A novel sequence-based method of predicting protein DNA-binding residues, using a machine learning approach View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2010-08

AUTHORS

Yudong Cai, ZhiSong He, Xiaohe Shi, Xiangying Kong, Lei Gu, Lu Xie

ABSTRACT

Protein-DNA interactions play an essential role in transcriptional regulation, DNA repair, and many vital biological processes. The mechanism of protein-DNA binding, however, remains unclear. For the study of many diseases, researchers must improve their understanding of the amino acid motifs that recognize DNA. Because identifying these motifs experimentally is expensive and time-consuming, it is necessary to devise an approach for computational prediction. Some in silico methods have been developed, but there are still considerable limitations. In this study, we used a machine learning approach to develop a new sequence-based method of predicting protein-DNA binding residues. To make these predictions, we used the properties of the micro-environment of each amino acid from the AAIndex as well as conservation scores. Testing by the cross-validation method, we obtained an overall accuracy of 94.89%. Our method shows that the amino acid micro-environment is important for DNA binding, and that it is possible to identify the protein-DNA binding sites with it. More... »

PAGES

99-105

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10059-010-0093-0

DOI

http://dx.doi.org/10.1007/s10059-010-0093-0

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1021300871

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/20706794


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": "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": "DNA", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "DNA-Binding Proteins", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Protein Binding", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Sequence Analysis, Protein", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Fudan University", 
          "id": "https://www.grid.ac/institutes/grid.8547.e", 
          "name": [
            "Institute of System Biology, Shanghai University, 200244, Shanghai, People\u2019s Republic of China", 
            "Centre for Computational Systems Biology, Fudan University, 200433, Shanghai, People\u2019s Republic of China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Cai", 
        "givenName": "Yudong", 
        "id": "sg:person.01344714423.17", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01344714423.17"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Zhejiang University", 
          "id": "https://www.grid.ac/institutes/grid.13402.34", 
          "name": [
            "Department of Bioinformatics, College of Life Sciences, Zhejiang University, 310058, ZheJiang, People\u2019s Republic of China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "He", 
        "givenName": "ZhiSong", 
        "id": "sg:person.01254504347.43", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01254504347.43"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Institute of Health Sciences", 
          "id": "https://www.grid.ac/institutes/grid.452350.5", 
          "name": [
            "Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences (CAS) and Shanghai Jiao Tong University School of Medicine, Shanghai, People\u2019s Republic of 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 Jiao Tong University", 
          "id": "https://www.grid.ac/institutes/grid.16821.3c", 
          "name": [
            "Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences (CAS) and Shanghai Jiao Tong University School of Medicine, Shanghai, People\u2019s Republic of China", 
            "State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiaotong University, 200025, Shanghai, People\u2019s Republic of China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kong", 
        "givenName": "Xiangying", 
        "id": "sg:person.01245221165.79", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01245221165.79"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Fraunhofer Institute for Algorithms and Scientific Computing", 
          "id": "https://www.grid.ac/institutes/grid.418688.b", 
          "name": [
            "Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Bonn, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Gu", 
        "givenName": "Lei", 
        "id": "sg:person.01160322451.91", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01160322451.91"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Shanghai Center For Bioinformation Technology", 
          "id": "https://www.grid.ac/institutes/grid.58095.31", 
          "name": [
            "Shanghai Center for Bioinformation Technology, 200235, Shanghai, People\u2019s Republic of China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Xie", 
        "givenName": "Lu", 
        "id": "sg:person.01050126306.16", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01050126306.16"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1093/nar/gkn589", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1001177930"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.gene.2005.07.022", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005572322"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/nar/gkn332", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006899310"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.gde.2005.05.002", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1007231307"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.gde.2005.05.002", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1007231307"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/btm404", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1007683223"
        ], 
        "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/10.1093/bioinformatics/btg432", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009866113"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/btl672", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010270206"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.cell.2008.05.023", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010946240"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/35094077", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015264155", 
          "https://doi.org/10.1038/35094077"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/35094077", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015264155", 
          "https://doi.org/10.1038/35094077"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1074/jbc.m710539200", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017785340"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/gb-2000-1-1-reviews001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017829910", 
          "https://doi.org/10.1186/gb-2000-1-1-reviews001"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/btn583", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018060228"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/btm174", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018894529"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1371/journal.pcbi.0010001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019254281"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/bti423", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024267147"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/btm299", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032460550"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.biosystems.2006.08.007", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034198460"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/nar/28.1.235", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035055456"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1006/jmbi.1997.0958", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038197633"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/prot.10146", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041509798"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/nar/gkn866", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042530703"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1074/jbc.m411443200", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046136432"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nmeth.1188", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046746398", 
          "https://doi.org/10.1038/nmeth.1188"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/nar/25.17.3389", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047265454"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1101/gr.3069205", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049120726"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.cbpa.2003.11.001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049613524"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/btg1040", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049915428"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s1097-2765(01)00352-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051882977"
        ], 
        "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/10.1021/pr800717y", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1056294600"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tpami.2005.159", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061742820"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tpami.2005.159", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061742820"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1142/s0219720006002387", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1063004727"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2010-08", 
    "datePublishedReg": "2010-08-01", 
    "description": "Protein-DNA interactions play an essential role in transcriptional regulation, DNA repair, and many vital biological processes. The mechanism of protein-DNA binding, however, remains unclear. For the study of many diseases, researchers must improve their understanding of the amino acid motifs that recognize DNA. Because identifying these motifs experimentally is expensive and time-consuming, it is necessary to devise an approach for computational prediction. Some in silico methods have been developed, but there are still considerable limitations. In this study, we used a machine learning approach to develop a new sequence-based method of predicting protein-DNA binding residues. To make these predictions, we used the properties of the micro-environment of each amino acid from the AAIndex as well as conservation scores. Testing by the cross-validation method, we obtained an overall accuracy of 94.89%. Our method shows that the amino acid micro-environment is important for DNA binding, and that it is possible to identify the protein-DNA binding sites with it.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s10059-010-0093-0", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1115754", 
        "issn": [
          "1016-8478", 
          "0219-1032"
        ], 
        "name": "Molecules and Cells", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "2", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "30"
      }
    ], 
    "name": "A novel sequence-based method of predicting protein DNA-binding residues, using a machine learning approach", 
    "pagination": "99-105", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "657ce75513f120eabcba97b3287400205f03ece2c360baf64f83caadb333b29c"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "20706794"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "9610936"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s10059-010-0093-0"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1021300871"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s10059-010-0093-0", 
      "https://app.dimensions.ai/details/publication/pub.1021300871"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T10:48", 
    "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/v90/0000000350_0000000350/records_77548_00000000.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1007/s10059-010-0093-0"
  }
]
 

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/s10059-010-0093-0'

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/s10059-010-0093-0'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s10059-010-0093-0'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s10059-010-0093-0'


 

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

247 TRIPLES      21 PREDICATES      68 URIs      27 LITERALS      15 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s10059-010-0093-0 schema:about N16925141a69c4e47842fd0bebed01470
2 N8e8a036613434b579596c9fda338aa6f
3 N9335f25fe7ab42ac84680e38875d6f7b
4 Nad59b0f428374cfaac34a66690e804ce
5 Nb2649fb0d57a48e4a3571fb5d4fc12ba
6 Nbd9120ee83cf4e10a71c1c9b6c0ac570
7 anzsrc-for:06
8 anzsrc-for:0601
9 schema:author N2997d75dfa8547b68e5bdfa2b85da453
10 schema:citation sg:pub.10.1038/35094077
11 sg:pub.10.1038/nmeth.1188
12 sg:pub.10.1186/gb-2000-1-1-reviews001
13 https://doi.org/10.1002/prot.10146
14 https://doi.org/10.1006/jmbi.1997.0958
15 https://doi.org/10.1016/j.bbrc.2006.07.149
16 https://doi.org/10.1016/j.biosystems.2006.08.007
17 https://doi.org/10.1016/j.cbpa.2003.11.001
18 https://doi.org/10.1016/j.cell.2008.05.023
19 https://doi.org/10.1016/j.gde.2005.05.002
20 https://doi.org/10.1016/j.gene.2005.07.022
21 https://doi.org/10.1016/s1097-2765(01)00352-5
22 https://doi.org/10.1021/pr800717y
23 https://doi.org/10.1074/jbc.m411443200
24 https://doi.org/10.1074/jbc.m710539200
25 https://doi.org/10.1093/bioinformatics/btg1040
26 https://doi.org/10.1093/bioinformatics/btg432
27 https://doi.org/10.1093/bioinformatics/bti423
28 https://doi.org/10.1093/bioinformatics/btl672
29 https://doi.org/10.1093/bioinformatics/btm174
30 https://doi.org/10.1093/bioinformatics/btm299
31 https://doi.org/10.1093/bioinformatics/btm404
32 https://doi.org/10.1093/bioinformatics/btn583
33 https://doi.org/10.1093/nar/25.17.3389
34 https://doi.org/10.1093/nar/28.1.235
35 https://doi.org/10.1093/nar/gkm259
36 https://doi.org/10.1093/nar/gkn332
37 https://doi.org/10.1093/nar/gkn589
38 https://doi.org/10.1093/nar/gkn866
39 https://doi.org/10.1101/gr.3069205
40 https://doi.org/10.1109/tpami.2005.159
41 https://doi.org/10.1142/s0219720006002387
42 https://doi.org/10.1371/journal.pcbi.0010001
43 schema:datePublished 2010-08
44 schema:datePublishedReg 2010-08-01
45 schema:description Protein-DNA interactions play an essential role in transcriptional regulation, DNA repair, and many vital biological processes. The mechanism of protein-DNA binding, however, remains unclear. For the study of many diseases, researchers must improve their understanding of the amino acid motifs that recognize DNA. Because identifying these motifs experimentally is expensive and time-consuming, it is necessary to devise an approach for computational prediction. Some in silico methods have been developed, but there are still considerable limitations. In this study, we used a machine learning approach to develop a new sequence-based method of predicting protein-DNA binding residues. To make these predictions, we used the properties of the micro-environment of each amino acid from the AAIndex as well as conservation scores. Testing by the cross-validation method, we obtained an overall accuracy of 94.89%. Our method shows that the amino acid micro-environment is important for DNA binding, and that it is possible to identify the protein-DNA binding sites with it.
46 schema:genre research_article
47 schema:inLanguage en
48 schema:isAccessibleForFree false
49 schema:isPartOf N58ed07674cc14c7a8fdf5c5d43308436
50 N88457354d8334cd0b275f2d64f6fa1ec
51 sg:journal.1115754
52 schema:name A novel sequence-based method of predicting protein DNA-binding residues, using a machine learning approach
53 schema:pagination 99-105
54 schema:productId N1239310b2b384e6ea6a4a1ef9e89e252
55 N203c592df1d94fe29704d7384515edd6
56 N3acdede172da4775a8251f75081b9389
57 Nb3c18bf677314b7d8755664e7f65228e
58 Ne9a405507eae44a7a905a6f46b94175e
59 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021300871
60 https://doi.org/10.1007/s10059-010-0093-0
61 schema:sdDatePublished 2019-04-11T10:48
62 schema:sdLicense https://scigraph.springernature.com/explorer/license/
63 schema:sdPublisher Ncdce8f5702e14dcf8a3d7b3977076b41
64 schema:url http://link.springer.com/10.1007/s10059-010-0093-0
65 sgo:license sg:explorer/license/
66 sgo:sdDataset articles
67 rdf:type schema:ScholarlyArticle
68 N1239310b2b384e6ea6a4a1ef9e89e252 schema:name dimensions_id
69 schema:value pub.1021300871
70 rdf:type schema:PropertyValue
71 N16925141a69c4e47842fd0bebed01470 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
72 schema:name Sequence Analysis, Protein
73 rdf:type schema:DefinedTerm
74 N198cf92c66754a72916764156a393171 rdf:first sg:person.01006623757.23
75 rdf:rest N515bd240d914463fa0e209ab8c29679f
76 N203c592df1d94fe29704d7384515edd6 schema:name doi
77 schema:value 10.1007/s10059-010-0093-0
78 rdf:type schema:PropertyValue
79 N2997d75dfa8547b68e5bdfa2b85da453 rdf:first sg:person.01344714423.17
80 rdf:rest Naa92f858e7024348b78883f778d2af9e
81 N3acdede172da4775a8251f75081b9389 schema:name pubmed_id
82 schema:value 20706794
83 rdf:type schema:PropertyValue
84 N515bd240d914463fa0e209ab8c29679f rdf:first sg:person.01245221165.79
85 rdf:rest Nef89425bd3b1483fbd6cb80db7e98a57
86 N58ed07674cc14c7a8fdf5c5d43308436 schema:volumeNumber 30
87 rdf:type schema:PublicationVolume
88 N88457354d8334cd0b275f2d64f6fa1ec schema:issueNumber 2
89 rdf:type schema:PublicationIssue
90 N8e8a036613434b579596c9fda338aa6f schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
91 schema:name Protein Binding
92 rdf:type schema:DefinedTerm
93 N9335f25fe7ab42ac84680e38875d6f7b schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
94 schema:name DNA-Binding Proteins
95 rdf:type schema:DefinedTerm
96 N9a67d8359cc34f08a248d51da479fdcf rdf:first sg:person.01050126306.16
97 rdf:rest rdf:nil
98 Naa92f858e7024348b78883f778d2af9e rdf:first sg:person.01254504347.43
99 rdf:rest N198cf92c66754a72916764156a393171
100 Nad59b0f428374cfaac34a66690e804ce schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
101 schema:name DNA
102 rdf:type schema:DefinedTerm
103 Nb2649fb0d57a48e4a3571fb5d4fc12ba schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
104 schema:name Artificial Intelligence
105 rdf:type schema:DefinedTerm
106 Nb3c18bf677314b7d8755664e7f65228e schema:name readcube_id
107 schema:value 657ce75513f120eabcba97b3287400205f03ece2c360baf64f83caadb333b29c
108 rdf:type schema:PropertyValue
109 Nbd9120ee83cf4e10a71c1c9b6c0ac570 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
110 schema:name Amino Acid Sequence
111 rdf:type schema:DefinedTerm
112 Ncdce8f5702e14dcf8a3d7b3977076b41 schema:name Springer Nature - SN SciGraph project
113 rdf:type schema:Organization
114 Ne9a405507eae44a7a905a6f46b94175e schema:name nlm_unique_id
115 schema:value 9610936
116 rdf:type schema:PropertyValue
117 Nef89425bd3b1483fbd6cb80db7e98a57 rdf:first sg:person.01160322451.91
118 rdf:rest N9a67d8359cc34f08a248d51da479fdcf
119 anzsrc-for:06 schema:inDefinedTermSet anzsrc-for:
120 schema:name Biological Sciences
121 rdf:type schema:DefinedTerm
122 anzsrc-for:0601 schema:inDefinedTermSet anzsrc-for:
123 schema:name Biochemistry and Cell Biology
124 rdf:type schema:DefinedTerm
125 sg:journal.1115754 schema:issn 0219-1032
126 1016-8478
127 schema:name Molecules and Cells
128 rdf:type schema:Periodical
129 sg:person.01006623757.23 schema:affiliation https://www.grid.ac/institutes/grid.452350.5
130 schema:familyName Shi
131 schema:givenName Xiaohe
132 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01006623757.23
133 rdf:type schema:Person
134 sg:person.01050126306.16 schema:affiliation https://www.grid.ac/institutes/grid.58095.31
135 schema:familyName Xie
136 schema:givenName Lu
137 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01050126306.16
138 rdf:type schema:Person
139 sg:person.01160322451.91 schema:affiliation https://www.grid.ac/institutes/grid.418688.b
140 schema:familyName Gu
141 schema:givenName Lei
142 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01160322451.91
143 rdf:type schema:Person
144 sg:person.01245221165.79 schema:affiliation https://www.grid.ac/institutes/grid.16821.3c
145 schema:familyName Kong
146 schema:givenName Xiangying
147 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01245221165.79
148 rdf:type schema:Person
149 sg:person.01254504347.43 schema:affiliation https://www.grid.ac/institutes/grid.13402.34
150 schema:familyName He
151 schema:givenName ZhiSong
152 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01254504347.43
153 rdf:type schema:Person
154 sg:person.01344714423.17 schema:affiliation https://www.grid.ac/institutes/grid.8547.e
155 schema:familyName Cai
156 schema:givenName Yudong
157 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01344714423.17
158 rdf:type schema:Person
159 sg:pub.10.1038/35094077 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015264155
160 https://doi.org/10.1038/35094077
161 rdf:type schema:CreativeWork
162 sg:pub.10.1038/nmeth.1188 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046746398
163 https://doi.org/10.1038/nmeth.1188
164 rdf:type schema:CreativeWork
165 sg:pub.10.1186/gb-2000-1-1-reviews001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017829910
166 https://doi.org/10.1186/gb-2000-1-1-reviews001
167 rdf:type schema:CreativeWork
168 https://doi.org/10.1002/prot.10146 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041509798
169 rdf:type schema:CreativeWork
170 https://doi.org/10.1006/jmbi.1997.0958 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038197633
171 rdf:type schema:CreativeWork
172 https://doi.org/10.1016/j.bbrc.2006.07.149 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007823956
173 rdf:type schema:CreativeWork
174 https://doi.org/10.1016/j.biosystems.2006.08.007 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034198460
175 rdf:type schema:CreativeWork
176 https://doi.org/10.1016/j.cbpa.2003.11.001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049613524
177 rdf:type schema:CreativeWork
178 https://doi.org/10.1016/j.cell.2008.05.023 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010946240
179 rdf:type schema:CreativeWork
180 https://doi.org/10.1016/j.gde.2005.05.002 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007231307
181 rdf:type schema:CreativeWork
182 https://doi.org/10.1016/j.gene.2005.07.022 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005572322
183 rdf:type schema:CreativeWork
184 https://doi.org/10.1016/s1097-2765(01)00352-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051882977
185 rdf:type schema:CreativeWork
186 https://doi.org/10.1021/pr800717y schema:sameAs https://app.dimensions.ai/details/publication/pub.1056294600
187 rdf:type schema:CreativeWork
188 https://doi.org/10.1074/jbc.m411443200 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046136432
189 rdf:type schema:CreativeWork
190 https://doi.org/10.1074/jbc.m710539200 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017785340
191 rdf:type schema:CreativeWork
192 https://doi.org/10.1093/bioinformatics/btg1040 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049915428
193 rdf:type schema:CreativeWork
194 https://doi.org/10.1093/bioinformatics/btg432 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009866113
195 rdf:type schema:CreativeWork
196 https://doi.org/10.1093/bioinformatics/bti423 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024267147
197 rdf:type schema:CreativeWork
198 https://doi.org/10.1093/bioinformatics/btl672 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010270206
199 rdf:type schema:CreativeWork
200 https://doi.org/10.1093/bioinformatics/btm174 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018894529
201 rdf:type schema:CreativeWork
202 https://doi.org/10.1093/bioinformatics/btm299 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032460550
203 rdf:type schema:CreativeWork
204 https://doi.org/10.1093/bioinformatics/btm404 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007683223
205 rdf:type schema:CreativeWork
206 https://doi.org/10.1093/bioinformatics/btn583 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018060228
207 rdf:type schema:CreativeWork
208 https://doi.org/10.1093/nar/25.17.3389 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047265454
209 rdf:type schema:CreativeWork
210 https://doi.org/10.1093/nar/28.1.235 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035055456
211 rdf:type schema:CreativeWork
212 https://doi.org/10.1093/nar/gkm259 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053546100
213 rdf:type schema:CreativeWork
214 https://doi.org/10.1093/nar/gkn332 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006899310
215 rdf:type schema:CreativeWork
216 https://doi.org/10.1093/nar/gkn589 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001177930
217 rdf:type schema:CreativeWork
218 https://doi.org/10.1093/nar/gkn866 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042530703
219 rdf:type schema:CreativeWork
220 https://doi.org/10.1101/gr.3069205 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049120726
221 rdf:type schema:CreativeWork
222 https://doi.org/10.1109/tpami.2005.159 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061742820
223 rdf:type schema:CreativeWork
224 https://doi.org/10.1142/s0219720006002387 schema:sameAs https://app.dimensions.ai/details/publication/pub.1063004727
225 rdf:type schema:CreativeWork
226 https://doi.org/10.1371/journal.pcbi.0010001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019254281
227 rdf:type schema:CreativeWork
228 https://www.grid.ac/institutes/grid.13402.34 schema:alternateName Zhejiang University
229 schema:name Department of Bioinformatics, College of Life Sciences, Zhejiang University, 310058, ZheJiang, People’s Republic of China
230 rdf:type schema:Organization
231 https://www.grid.ac/institutes/grid.16821.3c schema:alternateName Shanghai Jiao Tong University
232 schema:name Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences (CAS) and Shanghai Jiao Tong University School of Medicine, Shanghai, People’s Republic of China
233 State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiaotong University, 200025, Shanghai, People’s Republic of China
234 rdf:type schema:Organization
235 https://www.grid.ac/institutes/grid.418688.b schema:alternateName Fraunhofer Institute for Algorithms and Scientific Computing
236 schema:name Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Bonn, Germany
237 rdf:type schema:Organization
238 https://www.grid.ac/institutes/grid.452350.5 schema:alternateName Institute of Health Sciences
239 schema:name Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences (CAS) and Shanghai Jiao Tong University School of Medicine, Shanghai, People’s Republic of China
240 rdf:type schema:Organization
241 https://www.grid.ac/institutes/grid.58095.31 schema:alternateName Shanghai Center For Bioinformation Technology
242 schema:name Shanghai Center for Bioinformation Technology, 200235, Shanghai, People’s Republic of China
243 rdf:type schema:Organization
244 https://www.grid.ac/institutes/grid.8547.e schema:alternateName Fudan University
245 schema:name Centre for Computational Systems Biology, Fudan University, 200433, Shanghai, People’s Republic of China
246 Institute of System Biology, Shanghai University, 200244, Shanghai, People’s Republic of China
247 rdf:type schema:Organization
 




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


...