Predicting Secretory Proteins with SignalP View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2017

AUTHORS

Henrik Nielsen

ABSTRACT

SignalP is the currently most widely used program for prediction of signal peptides from amino acid sequences. Proteins with signal peptides are targeted to the secretory pathway, but are not necessarily secreted. After a brief introduction to the biology of signal peptides and the history of signal peptide prediction, this chapter will describe all the options of the current version of SignalP and the details of the output from the program. The chapter includes a case study where the scores of SignalP were used in a novel way to predict the functional effects of amino acid substitutions in signal peptides. More... »

PAGES

59-73

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-1-4939-7015-5_6

DOI

http://dx.doi.org/10.1007/978-1-4939-7015-5_6

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1085083567

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/28451972


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/2008/0601", 
        "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/2008/06", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Biological Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Algorithms", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Computational Biology", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Protein Sorting Signals", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Proteins", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Sequence Analysis, Protein", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Software", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Technical University of Denmark", 
          "id": "https://www.grid.ac/institutes/grid.5170.3", 
          "name": [
            "Department of Bio and Health Informatics, Technical University of Denmark"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Nielsen", 
        "givenName": "Henrik", 
        "id": "sg:person.07463377332.00", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07463377332.00"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1371/journal.pcbi.1000213", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000141088"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/16.8.741", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1001205116"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/protein/10.1.1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1001804651"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/nar/gkg509", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002198958"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2105-10-s15-s2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002399387", 
          "https://doi.org/10.1186/1471-2105-10-s15-s2"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1146/annurev.bi.57.070188.001441", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002620497"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/1462-2920.12105", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005371494"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nmeth0410-248", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1007489634", 
          "https://doi.org/10.1038/nmeth0410-248"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nmeth0410-248", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1007489634", 
          "https://doi.org/10.1038/nmeth0410-248"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/pro.5560060601", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009133816"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.2478/s11756-009-0118-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1012945314", 
          "https://doi.org/10.2478/s11756-009-0118-3"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf01868635", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013186929", 
          "https://doi.org/10.1007/bf01868635"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf01868635", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013186929", 
          "https://doi.org/10.1007/bf01868635"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/btn550", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014637543"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/humu.20798", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016431133"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1126/science.285.5428.760", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017945219"
        ], 
        "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/10.1093/emboj/18.11.2982", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018166997"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1083/jcb.105.6.2905", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018773283"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jmb.2004.03.016", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021467321"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nprot.2015.123", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021631236", 
          "https://doi.org/10.1038/nprot.2015.123"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/(sici)1097-0134(199602)24:2<165::aid-prot4>3.0.co;2-i", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025817597"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0304-4157(96)00003-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026534679"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1006/jmbi.2000.3903", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028412590"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1146/annurev-biochem-060614-034251", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1029604447"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jmb.2004.05.028", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030482208"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1432-1033.1983.tb07424.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030836600"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1371/journal.pone.0117380", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031777809"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1371/journal.pone.0117380", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031777809"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1110/ps.0303703", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032600631"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.compbiolchem.2011.12.001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034058950"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0168-1702(85)90051-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034315067"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0168-1702(85)90051-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034315067"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2164-16-s8-s1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035513632", 
          "https://doi.org/10.1186/1471-2164-16-s8-s1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2105-6-256", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036012600", 
          "https://doi.org/10.1186/1471-2105-6-256"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1128/mbio.00339-12", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038778573"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0304-4157(88)90013-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040026267"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0304-4157(88)90013-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040026267"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2105-6-167", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044447875", 
          "https://doi.org/10.1186/1471-2105-6-167"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2105-6-167", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044447875", 
          "https://doi.org/10.1186/1471-2105-6-167"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1139/m81-111", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048834333"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf00556363", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049459058", 
          "https://doi.org/10.1007/bf00556363"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf00556363", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049459058", 
          "https://doi.org/10.1007/bf00556363"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nmeth.1701", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049894869", 
          "https://doi.org/10.1038/nmeth.1701"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0092-8674(00)80444-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051562058"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/nar/14.11.4683", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053470686"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0092-8674(00)80443-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053683715"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/7.4.485", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1059414088"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/protein/2.7.531", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1059980451"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1126/science.270.5235.397", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062551464"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1142/s0129065797000537", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062899929"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1083334413", 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2017", 
    "datePublishedReg": "2017-01-01", 
    "description": "SignalP is the currently most widely used program for prediction of signal peptides from amino acid sequences. Proteins with signal peptides are targeted to the secretory pathway, but are not necessarily secreted. After a brief introduction to the biology of signal peptides and the history of signal peptide prediction, this chapter will describe all the options of the current version of SignalP and the details of the output from the program. The chapter includes a case study where the scores of SignalP were used in a novel way to predict the functional effects of amino acid substitutions in signal peptides.", 
    "editor": [
      {
        "familyName": "Kihara", 
        "givenName": "Daisuke", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/978-1-4939-7015-5_6", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isPartOf": {
      "isbn": [
        "978-1-4939-7013-1", 
        "978-1-4939-7015-5"
      ], 
      "name": "Protein Function Prediction", 
      "type": "Book"
    }, 
    "name": "Predicting Secretory Proteins with SignalP", 
    "pagination": "59-73", 
    "productId": [
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/978-1-4939-7015-5_6"
        ]
      }, 
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "af3e75f8a3af844dc25e59fa88c1e1b7f3b648f10d0139ac2e6a200c42eee0e7"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1085083567"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "28451972"
        ]
      }
    ], 
    "publisher": {
      "location": "New York, NY", 
      "name": "Springer New York", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/978-1-4939-7015-5_6", 
      "https://app.dimensions.ai/details/publication/pub.1085083567"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2019-04-15T15:59", 
    "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_00000600.jsonl", 
    "type": "Chapter", 
    "url": "http://link.springer.com/10.1007/978-1-4939-7015-5_6"
  }
]
 

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.10.1007/978-1-4939-7015-5_6'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-1-4939-7015-5_6'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-1-4939-7015-5_6'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-1-4939-7015-5_6'


 

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

237 TRIPLES      23 PREDICATES      79 URIs      27 LITERALS      15 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/978-1-4939-7015-5_6 schema:about N1f85f8ef155e4ab99815c8ade83ad29c
2 N250608e34ba54d2ca58ca602eafb05ba
3 N60c328c02c7c42a4bc855682662b7227
4 Nb5dea8ba2af945488ebe79d1aac2029e
5 Ndbab6b9e1e55406dbe8567099181dbb9
6 Nf6cea991661f4062b44b5e9753915434
7 anzsrc-for:06
8 anzsrc-for:0601
9 schema:author N8a1cec92e6504322bfadd38f66da1460
10 schema:citation sg:pub.10.1007/bf00556363
11 sg:pub.10.1007/bf01868635
12 sg:pub.10.1038/nmeth.1701
13 sg:pub.10.1038/nmeth0410-248
14 sg:pub.10.1038/nprot.2015.123
15 sg:pub.10.1186/1471-2105-10-s15-s2
16 sg:pub.10.1186/1471-2105-6-167
17 sg:pub.10.1186/1471-2105-6-256
18 sg:pub.10.1186/1471-2164-16-s8-s1
19 sg:pub.10.2478/s11756-009-0118-3
20 https://app.dimensions.ai/details/publication/pub.1083334413
21 https://doi.org/10.1002/(sici)1097-0134(199602)24:2<165::aid-prot4>3.0.co;2-i
22 https://doi.org/10.1002/humu.20798
23 https://doi.org/10.1002/pro.5560060601
24 https://doi.org/10.1006/jmbi.2000.3903
25 https://doi.org/10.1006/jmbi.2000.4315
26 https://doi.org/10.1016/0168-1702(85)90051-6
27 https://doi.org/10.1016/0304-4157(88)90013-5
28 https://doi.org/10.1016/0304-4157(96)00003-2
29 https://doi.org/10.1016/j.compbiolchem.2011.12.001
30 https://doi.org/10.1016/j.jmb.2004.03.016
31 https://doi.org/10.1016/j.jmb.2004.05.028
32 https://doi.org/10.1016/s0092-8674(00)80443-2
33 https://doi.org/10.1016/s0092-8674(00)80444-4
34 https://doi.org/10.1083/jcb.105.6.2905
35 https://doi.org/10.1093/bioinformatics/16.8.741
36 https://doi.org/10.1093/bioinformatics/7.4.485
37 https://doi.org/10.1093/bioinformatics/btn550
38 https://doi.org/10.1093/emboj/18.11.2982
39 https://doi.org/10.1093/nar/14.11.4683
40 https://doi.org/10.1093/nar/gkg509
41 https://doi.org/10.1093/protein/10.1.1
42 https://doi.org/10.1093/protein/2.7.531
43 https://doi.org/10.1110/ps.0303703
44 https://doi.org/10.1111/1462-2920.12105
45 https://doi.org/10.1111/j.1432-1033.1983.tb07424.x
46 https://doi.org/10.1126/science.270.5235.397
47 https://doi.org/10.1126/science.285.5428.760
48 https://doi.org/10.1128/mbio.00339-12
49 https://doi.org/10.1139/m81-111
50 https://doi.org/10.1142/s0129065797000537
51 https://doi.org/10.1146/annurev-biochem-060614-034251
52 https://doi.org/10.1146/annurev.bi.57.070188.001441
53 https://doi.org/10.1371/journal.pcbi.1000213
54 https://doi.org/10.1371/journal.pone.0117380
55 schema:datePublished 2017
56 schema:datePublishedReg 2017-01-01
57 schema:description SignalP is the currently most widely used program for prediction of signal peptides from amino acid sequences. Proteins with signal peptides are targeted to the secretory pathway, but are not necessarily secreted. After a brief introduction to the biology of signal peptides and the history of signal peptide prediction, this chapter will describe all the options of the current version of SignalP and the details of the output from the program. The chapter includes a case study where the scores of SignalP were used in a novel way to predict the functional effects of amino acid substitutions in signal peptides.
58 schema:editor Nb56d5d940e9a4cc3bbba206c0b298945
59 schema:genre chapter
60 schema:inLanguage en
61 schema:isAccessibleForFree true
62 schema:isPartOf N1046c24d82774c138286f06abaf06922
63 schema:name Predicting Secretory Proteins with SignalP
64 schema:pagination 59-73
65 schema:productId N25cb8d220aa64240b6388f2960042796
66 N2e4fea6aef3b4f3eab9fab75fd3a38dd
67 N4a5bcd20a2fc4a1787c9fe71a760d57f
68 N4a82064280ab42d783945e2d791cfbdd
69 schema:publisher Nc573e446a44c4d2ca1df912bbd23bad5
70 schema:sameAs https://app.dimensions.ai/details/publication/pub.1085083567
71 https://doi.org/10.1007/978-1-4939-7015-5_6
72 schema:sdDatePublished 2019-04-15T15:59
73 schema:sdLicense https://scigraph.springernature.com/explorer/license/
74 schema:sdPublisher N48b3d505ea1044658855fc4a98aa45dc
75 schema:url http://link.springer.com/10.1007/978-1-4939-7015-5_6
76 sgo:license sg:explorer/license/
77 sgo:sdDataset chapters
78 rdf:type schema:Chapter
79 N1046c24d82774c138286f06abaf06922 schema:isbn 978-1-4939-7013-1
80 978-1-4939-7015-5
81 schema:name Protein Function Prediction
82 rdf:type schema:Book
83 N1b017c9143b54033ae4a943b59714736 schema:familyName Kihara
84 schema:givenName Daisuke
85 rdf:type schema:Person
86 N1f85f8ef155e4ab99815c8ade83ad29c schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
87 schema:name Algorithms
88 rdf:type schema:DefinedTerm
89 N250608e34ba54d2ca58ca602eafb05ba schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
90 schema:name Proteins
91 rdf:type schema:DefinedTerm
92 N25cb8d220aa64240b6388f2960042796 schema:name doi
93 schema:value 10.1007/978-1-4939-7015-5_6
94 rdf:type schema:PropertyValue
95 N2e4fea6aef3b4f3eab9fab75fd3a38dd schema:name readcube_id
96 schema:value af3e75f8a3af844dc25e59fa88c1e1b7f3b648f10d0139ac2e6a200c42eee0e7
97 rdf:type schema:PropertyValue
98 N48b3d505ea1044658855fc4a98aa45dc schema:name Springer Nature - SN SciGraph project
99 rdf:type schema:Organization
100 N4a5bcd20a2fc4a1787c9fe71a760d57f schema:name pubmed_id
101 schema:value 28451972
102 rdf:type schema:PropertyValue
103 N4a82064280ab42d783945e2d791cfbdd schema:name dimensions_id
104 schema:value pub.1085083567
105 rdf:type schema:PropertyValue
106 N60c328c02c7c42a4bc855682662b7227 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
107 schema:name Protein Sorting Signals
108 rdf:type schema:DefinedTerm
109 N8a1cec92e6504322bfadd38f66da1460 rdf:first sg:person.07463377332.00
110 rdf:rest rdf:nil
111 Nb56d5d940e9a4cc3bbba206c0b298945 rdf:first N1b017c9143b54033ae4a943b59714736
112 rdf:rest rdf:nil
113 Nb5dea8ba2af945488ebe79d1aac2029e schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
114 schema:name Sequence Analysis, Protein
115 rdf:type schema:DefinedTerm
116 Nc573e446a44c4d2ca1df912bbd23bad5 schema:location New York, NY
117 schema:name Springer New York
118 rdf:type schema:Organisation
119 Ndbab6b9e1e55406dbe8567099181dbb9 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
120 schema:name Software
121 rdf:type schema:DefinedTerm
122 Nf6cea991661f4062b44b5e9753915434 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
123 schema:name Computational Biology
124 rdf:type schema:DefinedTerm
125 anzsrc-for:06 schema:inDefinedTermSet anzsrc-for:
126 schema:name Biological Sciences
127 rdf:type schema:DefinedTerm
128 anzsrc-for:0601 schema:inDefinedTermSet anzsrc-for:
129 schema:name Biochemistry and Cell Biology
130 rdf:type schema:DefinedTerm
131 sg:person.07463377332.00 schema:affiliation https://www.grid.ac/institutes/grid.5170.3
132 schema:familyName Nielsen
133 schema:givenName Henrik
134 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07463377332.00
135 rdf:type schema:Person
136 sg:pub.10.1007/bf00556363 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049459058
137 https://doi.org/10.1007/bf00556363
138 rdf:type schema:CreativeWork
139 sg:pub.10.1007/bf01868635 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013186929
140 https://doi.org/10.1007/bf01868635
141 rdf:type schema:CreativeWork
142 sg:pub.10.1038/nmeth.1701 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049894869
143 https://doi.org/10.1038/nmeth.1701
144 rdf:type schema:CreativeWork
145 sg:pub.10.1038/nmeth0410-248 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007489634
146 https://doi.org/10.1038/nmeth0410-248
147 rdf:type schema:CreativeWork
148 sg:pub.10.1038/nprot.2015.123 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021631236
149 https://doi.org/10.1038/nprot.2015.123
150 rdf:type schema:CreativeWork
151 sg:pub.10.1186/1471-2105-10-s15-s2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002399387
152 https://doi.org/10.1186/1471-2105-10-s15-s2
153 rdf:type schema:CreativeWork
154 sg:pub.10.1186/1471-2105-6-167 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044447875
155 https://doi.org/10.1186/1471-2105-6-167
156 rdf:type schema:CreativeWork
157 sg:pub.10.1186/1471-2105-6-256 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036012600
158 https://doi.org/10.1186/1471-2105-6-256
159 rdf:type schema:CreativeWork
160 sg:pub.10.1186/1471-2164-16-s8-s1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035513632
161 https://doi.org/10.1186/1471-2164-16-s8-s1
162 rdf:type schema:CreativeWork
163 sg:pub.10.2478/s11756-009-0118-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012945314
164 https://doi.org/10.2478/s11756-009-0118-3
165 rdf:type schema:CreativeWork
166 https://app.dimensions.ai/details/publication/pub.1083334413 schema:CreativeWork
167 https://doi.org/10.1002/(sici)1097-0134(199602)24:2<165::aid-prot4>3.0.co;2-i schema:sameAs https://app.dimensions.ai/details/publication/pub.1025817597
168 rdf:type schema:CreativeWork
169 https://doi.org/10.1002/humu.20798 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016431133
170 rdf:type schema:CreativeWork
171 https://doi.org/10.1002/pro.5560060601 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009133816
172 rdf:type schema:CreativeWork
173 https://doi.org/10.1006/jmbi.2000.3903 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028412590
174 rdf:type schema:CreativeWork
175 https://doi.org/10.1006/jmbi.2000.4315 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018016237
176 rdf:type schema:CreativeWork
177 https://doi.org/10.1016/0168-1702(85)90051-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034315067
178 rdf:type schema:CreativeWork
179 https://doi.org/10.1016/0304-4157(88)90013-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040026267
180 rdf:type schema:CreativeWork
181 https://doi.org/10.1016/0304-4157(96)00003-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026534679
182 rdf:type schema:CreativeWork
183 https://doi.org/10.1016/j.compbiolchem.2011.12.001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034058950
184 rdf:type schema:CreativeWork
185 https://doi.org/10.1016/j.jmb.2004.03.016 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021467321
186 rdf:type schema:CreativeWork
187 https://doi.org/10.1016/j.jmb.2004.05.028 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030482208
188 rdf:type schema:CreativeWork
189 https://doi.org/10.1016/s0092-8674(00)80443-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053683715
190 rdf:type schema:CreativeWork
191 https://doi.org/10.1016/s0092-8674(00)80444-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051562058
192 rdf:type schema:CreativeWork
193 https://doi.org/10.1083/jcb.105.6.2905 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018773283
194 rdf:type schema:CreativeWork
195 https://doi.org/10.1093/bioinformatics/16.8.741 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001205116
196 rdf:type schema:CreativeWork
197 https://doi.org/10.1093/bioinformatics/7.4.485 schema:sameAs https://app.dimensions.ai/details/publication/pub.1059414088
198 rdf:type schema:CreativeWork
199 https://doi.org/10.1093/bioinformatics/btn550 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014637543
200 rdf:type schema:CreativeWork
201 https://doi.org/10.1093/emboj/18.11.2982 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018166997
202 rdf:type schema:CreativeWork
203 https://doi.org/10.1093/nar/14.11.4683 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053470686
204 rdf:type schema:CreativeWork
205 https://doi.org/10.1093/nar/gkg509 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002198958
206 rdf:type schema:CreativeWork
207 https://doi.org/10.1093/protein/10.1.1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001804651
208 rdf:type schema:CreativeWork
209 https://doi.org/10.1093/protein/2.7.531 schema:sameAs https://app.dimensions.ai/details/publication/pub.1059980451
210 rdf:type schema:CreativeWork
211 https://doi.org/10.1110/ps.0303703 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032600631
212 rdf:type schema:CreativeWork
213 https://doi.org/10.1111/1462-2920.12105 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005371494
214 rdf:type schema:CreativeWork
215 https://doi.org/10.1111/j.1432-1033.1983.tb07424.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1030836600
216 rdf:type schema:CreativeWork
217 https://doi.org/10.1126/science.270.5235.397 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062551464
218 rdf:type schema:CreativeWork
219 https://doi.org/10.1126/science.285.5428.760 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017945219
220 rdf:type schema:CreativeWork
221 https://doi.org/10.1128/mbio.00339-12 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038778573
222 rdf:type schema:CreativeWork
223 https://doi.org/10.1139/m81-111 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048834333
224 rdf:type schema:CreativeWork
225 https://doi.org/10.1142/s0129065797000537 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062899929
226 rdf:type schema:CreativeWork
227 https://doi.org/10.1146/annurev-biochem-060614-034251 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029604447
228 rdf:type schema:CreativeWork
229 https://doi.org/10.1146/annurev.bi.57.070188.001441 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002620497
230 rdf:type schema:CreativeWork
231 https://doi.org/10.1371/journal.pcbi.1000213 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000141088
232 rdf:type schema:CreativeWork
233 https://doi.org/10.1371/journal.pone.0117380 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031777809
234 rdf:type schema:CreativeWork
235 https://www.grid.ac/institutes/grid.5170.3 schema:alternateName Technical University of Denmark
236 schema:name Department of Bio and Health Informatics, Technical University of Denmark
237 rdf:type schema:Organization
 




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


...