A knowledge-based method to predict the cooperative relationship between transcription factors View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2010-11

AUTHORS

Lingyi Lu, Ziliang Qian, XiaoHe Shi, Haipeng Li, Yu-Dong Cai, Yixue Li

ABSTRACT

Identifying the cooperation between transcription factors is crucial and challenging to uncover the mystery behind the complex gene expression patterns. Computational methods aimed to infer transcription factor cooperation are expected to get good results if we can integrate the knowledge (existed functional/structural annotations) of proteins. In this contribution, we proposed an information integrative computational framework to infer the cooperation between transcription factors, which relies on the hybridization-space method that can integrate the annotation information of proteins. In our computational experiments, by using function domain annotations only, on our testing dataset, the overall prediction accuracy and the specificity reaches 84.3% and 76.9%, respectively, which is a fairly good result and outperforms the prediction by both amino acid composition-based method and BLAST-based approach. The corresponding online service TFIPS (Transcription Factor Interaction Prediction System) is available on http://pcal.biosino.org/cgi-bin/TFIPS/TFIPS.pl. More... »

PAGES

815-819

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11030-009-9177-1

DOI

http://dx.doi.org/10.1007/s11030-009-9177-1

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1020130648

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/19590970


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/2008/0604", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Genetics", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/06", 
        "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": "Amino Acid Sequence", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Artificial Intelligence", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Binding Sites", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Binding, Competitive", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Computational Biology", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Drug Synergism", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Forecasting", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Knowledge Bases", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Protein Binding", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Structure-Activity Relationship", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Transcription Factors", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Transcriptional Activation", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Chinese Academy of Sciences", 
          "id": "https://www.grid.ac/institutes/grid.9227.e", 
          "name": [
            "Key Lab of Molecular Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yueyang Road, 200031, Shanghai, China", 
            "Graduate School of the Chinese Academy of Sciences, 19 Yuquan Road, 100039, Beijing, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Lu", 
        "givenName": "Lingyi", 
        "id": "sg:person.01101424411.04", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01101424411.04"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Chinese Academy of Sciences", 
          "id": "https://www.grid.ac/institutes/grid.9227.e", 
          "name": [
            "Key Lab of Molecular Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yueyang Road, 200031, Shanghai, China", 
            "Graduate School of the Chinese Academy of Sciences, 19 Yuquan Road, 100039, Beijing, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Qian", 
        "givenName": "Ziliang", 
        "id": "sg:person.01147537611.46", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01147537611.46"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Chinese Academy of Sciences", 
          "id": "https://www.grid.ac/institutes/grid.9227.e", 
          "name": [
            "Institute of Health Science Shanghai Institute for Biological Science, Chinese Academy of Science, 225 South ChongQing Road, 200025, Shanghai, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Shi", 
        "givenName": "XiaoHe", 
        "id": "sg:person.01006623757.23", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01006623757.23"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Shanghai Institutes for Biological Sciences", 
          "id": "https://www.grid.ac/institutes/grid.419092.7", 
          "name": [
            "CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 200031, Shanghai, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Li", 
        "givenName": "Haipeng", 
        "id": "sg:person.0774565071.50", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0774565071.50"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Fudan University", 
          "id": "https://www.grid.ac/institutes/grid.8547.e", 
          "name": [
            "Institute of System Biology, Shanghai University, 99 ShangDa Road, 200244, Shanghai, China", 
            "Centre for Computational Systems Biology, Fudan University, 220 HanDan Road, 200433, Shanghai, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Cai", 
        "givenName": "Yu-Dong", 
        "id": "sg:person.01344714423.17", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01344714423.17"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Shanghai Center For Bioinformation Technology", 
          "id": "https://www.grid.ac/institutes/grid.58095.31", 
          "name": [
            "Key Lab of Molecular Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yueyang Road, 200031, Shanghai, China", 
            "Shanghai Center for Bioinformation Technology, 100 Qinzhou Road, 200235, Shanghai, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Li", 
        "givenName": "Yixue", 
        "id": "sg:person.012163147207.05", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012163147207.05"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1093/nar/gki793", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000212820"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0960-9822(95)00151-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000796479"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2105-7-187", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003944654", 
          "https://doi.org/10.1186/1471-2105-7-187"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1128/mcb.10.4.1609", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1007601258"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.bbrc.2006.06.060", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1007696037"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.bbrc.2006.07.149", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1007823956"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jtbi.2005.05.035", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009486255"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jtbi.2005.05.035", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009486255"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/nar/gkg600", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010724185"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/gb-2004-5-8-r56", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010980355", 
          "https://doi.org/10.1186/gb-2004-5-8-r56"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0955-0674(97)80068-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011342727"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0022-2836(05)80360-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013618994"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1128/mcb.18.12.7020", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020326709"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0006-291x(03)00775-7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022038392"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/sj.onc.1204239", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024115588", 
          "https://doi.org/10.1038/sj.onc.1204239"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/sj.onc.1204239", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024115588", 
          "https://doi.org/10.1038/sj.onc.1204239"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.compbiolchem.2007.03.008", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026505462"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.bbrc.2004.08.113", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028017467"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/pr049835p", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028782603"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/nar/gkj487", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1029169351"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/nar/24.1.238", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031417336"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/nar/gkj143", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031585631"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1128/mcb.01344-08", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1033604762"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/17.9.847", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035069268"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/bth085", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036597443"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bib/3.3.225", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039314615"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1006/abio.1998.2876", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041156123"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/nar/gki442", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042584609"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/bth054", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051906788"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/pr050331g", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1056290845"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1089/104454902320308924", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1059203708"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1078360881", 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2010-11", 
    "datePublishedReg": "2010-11-01", 
    "description": "Identifying the cooperation between transcription factors is crucial and challenging to uncover the mystery behind the complex gene expression patterns. Computational methods aimed to infer transcription factor cooperation are expected to get good results if we can integrate the knowledge (existed functional/structural annotations) of proteins. In this contribution, we proposed an information integrative computational framework to infer the cooperation between transcription factors, which relies on the hybridization-space method that can integrate the annotation information of proteins. In our computational experiments, by using function domain annotations only, on our testing dataset, the overall prediction accuracy and the specificity reaches 84.3% and 76.9%, respectively, which is a fairly good result and outperforms the prediction by both amino acid composition-based method and BLAST-based approach. The corresponding online service TFIPS (Transcription Factor Interaction Prediction System) is available on http://pcal.biosino.org/cgi-bin/TFIPS/TFIPS.pl.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s11030-009-9177-1", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1114785", 
        "issn": [
          "1381-1991", 
          "1573-501X"
        ], 
        "name": "Molecular Diversity", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "4", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "14"
      }
    ], 
    "name": "A knowledge-based method to predict the cooperative relationship between transcription factors", 
    "pagination": "815-819", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "5fea6e9a54311c09e570530c1b5f24e4f8a0c0b8ccd809bae1aef33b79605058"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "19590970"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "9516534"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s11030-009-9177-1"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1020130648"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s11030-009-9177-1", 
      "https://app.dimensions.ai/details/publication/pub.1020130648"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T09:24", 
    "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/v90/0000000345_0000000345/records_64100_00000000.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1007%2Fs11030-009-9177-1"
  }
]
 

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/s11030-009-9177-1'

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/s11030-009-9177-1'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s11030-009-9177-1'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s11030-009-9177-1'


 

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

261 TRIPLES      21 PREDICATES      72 URIs      34 LITERALS      22 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s11030-009-9177-1 schema:about N028383bc82094e1696987047866b3d5f
2 N0b1cc0a6954c4b0b96720c2aaa652628
3 N232c62d31cd940afabc39401273d39f3
4 N28f80f363d0d40908f827c4beda97352
5 N4047a7bcd9df403a81c08ed2e762b565
6 N83388585894442d0a0ebb4f732af9169
7 N913931beccab4142adcf806ed42f41f4
8 Na7cfe50f6f2043359ff5a8eaa993868f
9 Naf1c81b0608147e6949fe014363cba8c
10 Nd14958037fc14999a1de7200479cef1e
11 Nd416ff5712b24a3088fbb38593a49951
12 Nd90b1dffca0341d8a2cb8cae401aada2
13 Ne39736f9791146ada509465b91bbdd53
14 anzsrc-for:06
15 anzsrc-for:0604
16 schema:author N1addb9aedff24ed5bd0031993fe9ea32
17 schema:citation sg:pub.10.1038/sj.onc.1204239
18 sg:pub.10.1186/1471-2105-7-187
19 sg:pub.10.1186/gb-2004-5-8-r56
20 https://app.dimensions.ai/details/publication/pub.1078360881
21 https://doi.org/10.1006/abio.1998.2876
22 https://doi.org/10.1016/j.bbrc.2004.08.113
23 https://doi.org/10.1016/j.bbrc.2006.06.060
24 https://doi.org/10.1016/j.bbrc.2006.07.149
25 https://doi.org/10.1016/j.compbiolchem.2007.03.008
26 https://doi.org/10.1016/j.jtbi.2005.05.035
27 https://doi.org/10.1016/s0006-291x(03)00775-7
28 https://doi.org/10.1016/s0022-2836(05)80360-2
29 https://doi.org/10.1016/s0955-0674(97)80068-3
30 https://doi.org/10.1016/s0960-9822(95)00151-5
31 https://doi.org/10.1021/pr049835p
32 https://doi.org/10.1021/pr050331g
33 https://doi.org/10.1089/104454902320308924
34 https://doi.org/10.1093/bib/3.3.225
35 https://doi.org/10.1093/bioinformatics/17.9.847
36 https://doi.org/10.1093/bioinformatics/bth054
37 https://doi.org/10.1093/bioinformatics/bth085
38 https://doi.org/10.1093/nar/24.1.238
39 https://doi.org/10.1093/nar/gkg600
40 https://doi.org/10.1093/nar/gki442
41 https://doi.org/10.1093/nar/gki793
42 https://doi.org/10.1093/nar/gkj143
43 https://doi.org/10.1093/nar/gkj487
44 https://doi.org/10.1128/mcb.01344-08
45 https://doi.org/10.1128/mcb.10.4.1609
46 https://doi.org/10.1128/mcb.18.12.7020
47 schema:datePublished 2010-11
48 schema:datePublishedReg 2010-11-01
49 schema:description Identifying the cooperation between transcription factors is crucial and challenging to uncover the mystery behind the complex gene expression patterns. Computational methods aimed to infer transcription factor cooperation are expected to get good results if we can integrate the knowledge (existed functional/structural annotations) of proteins. In this contribution, we proposed an information integrative computational framework to infer the cooperation between transcription factors, which relies on the hybridization-space method that can integrate the annotation information of proteins. In our computational experiments, by using function domain annotations only, on our testing dataset, the overall prediction accuracy and the specificity reaches 84.3% and 76.9%, respectively, which is a fairly good result and outperforms the prediction by both amino acid composition-based method and BLAST-based approach. The corresponding online service TFIPS (Transcription Factor Interaction Prediction System) is available on http://pcal.biosino.org/cgi-bin/TFIPS/TFIPS.pl.
50 schema:genre research_article
51 schema:inLanguage en
52 schema:isAccessibleForFree false
53 schema:isPartOf N8971f5a6e2b3423ab7b0c7e21c59c1a5
54 Nc9fdabde2ae4405c914c3426ac9937d1
55 sg:journal.1114785
56 schema:name A knowledge-based method to predict the cooperative relationship between transcription factors
57 schema:pagination 815-819
58 schema:productId N0b72ba7a22574b49b7be656f9b89a11b
59 N1020ae51cc764a5bbb6c94c882c9fd9d
60 N4cb50f8c67a1442892fb382d204e42d3
61 Nc6f3772508e24e84988e1d3d38c641bb
62 Ne8b3db65039f44e0986877aea4da1ff3
63 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020130648
64 https://doi.org/10.1007/s11030-009-9177-1
65 schema:sdDatePublished 2019-04-11T09:24
66 schema:sdLicense https://scigraph.springernature.com/explorer/license/
67 schema:sdPublisher N80d063afc60c47c6b168b7d7cf662895
68 schema:url http://link.springer.com/10.1007%2Fs11030-009-9177-1
69 sgo:license sg:explorer/license/
70 sgo:sdDataset articles
71 rdf:type schema:ScholarlyArticle
72 N028383bc82094e1696987047866b3d5f schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
73 schema:name Binding, Competitive
74 rdf:type schema:DefinedTerm
75 N08beab6a39f84be1bba7dc9263016fd1 rdf:first sg:person.012163147207.05
76 rdf:rest rdf:nil
77 N0b1cc0a6954c4b0b96720c2aaa652628 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
78 schema:name Structure-Activity Relationship
79 rdf:type schema:DefinedTerm
80 N0b72ba7a22574b49b7be656f9b89a11b schema:name doi
81 schema:value 10.1007/s11030-009-9177-1
82 rdf:type schema:PropertyValue
83 N1020ae51cc764a5bbb6c94c882c9fd9d schema:name pubmed_id
84 schema:value 19590970
85 rdf:type schema:PropertyValue
86 N117b7696630e478dbfbdfb91b9f21f6b rdf:first sg:person.0774565071.50
87 rdf:rest Nf6f6e10ed7cc4a2e8ef573bc58c0dfae
88 N1addb9aedff24ed5bd0031993fe9ea32 rdf:first sg:person.01101424411.04
89 rdf:rest N9368aaa973b24a249c16f78774b271b5
90 N232c62d31cd940afabc39401273d39f3 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
91 schema:name Computational Biology
92 rdf:type schema:DefinedTerm
93 N28f80f363d0d40908f827c4beda97352 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
94 schema:name Amino Acid Sequence
95 rdf:type schema:DefinedTerm
96 N4047a7bcd9df403a81c08ed2e762b565 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
97 schema:name Algorithms
98 rdf:type schema:DefinedTerm
99 N4cb50f8c67a1442892fb382d204e42d3 schema:name dimensions_id
100 schema:value pub.1020130648
101 rdf:type schema:PropertyValue
102 N80d063afc60c47c6b168b7d7cf662895 schema:name Springer Nature - SN SciGraph project
103 rdf:type schema:Organization
104 N83388585894442d0a0ebb4f732af9169 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
105 schema:name Drug Synergism
106 rdf:type schema:DefinedTerm
107 N878ecd0dfa3341cfbc0717f0c496b52d rdf:first sg:person.01006623757.23
108 rdf:rest N117b7696630e478dbfbdfb91b9f21f6b
109 N8971f5a6e2b3423ab7b0c7e21c59c1a5 schema:issueNumber 4
110 rdf:type schema:PublicationIssue
111 N913931beccab4142adcf806ed42f41f4 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
112 schema:name Forecasting
113 rdf:type schema:DefinedTerm
114 N9368aaa973b24a249c16f78774b271b5 rdf:first sg:person.01147537611.46
115 rdf:rest N878ecd0dfa3341cfbc0717f0c496b52d
116 Na7cfe50f6f2043359ff5a8eaa993868f schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
117 schema:name Transcriptional Activation
118 rdf:type schema:DefinedTerm
119 Naf1c81b0608147e6949fe014363cba8c schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
120 schema:name Transcription Factors
121 rdf:type schema:DefinedTerm
122 Nc6f3772508e24e84988e1d3d38c641bb schema:name readcube_id
123 schema:value 5fea6e9a54311c09e570530c1b5f24e4f8a0c0b8ccd809bae1aef33b79605058
124 rdf:type schema:PropertyValue
125 Nc9fdabde2ae4405c914c3426ac9937d1 schema:volumeNumber 14
126 rdf:type schema:PublicationVolume
127 Nd14958037fc14999a1de7200479cef1e schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
128 schema:name Knowledge Bases
129 rdf:type schema:DefinedTerm
130 Nd416ff5712b24a3088fbb38593a49951 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
131 schema:name Binding Sites
132 rdf:type schema:DefinedTerm
133 Nd90b1dffca0341d8a2cb8cae401aada2 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
134 schema:name Protein Binding
135 rdf:type schema:DefinedTerm
136 Ne39736f9791146ada509465b91bbdd53 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
137 schema:name Artificial Intelligence
138 rdf:type schema:DefinedTerm
139 Ne8b3db65039f44e0986877aea4da1ff3 schema:name nlm_unique_id
140 schema:value 9516534
141 rdf:type schema:PropertyValue
142 Nf6f6e10ed7cc4a2e8ef573bc58c0dfae rdf:first sg:person.01344714423.17
143 rdf:rest N08beab6a39f84be1bba7dc9263016fd1
144 anzsrc-for:06 schema:inDefinedTermSet anzsrc-for:
145 schema:name Biological Sciences
146 rdf:type schema:DefinedTerm
147 anzsrc-for:0604 schema:inDefinedTermSet anzsrc-for:
148 schema:name Genetics
149 rdf:type schema:DefinedTerm
150 sg:journal.1114785 schema:issn 1381-1991
151 1573-501X
152 schema:name Molecular Diversity
153 rdf:type schema:Periodical
154 sg:person.01006623757.23 schema:affiliation https://www.grid.ac/institutes/grid.9227.e
155 schema:familyName Shi
156 schema:givenName XiaoHe
157 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01006623757.23
158 rdf:type schema:Person
159 sg:person.01101424411.04 schema:affiliation https://www.grid.ac/institutes/grid.9227.e
160 schema:familyName Lu
161 schema:givenName Lingyi
162 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01101424411.04
163 rdf:type schema:Person
164 sg:person.01147537611.46 schema:affiliation https://www.grid.ac/institutes/grid.9227.e
165 schema:familyName Qian
166 schema:givenName Ziliang
167 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01147537611.46
168 rdf:type schema:Person
169 sg:person.012163147207.05 schema:affiliation https://www.grid.ac/institutes/grid.58095.31
170 schema:familyName Li
171 schema:givenName Yixue
172 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012163147207.05
173 rdf:type schema:Person
174 sg:person.01344714423.17 schema:affiliation https://www.grid.ac/institutes/grid.8547.e
175 schema:familyName Cai
176 schema:givenName Yu-Dong
177 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01344714423.17
178 rdf:type schema:Person
179 sg:person.0774565071.50 schema:affiliation https://www.grid.ac/institutes/grid.419092.7
180 schema:familyName Li
181 schema:givenName Haipeng
182 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0774565071.50
183 rdf:type schema:Person
184 sg:pub.10.1038/sj.onc.1204239 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024115588
185 https://doi.org/10.1038/sj.onc.1204239
186 rdf:type schema:CreativeWork
187 sg:pub.10.1186/1471-2105-7-187 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003944654
188 https://doi.org/10.1186/1471-2105-7-187
189 rdf:type schema:CreativeWork
190 sg:pub.10.1186/gb-2004-5-8-r56 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010980355
191 https://doi.org/10.1186/gb-2004-5-8-r56
192 rdf:type schema:CreativeWork
193 https://app.dimensions.ai/details/publication/pub.1078360881 schema:CreativeWork
194 https://doi.org/10.1006/abio.1998.2876 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041156123
195 rdf:type schema:CreativeWork
196 https://doi.org/10.1016/j.bbrc.2004.08.113 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028017467
197 rdf:type schema:CreativeWork
198 https://doi.org/10.1016/j.bbrc.2006.06.060 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007696037
199 rdf:type schema:CreativeWork
200 https://doi.org/10.1016/j.bbrc.2006.07.149 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007823956
201 rdf:type schema:CreativeWork
202 https://doi.org/10.1016/j.compbiolchem.2007.03.008 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026505462
203 rdf:type schema:CreativeWork
204 https://doi.org/10.1016/j.jtbi.2005.05.035 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009486255
205 rdf:type schema:CreativeWork
206 https://doi.org/10.1016/s0006-291x(03)00775-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022038392
207 rdf:type schema:CreativeWork
208 https://doi.org/10.1016/s0022-2836(05)80360-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013618994
209 rdf:type schema:CreativeWork
210 https://doi.org/10.1016/s0955-0674(97)80068-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011342727
211 rdf:type schema:CreativeWork
212 https://doi.org/10.1016/s0960-9822(95)00151-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000796479
213 rdf:type schema:CreativeWork
214 https://doi.org/10.1021/pr049835p schema:sameAs https://app.dimensions.ai/details/publication/pub.1028782603
215 rdf:type schema:CreativeWork
216 https://doi.org/10.1021/pr050331g schema:sameAs https://app.dimensions.ai/details/publication/pub.1056290845
217 rdf:type schema:CreativeWork
218 https://doi.org/10.1089/104454902320308924 schema:sameAs https://app.dimensions.ai/details/publication/pub.1059203708
219 rdf:type schema:CreativeWork
220 https://doi.org/10.1093/bib/3.3.225 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039314615
221 rdf:type schema:CreativeWork
222 https://doi.org/10.1093/bioinformatics/17.9.847 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035069268
223 rdf:type schema:CreativeWork
224 https://doi.org/10.1093/bioinformatics/bth054 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051906788
225 rdf:type schema:CreativeWork
226 https://doi.org/10.1093/bioinformatics/bth085 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036597443
227 rdf:type schema:CreativeWork
228 https://doi.org/10.1093/nar/24.1.238 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031417336
229 rdf:type schema:CreativeWork
230 https://doi.org/10.1093/nar/gkg600 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010724185
231 rdf:type schema:CreativeWork
232 https://doi.org/10.1093/nar/gki442 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042584609
233 rdf:type schema:CreativeWork
234 https://doi.org/10.1093/nar/gki793 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000212820
235 rdf:type schema:CreativeWork
236 https://doi.org/10.1093/nar/gkj143 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031585631
237 rdf:type schema:CreativeWork
238 https://doi.org/10.1093/nar/gkj487 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029169351
239 rdf:type schema:CreativeWork
240 https://doi.org/10.1128/mcb.01344-08 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033604762
241 rdf:type schema:CreativeWork
242 https://doi.org/10.1128/mcb.10.4.1609 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007601258
243 rdf:type schema:CreativeWork
244 https://doi.org/10.1128/mcb.18.12.7020 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020326709
245 rdf:type schema:CreativeWork
246 https://www.grid.ac/institutes/grid.419092.7 schema:alternateName Shanghai Institutes for Biological Sciences
247 schema:name CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 200031, Shanghai, China
248 rdf:type schema:Organization
249 https://www.grid.ac/institutes/grid.58095.31 schema:alternateName Shanghai Center For Bioinformation Technology
250 schema:name Key Lab of Molecular Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yueyang Road, 200031, Shanghai, China
251 Shanghai Center for Bioinformation Technology, 100 Qinzhou Road, 200235, Shanghai, China
252 rdf:type schema:Organization
253 https://www.grid.ac/institutes/grid.8547.e schema:alternateName Fudan University
254 schema:name Centre for Computational Systems Biology, Fudan University, 220 HanDan Road, 200433, Shanghai, China
255 Institute of System Biology, Shanghai University, 99 ShangDa Road, 200244, Shanghai, China
256 rdf:type schema:Organization
257 https://www.grid.ac/institutes/grid.9227.e schema:alternateName Chinese Academy of Sciences
258 schema:name Graduate School of the Chinese Academy of Sciences, 19 Yuquan Road, 100039, Beijing, China
259 Institute of Health Science Shanghai Institute for Biological Science, Chinese Academy of Science, 225 South ChongQing Road, 200025, Shanghai, China
260 Key Lab of Molecular Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yueyang Road, 200031, Shanghai, China
261 rdf:type schema:Organization
 




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


...