460.10 schema:affiliation grid-institutes:grid.64212.33
277 schema:familyName Boyle
278 schema:givenName John
279 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01110033460.10
280 rdf:type schema:Person
281 sg:person.01274460411.86 schema:affiliation grid-institutes:grid.64212.33
282 schema:familyName Killcoyne
283 schema:givenName Sarah
284 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01274460411.86
285 rdf:type schema:Person
286 sg:person.0736074277.36 schema:affiliation grid-institutes:grid.64212.33
287 schema:familyName Kreisberg
288 schema:givenName Richard
289 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0736074277.36
290 rdf:type schema:Person
291 sg:pub.10.1038/nature07385 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039570773
292 https://doi.org/10.1038/nature07385
293 rdf:type schema:CreativeWork
294 sg:pub.10.1038/nbt0410-322 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038433746
295 https://doi.org/10.1038/nbt0410-322
296 rdf:type schema:CreativeWork
297 sg:pub.10.1038/nmeth.1363 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020581697
298 https://doi.org/10.1038/nmeth.1363
299 rdf:type schema:CreativeWork
300 sg:pub.10.1186/1471-2105-10-79 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039759522
301 https://doi.org/10.1186/1471-2105-10-79
302 rdf:type schema:CreativeWork
303 sg:pub.10.1186/1471-2105-12-78 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019495389
304 https://doi.org/10.1186/1471-2105-12-78
305 rdf:type schema:CreativeWork
306 sg:pub.10.1186/1471-2105-9-295 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047116914
307 https://doi.org/10.1186/1471-2105-9-295
308 rdf:type schema:CreativeWork
309 sg:pub.10.1186/1741-7007-5-33 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004081265
310 https://doi.org/10.1186/1741-7007-5-33
311 rdf:type schema:CreativeWork
312 sg:pub.10.1186/1755-8417-2-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039522645
313 https://doi.org/10.1186/1755-8417-2-3
314 rdf:type schema:CreativeWork
315 sg:pub.10.1186/1755-8794-3-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049012532
316 https://doi.org/10.1186/1755-8794-3-7
317 rdf:type schema:CreativeWork
318 grid-institutes:grid.64212.33 schema:alternateName Institute for Systems Biology, 401 Terry Ave N, 98092, Seattle, WA, USA
319 schema:name Institute for Systems Biology, 401 Terry Ave N, 98092, Seattle, WA, USA
320 rdf:type schema:Organization
 




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


...