A close look at protein function prediction evaluation protocols View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2015-12

AUTHORS

Indika Kahanda, Christopher S Funk, Fahad Ullah, Karin M Verspoor, Asa Ben-Hur

ABSTRACT

BACKGROUND: The recently held Critical Assessment of Function Annotation challenge (CAFA2) required its participants to submit predictions for a large number of target proteins regardless of whether they have previous annotations or not. This is in contrast to the original CAFA challenge in which participants were asked to submit predictions for proteins with no existing annotations. The CAFA2 task is more realistic, in that it more closely mimics the accumulation of annotations over time. In this study we compare these tasks in terms of their difficulty, and determine whether cross-validation provides a good estimate of performance. RESULTS: The CAFA2 task is a combination of two subtasks: making predictions on annotated proteins and making predictions on previously unannotated proteins. In this study we analyze the performance of several function prediction methods in these two scenarios. Our results show that several methods (structured support vector machine, binary support vector machines and guilt-by-association methods) do not usually achieve the same level of accuracy on these two tasks as that achieved by cross-validation, and that predicting novel annotations for previously annotated proteins is a harder problem than predicting annotations for uncharacterized proteins. We also find that different methods have different performance characteristics in these tasks, and that cross-validation is not adequate at estimating performance and ranking methods. CONCLUSIONS: These results have implications for the design of computational experiments in the area of automated function prediction and can provide useful insight for the understanding and design of future CAFA competitions. More... »

PAGES

41

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s13742-015-0082-5

DOI

http://dx.doi.org/10.1186/s13742-015-0082-5

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1043813395

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/26380075


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

JSON-LD is the canonical representation for SciGraph data.

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

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0801", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Artificial Intelligence and Image Processing", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Information and Computing Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Databases, Protein", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Proteins", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Colorado State University", 
          "id": "https://www.grid.ac/institutes/grid.47894.36", 
          "name": [
            "Department of Computer Science, Colorado State University, 80523, Fort Collins, CO, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kahanda", 
        "givenName": "Indika", 
        "id": "sg:person.01365060341.83", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01365060341.83"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Colorado Anschutz Medical Campus", 
          "id": "https://www.grid.ac/institutes/grid.430503.1", 
          "name": [
            "Computational Bioscience Program, University of Colorado School of Medicine, 80045, Aurora, CO, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Funk", 
        "givenName": "Christopher S", 
        "id": "sg:person.01142564764.38", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01142564764.38"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Colorado State University", 
          "id": "https://www.grid.ac/institutes/grid.47894.36", 
          "name": [
            "Department of Computer Science, Colorado State University, 80523, Fort Collins, CO, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Ullah", 
        "givenName": "Fahad", 
        "id": "sg:person.01054626161.63", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01054626161.63"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Melbourne", 
          "id": "https://www.grid.ac/institutes/grid.1008.9", 
          "name": [
            "Department of Computing and Information Systems, University of Melbourne, 3010 Parkville, Victoria, Australia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Verspoor", 
        "givenName": "Karin M", 
        "id": "sg:person.01372713104.04", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01372713104.04"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Colorado State University", 
          "id": "https://www.grid.ac/institutes/grid.47894.36", 
          "name": [
            "Department of Computer Science, Colorado State University, 80523, Fort Collins, CO, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Ben-Hur", 
        "givenName": "Asa", 
        "id": "sg:person.01242755504.30", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01242755504.30"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1371/journal.pcbi.1003063", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002465231"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1752-0509-4-43", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005678588", 
          "https://doi.org/10.1186/1752-0509-4-43"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/prot.23029", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005949120"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/gb-2008-9-s1-s3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006959314", 
          "https://doi.org/10.1186/gb-2008-9-s1-s3"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1371/journal.pone.0017258", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011603599"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/prot.10381", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1012185703"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1371/journal.pcbi.1002444", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017216956"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1006/jmbi.2000.4315", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018016237"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/prot.340230303", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020261308"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2105-14-s3-s10", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020835861", 
          "https://doi.org/10.1186/1471-2105-14-s3-s10"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2105-14-s3-s10", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020835861", 
          "https://doi.org/10.1186/1471-2105-14-s3-s10"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1752-0509-4-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021942884", 
          "https://doi.org/10.1186/1752-0509-4-1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/nar/gkq537", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021971381"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/nar/gks1158", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022430916"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/nar/gkq973", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025608399"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1038/msb4100129", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030436057"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1038/msb4100129", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030436057"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1371/journal.pbio.1001638", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032956747"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2105-10-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038894722", 
          "https://doi.org/10.1186/1471-2105-10-1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/75556", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044135237", 
          "https://doi.org/10.1038/75556"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/75556", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044135237", 
          "https://doi.org/10.1038/75556"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00018-003-3114-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045087383", 
          "https://doi.org/10.1007/s00018-003-3114-8"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nmeth.2340", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046100259", 
          "https://doi.org/10.1038/nmeth.2340"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/s12859-014-0430-y", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046106991", 
          "https://doi.org/10.1186/s12859-014-0430-y"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/s12859-014-0430-y", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046106991", 
          "https://doi.org/10.1186/s12859-014-0430-y"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/s12859-014-0430-y", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046106991", 
          "https://doi.org/10.1186/s12859-014-0430-y"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2105-10-421", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050579230", 
          "https://doi.org/10.1186/1471-2105-10-421"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/s13326-015-0006-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051473041", 
          "https://doi.org/10.1186/s13326-015-0006-4"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/s13326-015-0006-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051473041", 
          "https://doi.org/10.1186/s13326-015-0006-4"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/btu472", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053512791"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/nar/gkm259", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053546100"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1142/s0219720010004744", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1063004958"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2015-12", 
    "datePublishedReg": "2015-12-01", 
    "description": "BACKGROUND: The recently held Critical Assessment of Function Annotation challenge (CAFA2) required its participants to submit predictions for a large number of target proteins regardless of whether they have previous annotations or not. This is in contrast to the original CAFA challenge in which participants were asked to submit predictions for proteins with no existing annotations. The CAFA2 task is more realistic, in that it more closely mimics the accumulation of annotations over time. In this study we compare these tasks in terms of their difficulty, and determine whether cross-validation provides a good estimate of performance.\nRESULTS: The CAFA2 task is a combination of two subtasks: making predictions on annotated proteins and making predictions on previously unannotated proteins. In this study we analyze the performance of several function prediction methods in these two scenarios. Our results show that several methods (structured support vector machine, binary support vector machines and guilt-by-association methods) do not usually achieve the same level of accuracy on these two tasks as that achieved by cross-validation, and that predicting novel annotations for previously annotated proteins is a harder problem than predicting annotations for uncharacterized proteins. We also find that different methods have different performance characteristics in these tasks, and that cross-validation is not adequate at estimating performance and ranking methods.\nCONCLUSIONS: These results have implications for the design of computational experiments in the area of automated function prediction and can provide useful insight for the understanding and design of future CAFA competitions.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1186/s13742-015-0082-5", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.3111397", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": [
      {
        "id": "sg:journal.1047731", 
        "issn": [
          "2047-217X"
        ], 
        "name": "GigaScience", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "4"
      }
    ], 
    "name": "A close look at protein function prediction evaluation protocols", 
    "pagination": "41", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "d0b356f9088234559025a7371a785d6a3518910744ee4abec6f88df95f42fd56"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "26380075"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "101596872"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1186/s13742-015-0082-5"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1043813395"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1186/s13742-015-0082-5", 
      "https://app.dimensions.ai/details/publication/pub.1043813395"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-10T17:34", 
    "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_8672_00000523.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1186%2Fs13742-015-0082-5"
  }
]
 

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/s13742-015-0082-5'

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/s13742-015-0082-5'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/s13742-015-0082-5'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/s13742-015-0082-5'


 

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

201 TRIPLES      21 PREDICATES      57 URIs      23 LITERALS      11 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1186/s13742-015-0082-5 schema:about N55f5d5f7f7a5410fa12febf4c8b9cf88
2 N78e612df5b1a4f3fa17fa7adcea4561d
3 anzsrc-for:08
4 anzsrc-for:0801
5 schema:author Ne2613f5fc9d14f99ba9fd2973c139e13
6 schema:citation sg:pub.10.1007/s00018-003-3114-8
7 sg:pub.10.1038/75556
8 sg:pub.10.1038/nmeth.2340
9 sg:pub.10.1186/1471-2105-10-1
10 sg:pub.10.1186/1471-2105-10-421
11 sg:pub.10.1186/1471-2105-14-s3-s10
12 sg:pub.10.1186/1752-0509-4-1
13 sg:pub.10.1186/1752-0509-4-43
14 sg:pub.10.1186/gb-2008-9-s1-s3
15 sg:pub.10.1186/s12859-014-0430-y
16 sg:pub.10.1186/s13326-015-0006-4
17 https://doi.org/10.1002/prot.10381
18 https://doi.org/10.1002/prot.23029
19 https://doi.org/10.1002/prot.340230303
20 https://doi.org/10.1006/jmbi.2000.4315
21 https://doi.org/10.1038/msb4100129
22 https://doi.org/10.1093/bioinformatics/btu472
23 https://doi.org/10.1093/nar/gkm259
24 https://doi.org/10.1093/nar/gkq537
25 https://doi.org/10.1093/nar/gkq973
26 https://doi.org/10.1093/nar/gks1158
27 https://doi.org/10.1142/s0219720010004744
28 https://doi.org/10.1371/journal.pbio.1001638
29 https://doi.org/10.1371/journal.pcbi.1002444
30 https://doi.org/10.1371/journal.pcbi.1003063
31 https://doi.org/10.1371/journal.pone.0017258
32 schema:datePublished 2015-12
33 schema:datePublishedReg 2015-12-01
34 schema:description BACKGROUND: The recently held Critical Assessment of Function Annotation challenge (CAFA2) required its participants to submit predictions for a large number of target proteins regardless of whether they have previous annotations or not. This is in contrast to the original CAFA challenge in which participants were asked to submit predictions for proteins with no existing annotations. The CAFA2 task is more realistic, in that it more closely mimics the accumulation of annotations over time. In this study we compare these tasks in terms of their difficulty, and determine whether cross-validation provides a good estimate of performance. RESULTS: The CAFA2 task is a combination of two subtasks: making predictions on annotated proteins and making predictions on previously unannotated proteins. In this study we analyze the performance of several function prediction methods in these two scenarios. Our results show that several methods (structured support vector machine, binary support vector machines and guilt-by-association methods) do not usually achieve the same level of accuracy on these two tasks as that achieved by cross-validation, and that predicting novel annotations for previously annotated proteins is a harder problem than predicting annotations for uncharacterized proteins. We also find that different methods have different performance characteristics in these tasks, and that cross-validation is not adequate at estimating performance and ranking methods. CONCLUSIONS: These results have implications for the design of computational experiments in the area of automated function prediction and can provide useful insight for the understanding and design of future CAFA competitions.
35 schema:genre research_article
36 schema:inLanguage en
37 schema:isAccessibleForFree true
38 schema:isPartOf N5d8b083808f74d008dca1130f33bb52a
39 N99a174f0527941f9b0f6e7208b7c0368
40 sg:journal.1047731
41 schema:name A close look at protein function prediction evaluation protocols
42 schema:pagination 41
43 schema:productId N213f38d779ca4cc1b9f8babc8486cf8c
44 N34e52794ef934ccd8ef43e3ca38330e0
45 N709464236a0a49c39f7f0a18562850e3
46 N74330e5898c84ca0b7689d27f0a0964f
47 N7cc4d8dd76224e00a7c5a4870483df63
48 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043813395
49 https://doi.org/10.1186/s13742-015-0082-5
50 schema:sdDatePublished 2019-04-10T17:34
51 schema:sdLicense https://scigraph.springernature.com/explorer/license/
52 schema:sdPublisher N748fd07d22784b69b23da2b4bd31c051
53 schema:url http://link.springer.com/10.1186%2Fs13742-015-0082-5
54 sgo:license sg:explorer/license/
55 sgo:sdDataset articles
56 rdf:type schema:ScholarlyArticle
57 N213f38d779ca4cc1b9f8babc8486cf8c schema:name readcube_id
58 schema:value d0b356f9088234559025a7371a785d6a3518910744ee4abec6f88df95f42fd56
59 rdf:type schema:PropertyValue
60 N34e52794ef934ccd8ef43e3ca38330e0 schema:name doi
61 schema:value 10.1186/s13742-015-0082-5
62 rdf:type schema:PropertyValue
63 N55f5d5f7f7a5410fa12febf4c8b9cf88 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
64 schema:name Databases, Protein
65 rdf:type schema:DefinedTerm
66 N5d8b083808f74d008dca1130f33bb52a schema:issueNumber 1
67 rdf:type schema:PublicationIssue
68 N680999bbb22c43e48687e5d84cffa3f3 rdf:first sg:person.01242755504.30
69 rdf:rest rdf:nil
70 N709464236a0a49c39f7f0a18562850e3 schema:name pubmed_id
71 schema:value 26380075
72 rdf:type schema:PropertyValue
73 N74330e5898c84ca0b7689d27f0a0964f schema:name dimensions_id
74 schema:value pub.1043813395
75 rdf:type schema:PropertyValue
76 N748fd07d22784b69b23da2b4bd31c051 schema:name Springer Nature - SN SciGraph project
77 rdf:type schema:Organization
78 N76d5ef9608584cb8b79b103a5b89aca6 rdf:first sg:person.01142564764.38
79 rdf:rest N932c37d7f23041cab27d1d5fbb15f06a
80 N78e612df5b1a4f3fa17fa7adcea4561d schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
81 schema:name Proteins
82 rdf:type schema:DefinedTerm
83 N7cc4d8dd76224e00a7c5a4870483df63 schema:name nlm_unique_id
84 schema:value 101596872
85 rdf:type schema:PropertyValue
86 N932c37d7f23041cab27d1d5fbb15f06a rdf:first sg:person.01054626161.63
87 rdf:rest Nd74d2aeb92b14d539543a72705889dae
88 N99a174f0527941f9b0f6e7208b7c0368 schema:volumeNumber 4
89 rdf:type schema:PublicationVolume
90 Nd74d2aeb92b14d539543a72705889dae rdf:first sg:person.01372713104.04
91 rdf:rest N680999bbb22c43e48687e5d84cffa3f3
92 Ne2613f5fc9d14f99ba9fd2973c139e13 rdf:first sg:person.01365060341.83
93 rdf:rest N76d5ef9608584cb8b79b103a5b89aca6
94 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
95 schema:name Information and Computing Sciences
96 rdf:type schema:DefinedTerm
97 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
98 schema:name Artificial Intelligence and Image Processing
99 rdf:type schema:DefinedTerm
100 sg:grant.3111397 http://pending.schema.org/fundedItem sg:pub.10.1186/s13742-015-0082-5
101 rdf:type schema:MonetaryGrant
102 sg:journal.1047731 schema:issn 2047-217X
103 schema:name GigaScience
104 rdf:type schema:Periodical
105 sg:person.01054626161.63 schema:affiliation https://www.grid.ac/institutes/grid.47894.36
106 schema:familyName Ullah
107 schema:givenName Fahad
108 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01054626161.63
109 rdf:type schema:Person
110 sg:person.01142564764.38 schema:affiliation https://www.grid.ac/institutes/grid.430503.1
111 schema:familyName Funk
112 schema:givenName Christopher S
113 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01142564764.38
114 rdf:type schema:Person
115 sg:person.01242755504.30 schema:affiliation https://www.grid.ac/institutes/grid.47894.36
116 schema:familyName Ben-Hur
117 schema:givenName Asa
118 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01242755504.30
119 rdf:type schema:Person
120 sg:person.01365060341.83 schema:affiliation https://www.grid.ac/institutes/grid.47894.36
121 schema:familyName Kahanda
122 schema:givenName Indika
123 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01365060341.83
124 rdf:type schema:Person
125 sg:person.01372713104.04 schema:affiliation https://www.grid.ac/institutes/grid.1008.9
126 schema:familyName Verspoor
127 schema:givenName Karin M
128 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01372713104.04
129 rdf:type schema:Person
130 sg:pub.10.1007/s00018-003-3114-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045087383
131 https://doi.org/10.1007/s00018-003-3114-8
132 rdf:type schema:CreativeWork
133 sg:pub.10.1038/75556 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044135237
134 https://doi.org/10.1038/75556
135 rdf:type schema:CreativeWork
136 sg:pub.10.1038/nmeth.2340 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046100259
137 https://doi.org/10.1038/nmeth.2340
138 rdf:type schema:CreativeWork
139 sg:pub.10.1186/1471-2105-10-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038894722
140 https://doi.org/10.1186/1471-2105-10-1
141 rdf:type schema:CreativeWork
142 sg:pub.10.1186/1471-2105-10-421 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050579230
143 https://doi.org/10.1186/1471-2105-10-421
144 rdf:type schema:CreativeWork
145 sg:pub.10.1186/1471-2105-14-s3-s10 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020835861
146 https://doi.org/10.1186/1471-2105-14-s3-s10
147 rdf:type schema:CreativeWork
148 sg:pub.10.1186/1752-0509-4-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021942884
149 https://doi.org/10.1186/1752-0509-4-1
150 rdf:type schema:CreativeWork
151 sg:pub.10.1186/1752-0509-4-43 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005678588
152 https://doi.org/10.1186/1752-0509-4-43
153 rdf:type schema:CreativeWork
154 sg:pub.10.1186/gb-2008-9-s1-s3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006959314
155 https://doi.org/10.1186/gb-2008-9-s1-s3
156 rdf:type schema:CreativeWork
157 sg:pub.10.1186/s12859-014-0430-y schema:sameAs https://app.dimensions.ai/details/publication/pub.1046106991
158 https://doi.org/10.1186/s12859-014-0430-y
159 rdf:type schema:CreativeWork
160 sg:pub.10.1186/s13326-015-0006-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051473041
161 https://doi.org/10.1186/s13326-015-0006-4
162 rdf:type schema:CreativeWork
163 https://doi.org/10.1002/prot.10381 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012185703
164 rdf:type schema:CreativeWork
165 https://doi.org/10.1002/prot.23029 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005949120
166 rdf:type schema:CreativeWork
167 https://doi.org/10.1002/prot.340230303 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020261308
168 rdf:type schema:CreativeWork
169 https://doi.org/10.1006/jmbi.2000.4315 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018016237
170 rdf:type schema:CreativeWork
171 https://doi.org/10.1038/msb4100129 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030436057
172 rdf:type schema:CreativeWork
173 https://doi.org/10.1093/bioinformatics/btu472 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053512791
174 rdf:type schema:CreativeWork
175 https://doi.org/10.1093/nar/gkm259 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053546100
176 rdf:type schema:CreativeWork
177 https://doi.org/10.1093/nar/gkq537 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021971381
178 rdf:type schema:CreativeWork
179 https://doi.org/10.1093/nar/gkq973 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025608399
180 rdf:type schema:CreativeWork
181 https://doi.org/10.1093/nar/gks1158 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022430916
182 rdf:type schema:CreativeWork
183 https://doi.org/10.1142/s0219720010004744 schema:sameAs https://app.dimensions.ai/details/publication/pub.1063004958
184 rdf:type schema:CreativeWork
185 https://doi.org/10.1371/journal.pbio.1001638 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032956747
186 rdf:type schema:CreativeWork
187 https://doi.org/10.1371/journal.pcbi.1002444 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017216956
188 rdf:type schema:CreativeWork
189 https://doi.org/10.1371/journal.pcbi.1003063 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002465231
190 rdf:type schema:CreativeWork
191 https://doi.org/10.1371/journal.pone.0017258 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011603599
192 rdf:type schema:CreativeWork
193 https://www.grid.ac/institutes/grid.1008.9 schema:alternateName University of Melbourne
194 schema:name Department of Computing and Information Systems, University of Melbourne, 3010 Parkville, Victoria, Australia
195 rdf:type schema:Organization
196 https://www.grid.ac/institutes/grid.430503.1 schema:alternateName University of Colorado Anschutz Medical Campus
197 schema:name Computational Bioscience Program, University of Colorado School of Medicine, 80045, Aurora, CO, USA
198 rdf:type schema:Organization
199 https://www.grid.ac/institutes/grid.47894.36 schema:alternateName Colorado State University
200 schema:name Department of Computer Science, Colorado State University, 80523, Fort Collins, CO, USA
201 rdf:type schema:Organization
 




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


...