Mathematical models applied to thyroid cancer View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-04

AUTHORS

Jairo Gomes da Silva, Rafael Martins de Morais, Izabel Cristina Rodrigues da Silva, Paulo Fernando de Arruda Mancera

ABSTRACT

Thyroid cancer is the most prevalent endocrine neoplasia in the world. The use of mathematical models on the development of tumors has yielded numerous results in this field and modeling with differential equations is present in many papers on cancer. In order to know the use of mathematical models with differential equations or similar in the study of thyroid cancer, studies since 2006 to date was reviewed. Systems with ordinary or partial differential equations were the means most frequently adopted by the authors. The models deal with tumor growth, effective half-life of radioiodine applied after thyroidectomy, the treatment with iodine-131, thyroid volume before thyroidectomy, and others. The variables usually employed in the models includes tumor volume, thyroid volume, amount of iodine, thyroglobulin and thyroxine hormone, radioiodine activity, and physical characteristics such as pressure, density, and displacement of the thyroid molecules. In conclusion, the mathematical models used so far with differential equations approach several aspects of thyroid cancer, including participation in methods of execution or follow-up of treatments. With the development of new models, an increase in the current understanding of the detection, evolution, and treatment of diseases is a step that should be considered. More... »

PAGES

183-189

References to SciGraph publications

Journal

TITLE

Biophysical Reviews

ISSUE

2

VOLUME

11

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s12551-019-00504-7

DOI

http://dx.doi.org/10.1007/s12551-019-00504-7

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1112158858

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30771157


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/2008/0102", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Applied Mathematics", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/01", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Mathematical Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Sao Paulo State University", 
          "id": "https://www.grid.ac/institutes/grid.410543.7", 
          "name": [
            "Instituto de Bioci\u00eancias, Programa de P\u00f3s-Gradua\u00e7\u00e3o em Biometria, Universidade Estadual Paulista (UNESP), Distrito de Rubi\u00e3o J\u00fanior, 18618\u2013689, Botucatu, SP, Brazil"
          ], 
          "type": "Organization"
        }, 
        "familyName": "da Silva", 
        "givenName": "Jairo Gomes", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "name": [
            "Faculdade de Ceil\u00e2ndia, Campus Universit\u00e1rio, Programa de P\u00f3s-Gradua\u00e7\u00e3o em Ci\u00eancias M\u00e9dicas, Universidade de Bras\u00edlia (UnB), 72220275, Ceil\u00e2ndia Sul, DF, Brazil"
          ], 
          "type": "Organization"
        }, 
        "familyName": "de Morais", 
        "givenName": "Rafael Martins", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "name": [
            "Faculdade de Ceil\u00e2ndia, Campus Universit\u00e1rio, Universidade de Bras\u00edlia (UnB), 72220275, Ceil\u00e2ndia Sul, DF, Brazil"
          ], 
          "type": "Organization"
        }, 
        "familyName": "da Silva", 
        "givenName": "Izabel Cristina Rodrigues", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Sao Paulo State University", 
          "id": "https://www.grid.ac/institutes/grid.410543.7", 
          "name": [
            "Instituto de Bioci\u00eancias, Universidade Estadual Paulista (UNESP), 18618\u2013689, Botucatu, SP, Brazil"
          ], 
          "type": "Organization"
        }, 
        "familyName": "de Arruda Mancera", 
        "givenName": "Paulo Fernando", 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/bf02477840", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000075991", 
          "https://doi.org/10.1007/bf02477840"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf02477840", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000075991", 
          "https://doi.org/10.1007/bf02477840"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.clon.2010.05.004", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005584603"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3389/fendo.2016.00064", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008044610"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1530/eje-10-0106", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011279335"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nrendo.2011.127", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011299483", 
          "https://doi.org/10.1038/nrendo.2011.127"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1530/eje-12-0819", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1012413154"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nrclinonc.2015.204", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014099062", 
          "https://doi.org/10.1038/nrclinonc.2015.204"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3349/ymj.2017.58.1.1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014636447"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/pros.20978", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016030966"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/pros.20978", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016030966"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.physa.2016.07.076", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026274935"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.ijsu.2010.01.005", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028521843"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1590/s0004-27302007000500004", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028685237"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-1-4613-0215-5_2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030331183", 
          "https://doi.org/10.1007/978-1-4613-0215-5_2"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/modpathol.3880305", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032172188", 
          "https://doi.org/10.1038/modpathol.3880305"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/modpathol.3880305", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032172188", 
          "https://doi.org/10.1038/modpathol.3880305"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.toxrep.2015.07.010", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032940358"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.mbs.2007.10.009", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037405962"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1371/journal.pmed.1000097", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037575666"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00423-008-0399-y", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038757528", 
          "https://doi.org/10.1007/s00423-008-0399-y"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11538-014-9955-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040798133", 
          "https://doi.org/10.1007/s11538-014-9955-5"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jamcollsurg.2004.10.031", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042749150"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1097/01.cco.0000143681.37692.32", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044087878"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1097/01.cco.0000143681.37692.32", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044087878"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1056/nejmra1501993", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044889611"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1056/nejmra1501993", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044889611"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nrdp.2015.77", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046396149", 
          "https://doi.org/10.1038/nrdp.2015.77"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11154-016-9386-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052213844", 
          "https://doi.org/10.1007/s11154-016-9386-8"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11154-016-9386-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052213844", 
          "https://doi.org/10.1007/s11154-016-9386-8"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1088/0031-9155/51/24/011", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1059026367"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1126/scitranslmed.3003110", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062687252"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1210/er.2012-1030", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064286029"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1210/jc.2012-4223", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064294098"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1269/jrr.07031", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064613122"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3934/dcdsb.2004.4.187", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1071735924"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/iembs.2010.5626373", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1078304961"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.18632/oncotarget.16637", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1084373746"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1371/journal.pone.0177068", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1085220542"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1371/journal.pone.0177068", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1085220542"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/jnci/djx209", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1091808476"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/jnci/djx209", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1091808476"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/s41598-017-13772-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1092152882", 
          "https://doi.org/10.1038/s41598-017-13772-8"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/s12902-018-0241-7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1101367347", 
          "https://doi.org/10.1186/s12902-018-0241-7"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/s12902-018-0241-7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1101367347", 
          "https://doi.org/10.1186/s12902-018-0241-7"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/s12902-018-0241-7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1101367347", 
          "https://doi.org/10.1186/s12902-018-0241-7"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/s12902-018-0241-7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1101367347", 
          "https://doi.org/10.1186/s12902-018-0241-7"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s12020-018-1637-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1104331774", 
          "https://doi.org/10.1007/s12020-018-1637-x"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2019-04", 
    "datePublishedReg": "2019-04-01", 
    "description": "Thyroid cancer is the most prevalent endocrine neoplasia in the world. The use of mathematical models on the development of tumors has yielded numerous results in this field and modeling with differential equations is present in many papers on cancer. In order to know the use of mathematical models with differential equations or similar in the study of thyroid cancer, studies since 2006 to date was reviewed. Systems with ordinary or partial differential equations were the means most frequently adopted by the authors. The models deal with tumor growth, effective half-life of radioiodine applied after thyroidectomy, the treatment with iodine-131, thyroid volume before thyroidectomy, and others. The variables usually employed in the models includes tumor volume, thyroid volume, amount of iodine, thyroglobulin and thyroxine hormone, radioiodine activity, and physical characteristics such as pressure, density, and displacement of the thyroid molecules. In conclusion, the mathematical models used so far with differential equations approach several aspects of thyroid cancer, including participation in methods of execution or follow-up of treatments. With the development of new models, an increase in the current understanding of the detection, evolution, and treatment of diseases is a step that should be considered.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s12551-019-00504-7", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1041162", 
        "issn": [
          "1867-2450", 
          "1867-2469"
        ], 
        "name": "Biophysical Reviews", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "2", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "11"
      }
    ], 
    "name": "Mathematical models applied to thyroid cancer", 
    "pagination": "183-189", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "88cdeb6c70477a4b232f33336f4503a1dea44dc31b858f4def47bfa44ee3c1b7"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "30771157"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "101498573"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s12551-019-00504-7"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1112158858"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s12551-019-00504-7", 
      "https://app.dimensions.ai/details/publication/pub.1112158858"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T13:27", 
    "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/v90/0000000370_0000000370/records_46737_00000003.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://link.springer.com/10.1007%2Fs12551-019-00504-7"
  }
]
 

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/s12551-019-00504-7'

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/s12551-019-00504-7'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s12551-019-00504-7'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s12551-019-00504-7'


 

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

214 TRIPLES      21 PREDICATES      66 URIs      21 LITERALS      9 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s12551-019-00504-7 schema:about anzsrc-for:01
2 anzsrc-for:0102
3 schema:author N473f3b8f365f4ef2b1b1640f9ebacaf0
4 schema:citation sg:pub.10.1007/978-1-4613-0215-5_2
5 sg:pub.10.1007/bf02477840
6 sg:pub.10.1007/s00423-008-0399-y
7 sg:pub.10.1007/s11154-016-9386-8
8 sg:pub.10.1007/s11538-014-9955-5
9 sg:pub.10.1007/s12020-018-1637-x
10 sg:pub.10.1038/modpathol.3880305
11 sg:pub.10.1038/nrclinonc.2015.204
12 sg:pub.10.1038/nrdp.2015.77
13 sg:pub.10.1038/nrendo.2011.127
14 sg:pub.10.1038/s41598-017-13772-8
15 sg:pub.10.1186/s12902-018-0241-7
16 https://doi.org/10.1002/pros.20978
17 https://doi.org/10.1016/j.clon.2010.05.004
18 https://doi.org/10.1016/j.ijsu.2010.01.005
19 https://doi.org/10.1016/j.jamcollsurg.2004.10.031
20 https://doi.org/10.1016/j.mbs.2007.10.009
21 https://doi.org/10.1016/j.physa.2016.07.076
22 https://doi.org/10.1016/j.toxrep.2015.07.010
23 https://doi.org/10.1056/nejmra1501993
24 https://doi.org/10.1088/0031-9155/51/24/011
25 https://doi.org/10.1093/jnci/djx209
26 https://doi.org/10.1097/01.cco.0000143681.37692.32
27 https://doi.org/10.1109/iembs.2010.5626373
28 https://doi.org/10.1126/scitranslmed.3003110
29 https://doi.org/10.1210/er.2012-1030
30 https://doi.org/10.1210/jc.2012-4223
31 https://doi.org/10.1269/jrr.07031
32 https://doi.org/10.1371/journal.pmed.1000097
33 https://doi.org/10.1371/journal.pone.0177068
34 https://doi.org/10.1530/eje-10-0106
35 https://doi.org/10.1530/eje-12-0819
36 https://doi.org/10.1590/s0004-27302007000500004
37 https://doi.org/10.18632/oncotarget.16637
38 https://doi.org/10.3349/ymj.2017.58.1.1
39 https://doi.org/10.3389/fendo.2016.00064
40 https://doi.org/10.3934/dcdsb.2004.4.187
41 schema:datePublished 2019-04
42 schema:datePublishedReg 2019-04-01
43 schema:description Thyroid cancer is the most prevalent endocrine neoplasia in the world. The use of mathematical models on the development of tumors has yielded numerous results in this field and modeling with differential equations is present in many papers on cancer. In order to know the use of mathematical models with differential equations or similar in the study of thyroid cancer, studies since 2006 to date was reviewed. Systems with ordinary or partial differential equations were the means most frequently adopted by the authors. The models deal with tumor growth, effective half-life of radioiodine applied after thyroidectomy, the treatment with iodine-131, thyroid volume before thyroidectomy, and others. The variables usually employed in the models includes tumor volume, thyroid volume, amount of iodine, thyroglobulin and thyroxine hormone, radioiodine activity, and physical characteristics such as pressure, density, and displacement of the thyroid molecules. In conclusion, the mathematical models used so far with differential equations approach several aspects of thyroid cancer, including participation in methods of execution or follow-up of treatments. With the development of new models, an increase in the current understanding of the detection, evolution, and treatment of diseases is a step that should be considered.
44 schema:genre research_article
45 schema:inLanguage en
46 schema:isAccessibleForFree false
47 schema:isPartOf N769bbe948efe49aabbf17a189a05d985
48 Nc4d81d11af5040639c2dd0dbfc61215b
49 sg:journal.1041162
50 schema:name Mathematical models applied to thyroid cancer
51 schema:pagination 183-189
52 schema:productId N04135711f30543e98dbd0846c85aaede
53 N56e77b15c1d3429287a571448324ac14
54 N6342344b101c4b98a5a3b821d6ddb895
55 N9771002ddfd34a75bdf6eceda378fda0
56 Nb72924b57b9b40be9302d4f3348e4ede
57 schema:sameAs https://app.dimensions.ai/details/publication/pub.1112158858
58 https://doi.org/10.1007/s12551-019-00504-7
59 schema:sdDatePublished 2019-04-11T13:27
60 schema:sdLicense https://scigraph.springernature.com/explorer/license/
61 schema:sdPublisher Na7ebee1b8131446a8ea7d329e30c1b93
62 schema:url https://link.springer.com/10.1007%2Fs12551-019-00504-7
63 sgo:license sg:explorer/license/
64 sgo:sdDataset articles
65 rdf:type schema:ScholarlyArticle
66 N04135711f30543e98dbd0846c85aaede schema:name pubmed_id
67 schema:value 30771157
68 rdf:type schema:PropertyValue
69 N473f3b8f365f4ef2b1b1640f9ebacaf0 rdf:first N598a76e3d0994d0ea46b9d60bc9e0b93
70 rdf:rest N8e9256b8326447f68ae0e3e931951792
71 N4dd203ffb8ce4ce3a42aa74090db9925 rdf:first N52d96f258048438fb8a589abb9c4a46b
72 rdf:rest rdf:nil
73 N52d96f258048438fb8a589abb9c4a46b schema:affiliation https://www.grid.ac/institutes/grid.410543.7
74 schema:familyName de Arruda Mancera
75 schema:givenName Paulo Fernando
76 rdf:type schema:Person
77 N56e77b15c1d3429287a571448324ac14 schema:name doi
78 schema:value 10.1007/s12551-019-00504-7
79 rdf:type schema:PropertyValue
80 N598a76e3d0994d0ea46b9d60bc9e0b93 schema:affiliation https://www.grid.ac/institutes/grid.410543.7
81 schema:familyName da Silva
82 schema:givenName Jairo Gomes
83 rdf:type schema:Person
84 N6342344b101c4b98a5a3b821d6ddb895 schema:name nlm_unique_id
85 schema:value 101498573
86 rdf:type schema:PropertyValue
87 N6fff305c1f89421c98e52715db812e38 schema:affiliation Naa0e50f945ec45369d0499148f7f837d
88 schema:familyName da Silva
89 schema:givenName Izabel Cristina Rodrigues
90 rdf:type schema:Person
91 N702fedfc401f484bb0cf12f90fff4823 rdf:first N6fff305c1f89421c98e52715db812e38
92 rdf:rest N4dd203ffb8ce4ce3a42aa74090db9925
93 N769bbe948efe49aabbf17a189a05d985 schema:volumeNumber 11
94 rdf:type schema:PublicationVolume
95 N8e9256b8326447f68ae0e3e931951792 rdf:first Nc87238a573ee4672842cdc366300737d
96 rdf:rest N702fedfc401f484bb0cf12f90fff4823
97 N9771002ddfd34a75bdf6eceda378fda0 schema:name readcube_id
98 schema:value 88cdeb6c70477a4b232f33336f4503a1dea44dc31b858f4def47bfa44ee3c1b7
99 rdf:type schema:PropertyValue
100 N9d447c7ac9e840a394454b0864cf7acd schema:name Faculdade de Ceilândia, Campus Universitário, Programa de Pós-Graduação em Ciências Médicas, Universidade de Brasília (UnB), 72220275, Ceilândia Sul, DF, Brazil
101 rdf:type schema:Organization
102 Na7ebee1b8131446a8ea7d329e30c1b93 schema:name Springer Nature - SN SciGraph project
103 rdf:type schema:Organization
104 Naa0e50f945ec45369d0499148f7f837d schema:name Faculdade de Ceilândia, Campus Universitário, Universidade de Brasília (UnB), 72220275, Ceilândia Sul, DF, Brazil
105 rdf:type schema:Organization
106 Nb72924b57b9b40be9302d4f3348e4ede schema:name dimensions_id
107 schema:value pub.1112158858
108 rdf:type schema:PropertyValue
109 Nc4d81d11af5040639c2dd0dbfc61215b schema:issueNumber 2
110 rdf:type schema:PublicationIssue
111 Nc87238a573ee4672842cdc366300737d schema:affiliation N9d447c7ac9e840a394454b0864cf7acd
112 schema:familyName de Morais
113 schema:givenName Rafael Martins
114 rdf:type schema:Person
115 anzsrc-for:01 schema:inDefinedTermSet anzsrc-for:
116 schema:name Mathematical Sciences
117 rdf:type schema:DefinedTerm
118 anzsrc-for:0102 schema:inDefinedTermSet anzsrc-for:
119 schema:name Applied Mathematics
120 rdf:type schema:DefinedTerm
121 sg:journal.1041162 schema:issn 1867-2450
122 1867-2469
123 schema:name Biophysical Reviews
124 rdf:type schema:Periodical
125 sg:pub.10.1007/978-1-4613-0215-5_2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030331183
126 https://doi.org/10.1007/978-1-4613-0215-5_2
127 rdf:type schema:CreativeWork
128 sg:pub.10.1007/bf02477840 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000075991
129 https://doi.org/10.1007/bf02477840
130 rdf:type schema:CreativeWork
131 sg:pub.10.1007/s00423-008-0399-y schema:sameAs https://app.dimensions.ai/details/publication/pub.1038757528
132 https://doi.org/10.1007/s00423-008-0399-y
133 rdf:type schema:CreativeWork
134 sg:pub.10.1007/s11154-016-9386-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052213844
135 https://doi.org/10.1007/s11154-016-9386-8
136 rdf:type schema:CreativeWork
137 sg:pub.10.1007/s11538-014-9955-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040798133
138 https://doi.org/10.1007/s11538-014-9955-5
139 rdf:type schema:CreativeWork
140 sg:pub.10.1007/s12020-018-1637-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1104331774
141 https://doi.org/10.1007/s12020-018-1637-x
142 rdf:type schema:CreativeWork
143 sg:pub.10.1038/modpathol.3880305 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032172188
144 https://doi.org/10.1038/modpathol.3880305
145 rdf:type schema:CreativeWork
146 sg:pub.10.1038/nrclinonc.2015.204 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014099062
147 https://doi.org/10.1038/nrclinonc.2015.204
148 rdf:type schema:CreativeWork
149 sg:pub.10.1038/nrdp.2015.77 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046396149
150 https://doi.org/10.1038/nrdp.2015.77
151 rdf:type schema:CreativeWork
152 sg:pub.10.1038/nrendo.2011.127 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011299483
153 https://doi.org/10.1038/nrendo.2011.127
154 rdf:type schema:CreativeWork
155 sg:pub.10.1038/s41598-017-13772-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1092152882
156 https://doi.org/10.1038/s41598-017-13772-8
157 rdf:type schema:CreativeWork
158 sg:pub.10.1186/s12902-018-0241-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1101367347
159 https://doi.org/10.1186/s12902-018-0241-7
160 rdf:type schema:CreativeWork
161 https://doi.org/10.1002/pros.20978 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016030966
162 rdf:type schema:CreativeWork
163 https://doi.org/10.1016/j.clon.2010.05.004 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005584603
164 rdf:type schema:CreativeWork
165 https://doi.org/10.1016/j.ijsu.2010.01.005 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028521843
166 rdf:type schema:CreativeWork
167 https://doi.org/10.1016/j.jamcollsurg.2004.10.031 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042749150
168 rdf:type schema:CreativeWork
169 https://doi.org/10.1016/j.mbs.2007.10.009 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037405962
170 rdf:type schema:CreativeWork
171 https://doi.org/10.1016/j.physa.2016.07.076 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026274935
172 rdf:type schema:CreativeWork
173 https://doi.org/10.1016/j.toxrep.2015.07.010 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032940358
174 rdf:type schema:CreativeWork
175 https://doi.org/10.1056/nejmra1501993 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044889611
176 rdf:type schema:CreativeWork
177 https://doi.org/10.1088/0031-9155/51/24/011 schema:sameAs https://app.dimensions.ai/details/publication/pub.1059026367
178 rdf:type schema:CreativeWork
179 https://doi.org/10.1093/jnci/djx209 schema:sameAs https://app.dimensions.ai/details/publication/pub.1091808476
180 rdf:type schema:CreativeWork
181 https://doi.org/10.1097/01.cco.0000143681.37692.32 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044087878
182 rdf:type schema:CreativeWork
183 https://doi.org/10.1109/iembs.2010.5626373 schema:sameAs https://app.dimensions.ai/details/publication/pub.1078304961
184 rdf:type schema:CreativeWork
185 https://doi.org/10.1126/scitranslmed.3003110 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062687252
186 rdf:type schema:CreativeWork
187 https://doi.org/10.1210/er.2012-1030 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064286029
188 rdf:type schema:CreativeWork
189 https://doi.org/10.1210/jc.2012-4223 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064294098
190 rdf:type schema:CreativeWork
191 https://doi.org/10.1269/jrr.07031 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064613122
192 rdf:type schema:CreativeWork
193 https://doi.org/10.1371/journal.pmed.1000097 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037575666
194 rdf:type schema:CreativeWork
195 https://doi.org/10.1371/journal.pone.0177068 schema:sameAs https://app.dimensions.ai/details/publication/pub.1085220542
196 rdf:type schema:CreativeWork
197 https://doi.org/10.1530/eje-10-0106 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011279335
198 rdf:type schema:CreativeWork
199 https://doi.org/10.1530/eje-12-0819 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012413154
200 rdf:type schema:CreativeWork
201 https://doi.org/10.1590/s0004-27302007000500004 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028685237
202 rdf:type schema:CreativeWork
203 https://doi.org/10.18632/oncotarget.16637 schema:sameAs https://app.dimensions.ai/details/publication/pub.1084373746
204 rdf:type schema:CreativeWork
205 https://doi.org/10.3349/ymj.2017.58.1.1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014636447
206 rdf:type schema:CreativeWork
207 https://doi.org/10.3389/fendo.2016.00064 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008044610
208 rdf:type schema:CreativeWork
209 https://doi.org/10.3934/dcdsb.2004.4.187 schema:sameAs https://app.dimensions.ai/details/publication/pub.1071735924
210 rdf:type schema:CreativeWork
211 https://www.grid.ac/institutes/grid.410543.7 schema:alternateName Sao Paulo State University
212 schema:name Instituto de Biociências, Programa de Pós-Graduação em Biometria, Universidade Estadual Paulista (UNESP), Distrito de Rubião Júnior, 18618–689, Botucatu, SP, Brazil
213 Instituto de Biociências, Universidade Estadual Paulista (UNESP), 18618–689, Botucatu, SP, Brazil
214 rdf:type schema:Organization
 




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


...