An Application of Artificial Immune Recognition System for Prediction of Diabetes Following Gestational Diabetes View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2011-06

AUTHORS

Hung-Chun Lin, Chao-Ton Su, Pa-Chun Wang

ABSTRACT

Diabetes mellitus (DM) is a disease prevalent in population and is not easily perceived in its initial stage but may sway a patient very seriously in later stage. In accordance with the estimation of World Health Organization (WHO), there will be 370 million diabetics which are 5.4% of the global people in 2030, so it becomes more and more important to predict whether a pregnant woman has or is likely to acquire diabetes. This study is conducted with the use of the machine learning-Artificial Immune Recognition System (AIRS)-to assist doctors in predicting pregnant women who have premonition of type 2 diabetes. AIRS is proposed by Andrew Watkins in 2001 and it makes use of the metaphor of the vertebrate immune system to recognize antigens, select clone, and memorize cells. Additionally, AIRS includes a mechanism, limited resource, to restrain the number of memory cells from increasing uncontrollably. It has also showed positive results on problems in which it was applied. The objective of this study is to investigate the feasibility in using AIRS to predict gestational diabetes mellitus (GDM) subsequent DM. The dataset of diabetes has imbalanced data, but the overall classification recall could still reach 62.8%, which is better than the traditional method, logistic regression, and the technique which is thought as one of the powerful classification approaches, support vector machines (SVM). More... »

PAGES

283-289

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10916-009-9364-8

DOI

http://dx.doi.org/10.1007/s10916-009-9364-8

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1039913800

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/20703562


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/2008/1107", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Immunology", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/11", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Medical and Health Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Algorithms", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Antibodies", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Antigens", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Artificial Intelligence", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Blood Glucose", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Diabetes Mellitus, Type 2", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Diabetes, Gestational", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Female", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Humans", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Logistic Models", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Pregnancy", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Risk Assessment", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Risk Factors", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Taiwan", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "National Tsing Hua University", 
          "id": "https://www.grid.ac/institutes/grid.38348.34", 
          "name": [
            "Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu, Taiwan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Lin", 
        "givenName": "Hung-Chun", 
        "id": "sg:person.015252763631.82", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015252763631.82"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "National Tsing Hua University", 
          "id": "https://www.grid.ac/institutes/grid.38348.34", 
          "name": [
            "Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu, Taiwan", 
            "Room 820, Engineering Building I, 101, Sec. 2, Kuang Fu Road, 30013, Hsinchu, Taiwan, Republic of China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Su", 
        "givenName": "Chao-Ton", 
        "id": "sg:person.016137101441.02", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016137101441.02"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Cathay General Hospital", 
          "id": "https://www.grid.ac/institutes/grid.413535.5", 
          "name": [
            "Department of Otolaryngology, Cathay General Hospital, Fu Jen Catholic University School of Medicine, Taipei, Taiwan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Wang", 
        "givenName": "Pa-Chun", 
        "id": "sg:person.0620141324.98", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0620141324.98"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1080/09513590802444134", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005203116"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/1961189.1961199", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013637525"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-540-30182-0_2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015126656", 
          "https://doi.org/10.1007/978-3-540-30182-0_2"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-540-30182-0_2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015126656", 
          "https://doi.org/10.1007/978-3-540-30182-0_2"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.eswa.2006.09.001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020370633"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10916-008-9155-7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1029945507", 
          "https://doi.org/10.1007/s10916-008-9155-7"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2337/dc06-1816", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035065697"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.patcog.2006.05.028", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035905484"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jdiacomp.2005.05.001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036500053"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/btp107", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036963638"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.diabres.2008.07.023", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040731003"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2337/dc07-1957", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046618093"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10916-008-9129-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048167479", 
          "https://doi.org/10.1007/s10916-008-9129-9"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1097/01.aog.0000189081.46925.90", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049801132"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1097/01.aog.0000189081.46925.90", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049801132"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.compmedimag.2007.01.003", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052781662"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2337/diacare.28.11.2750", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053307067"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/72.991427", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061219719"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tkde.2005.95", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061661489"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tsmcb.2008.2002909", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061796856"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1077527570", 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1590/s0100-879x2008000800008", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1077726975"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/fuzz.2002.1005080", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1094312151"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/cec.2002.1007049", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1095618390"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2011-06", 
    "datePublishedReg": "2011-06-01", 
    "description": "Diabetes mellitus (DM) is a disease prevalent in population and is not easily perceived in its initial stage but may sway a patient very seriously in later stage. In accordance with the estimation of World Health Organization (WHO), there will be 370 million diabetics which are 5.4% of the global people in 2030, so it becomes more and more important to predict whether a pregnant woman has or is likely to acquire diabetes. This study is conducted with the use of the machine learning-Artificial Immune Recognition System (AIRS)-to assist doctors in predicting pregnant women who have premonition of type 2 diabetes. AIRS is proposed by Andrew Watkins in 2001 and it makes use of the metaphor of the vertebrate immune system to recognize antigens, select clone, and memorize cells. Additionally, AIRS includes a mechanism, limited resource, to restrain the number of memory cells from increasing uncontrollably. It has also showed positive results on problems in which it was applied. The objective of this study is to investigate the feasibility in using AIRS to predict gestational diabetes mellitus (GDM) subsequent DM. The dataset of diabetes has imbalanced data, but the overall classification recall could still reach 62.8%, which is better than the traditional method, logistic regression, and the technique which is thought as one of the powerful classification approaches, support vector machines (SVM).", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s10916-009-9364-8", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1088158", 
        "issn": [
          "0148-5598", 
          "1573-689X"
        ], 
        "name": "Journal of Medical Systems", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "3", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "35"
      }
    ], 
    "name": "An Application of Artificial Immune Recognition System for Prediction of Diabetes Following Gestational Diabetes", 
    "pagination": "283-289", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "be21fdf9b8d9df917df357cc806b2e100dec226ac7d76536039ce3741bedbf7e"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "20703562"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "7806056"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s10916-009-9364-8"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1039913800"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s10916-009-9364-8", 
      "https://app.dimensions.ai/details/publication/pub.1039913800"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-10T20:47", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8684_00000514.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1007%2Fs10916-009-9364-8"
  }
]
 

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/s10916-009-9364-8'

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/s10916-009-9364-8'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s10916-009-9364-8'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s10916-009-9364-8'


 

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

211 TRIPLES      21 PREDICATES      65 URIs      35 LITERALS      23 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s10916-009-9364-8 schema:about N1cb2f888802c4d049b3c2da369f7fb45
2 N590d429aa6ff456d9fcf989c83e3c738
3 N6cf31fb1d17d49b391273eae6d2e2711
4 N7675552ddc764ae5941e92f0de53e45a
5 N7c2701e7afb04f63a51236cf199f9c60
6 N9090b62f7bca4c618b68efeeee815a26
7 N9f4436ad3eed490fbdf8fadd4e462cc7
8 Na006c6b121034068b78fb0b61f650a11
9 Nc8ecb61f13864f68b781cef536ac9177
10 Ncb4416dec7364194b08d1b29a4c666f3
11 Nd19d209cf6374c1184553e8f5adfd096
12 Ne4911e94e223460fa61c03725ff6f890
13 Nec4ea3471634410ab639ffb1ab4826c3
14 Nf5d532b01eaf4e818e286d258ddd4262
15 anzsrc-for:11
16 anzsrc-for:1107
17 schema:author N099562be928f48ae8a885fd8ee6dc4ea
18 schema:citation sg:pub.10.1007/978-3-540-30182-0_2
19 sg:pub.10.1007/s10916-008-9129-9
20 sg:pub.10.1007/s10916-008-9155-7
21 https://app.dimensions.ai/details/publication/pub.1077527570
22 https://doi.org/10.1016/j.compmedimag.2007.01.003
23 https://doi.org/10.1016/j.diabres.2008.07.023
24 https://doi.org/10.1016/j.eswa.2006.09.001
25 https://doi.org/10.1016/j.jdiacomp.2005.05.001
26 https://doi.org/10.1016/j.patcog.2006.05.028
27 https://doi.org/10.1080/09513590802444134
28 https://doi.org/10.1093/bioinformatics/btp107
29 https://doi.org/10.1097/01.aog.0000189081.46925.90
30 https://doi.org/10.1109/72.991427
31 https://doi.org/10.1109/cec.2002.1007049
32 https://doi.org/10.1109/fuzz.2002.1005080
33 https://doi.org/10.1109/tkde.2005.95
34 https://doi.org/10.1109/tsmcb.2008.2002909
35 https://doi.org/10.1145/1961189.1961199
36 https://doi.org/10.1590/s0100-879x2008000800008
37 https://doi.org/10.2337/dc06-1816
38 https://doi.org/10.2337/dc07-1957
39 https://doi.org/10.2337/diacare.28.11.2750
40 schema:datePublished 2011-06
41 schema:datePublishedReg 2011-06-01
42 schema:description Diabetes mellitus (DM) is a disease prevalent in population and is not easily perceived in its initial stage but may sway a patient very seriously in later stage. In accordance with the estimation of World Health Organization (WHO), there will be 370 million diabetics which are 5.4% of the global people in 2030, so it becomes more and more important to predict whether a pregnant woman has or is likely to acquire diabetes. This study is conducted with the use of the machine learning-Artificial Immune Recognition System (AIRS)-to assist doctors in predicting pregnant women who have premonition of type 2 diabetes. AIRS is proposed by Andrew Watkins in 2001 and it makes use of the metaphor of the vertebrate immune system to recognize antigens, select clone, and memorize cells. Additionally, AIRS includes a mechanism, limited resource, to restrain the number of memory cells from increasing uncontrollably. It has also showed positive results on problems in which it was applied. The objective of this study is to investigate the feasibility in using AIRS to predict gestational diabetes mellitus (GDM) subsequent DM. The dataset of diabetes has imbalanced data, but the overall classification recall could still reach 62.8%, which is better than the traditional method, logistic regression, and the technique which is thought as one of the powerful classification approaches, support vector machines (SVM).
43 schema:genre research_article
44 schema:inLanguage en
45 schema:isAccessibleForFree false
46 schema:isPartOf N5395326b3d6841adb98ae092cbc7359d
47 Nb910eb0d32814ca2a56de5705559c307
48 sg:journal.1088158
49 schema:name An Application of Artificial Immune Recognition System for Prediction of Diabetes Following Gestational Diabetes
50 schema:pagination 283-289
51 schema:productId N750d007793d54131b93c23c7345b9c22
52 N8ec7879a0e364e3a9103adb2247a3ec3
53 N8f512479d17e45be83b180f715bd4ad1
54 Nda504d0bae8944eab75274450f0849c4
55 Ndf275cbb847e49689934a0badd5cc729
56 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039913800
57 https://doi.org/10.1007/s10916-009-9364-8
58 schema:sdDatePublished 2019-04-10T20:47
59 schema:sdLicense https://scigraph.springernature.com/explorer/license/
60 schema:sdPublisher Nbfc8595f831e46c7861365e096322ebc
61 schema:url http://link.springer.com/10.1007%2Fs10916-009-9364-8
62 sgo:license sg:explorer/license/
63 sgo:sdDataset articles
64 rdf:type schema:ScholarlyArticle
65 N099562be928f48ae8a885fd8ee6dc4ea rdf:first sg:person.015252763631.82
66 rdf:rest Nc32561c25b8f4e5aafe6da7268029b0f
67 N1cb2f888802c4d049b3c2da369f7fb45 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
68 schema:name Female
69 rdf:type schema:DefinedTerm
70 N2f35e06d8af04e45bc717c35f7136a27 rdf:first sg:person.0620141324.98
71 rdf:rest rdf:nil
72 N5395326b3d6841adb98ae092cbc7359d schema:volumeNumber 35
73 rdf:type schema:PublicationVolume
74 N590d429aa6ff456d9fcf989c83e3c738 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
75 schema:name Diabetes, Gestational
76 rdf:type schema:DefinedTerm
77 N6cf31fb1d17d49b391273eae6d2e2711 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
78 schema:name Risk Assessment
79 rdf:type schema:DefinedTerm
80 N750d007793d54131b93c23c7345b9c22 schema:name nlm_unique_id
81 schema:value 7806056
82 rdf:type schema:PropertyValue
83 N7675552ddc764ae5941e92f0de53e45a schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
84 schema:name Algorithms
85 rdf:type schema:DefinedTerm
86 N7c2701e7afb04f63a51236cf199f9c60 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
87 schema:name Artificial Intelligence
88 rdf:type schema:DefinedTerm
89 N8ec7879a0e364e3a9103adb2247a3ec3 schema:name dimensions_id
90 schema:value pub.1039913800
91 rdf:type schema:PropertyValue
92 N8f512479d17e45be83b180f715bd4ad1 schema:name pubmed_id
93 schema:value 20703562
94 rdf:type schema:PropertyValue
95 N9090b62f7bca4c618b68efeeee815a26 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
96 schema:name Risk Factors
97 rdf:type schema:DefinedTerm
98 N9f4436ad3eed490fbdf8fadd4e462cc7 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
99 schema:name Diabetes Mellitus, Type 2
100 rdf:type schema:DefinedTerm
101 Na006c6b121034068b78fb0b61f650a11 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
102 schema:name Pregnancy
103 rdf:type schema:DefinedTerm
104 Nb910eb0d32814ca2a56de5705559c307 schema:issueNumber 3
105 rdf:type schema:PublicationIssue
106 Nbfc8595f831e46c7861365e096322ebc schema:name Springer Nature - SN SciGraph project
107 rdf:type schema:Organization
108 Nc32561c25b8f4e5aafe6da7268029b0f rdf:first sg:person.016137101441.02
109 rdf:rest N2f35e06d8af04e45bc717c35f7136a27
110 Nc8ecb61f13864f68b781cef536ac9177 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
111 schema:name Antibodies
112 rdf:type schema:DefinedTerm
113 Ncb4416dec7364194b08d1b29a4c666f3 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
114 schema:name Blood Glucose
115 rdf:type schema:DefinedTerm
116 Nd19d209cf6374c1184553e8f5adfd096 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
117 schema:name Taiwan
118 rdf:type schema:DefinedTerm
119 Nda504d0bae8944eab75274450f0849c4 schema:name doi
120 schema:value 10.1007/s10916-009-9364-8
121 rdf:type schema:PropertyValue
122 Ndf275cbb847e49689934a0badd5cc729 schema:name readcube_id
123 schema:value be21fdf9b8d9df917df357cc806b2e100dec226ac7d76536039ce3741bedbf7e
124 rdf:type schema:PropertyValue
125 Ne4911e94e223460fa61c03725ff6f890 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
126 schema:name Humans
127 rdf:type schema:DefinedTerm
128 Nec4ea3471634410ab639ffb1ab4826c3 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
129 schema:name Logistic Models
130 rdf:type schema:DefinedTerm
131 Nf5d532b01eaf4e818e286d258ddd4262 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
132 schema:name Antigens
133 rdf:type schema:DefinedTerm
134 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
135 schema:name Medical and Health Sciences
136 rdf:type schema:DefinedTerm
137 anzsrc-for:1107 schema:inDefinedTermSet anzsrc-for:
138 schema:name Immunology
139 rdf:type schema:DefinedTerm
140 sg:journal.1088158 schema:issn 0148-5598
141 1573-689X
142 schema:name Journal of Medical Systems
143 rdf:type schema:Periodical
144 sg:person.015252763631.82 schema:affiliation https://www.grid.ac/institutes/grid.38348.34
145 schema:familyName Lin
146 schema:givenName Hung-Chun
147 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015252763631.82
148 rdf:type schema:Person
149 sg:person.016137101441.02 schema:affiliation https://www.grid.ac/institutes/grid.38348.34
150 schema:familyName Su
151 schema:givenName Chao-Ton
152 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016137101441.02
153 rdf:type schema:Person
154 sg:person.0620141324.98 schema:affiliation https://www.grid.ac/institutes/grid.413535.5
155 schema:familyName Wang
156 schema:givenName Pa-Chun
157 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0620141324.98
158 rdf:type schema:Person
159 sg:pub.10.1007/978-3-540-30182-0_2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015126656
160 https://doi.org/10.1007/978-3-540-30182-0_2
161 rdf:type schema:CreativeWork
162 sg:pub.10.1007/s10916-008-9129-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048167479
163 https://doi.org/10.1007/s10916-008-9129-9
164 rdf:type schema:CreativeWork
165 sg:pub.10.1007/s10916-008-9155-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029945507
166 https://doi.org/10.1007/s10916-008-9155-7
167 rdf:type schema:CreativeWork
168 https://app.dimensions.ai/details/publication/pub.1077527570 schema:CreativeWork
169 https://doi.org/10.1016/j.compmedimag.2007.01.003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052781662
170 rdf:type schema:CreativeWork
171 https://doi.org/10.1016/j.diabres.2008.07.023 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040731003
172 rdf:type schema:CreativeWork
173 https://doi.org/10.1016/j.eswa.2006.09.001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020370633
174 rdf:type schema:CreativeWork
175 https://doi.org/10.1016/j.jdiacomp.2005.05.001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036500053
176 rdf:type schema:CreativeWork
177 https://doi.org/10.1016/j.patcog.2006.05.028 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035905484
178 rdf:type schema:CreativeWork
179 https://doi.org/10.1080/09513590802444134 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005203116
180 rdf:type schema:CreativeWork
181 https://doi.org/10.1093/bioinformatics/btp107 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036963638
182 rdf:type schema:CreativeWork
183 https://doi.org/10.1097/01.aog.0000189081.46925.90 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049801132
184 rdf:type schema:CreativeWork
185 https://doi.org/10.1109/72.991427 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061219719
186 rdf:type schema:CreativeWork
187 https://doi.org/10.1109/cec.2002.1007049 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095618390
188 rdf:type schema:CreativeWork
189 https://doi.org/10.1109/fuzz.2002.1005080 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094312151
190 rdf:type schema:CreativeWork
191 https://doi.org/10.1109/tkde.2005.95 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061661489
192 rdf:type schema:CreativeWork
193 https://doi.org/10.1109/tsmcb.2008.2002909 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061796856
194 rdf:type schema:CreativeWork
195 https://doi.org/10.1145/1961189.1961199 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013637525
196 rdf:type schema:CreativeWork
197 https://doi.org/10.1590/s0100-879x2008000800008 schema:sameAs https://app.dimensions.ai/details/publication/pub.1077726975
198 rdf:type schema:CreativeWork
199 https://doi.org/10.2337/dc06-1816 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035065697
200 rdf:type schema:CreativeWork
201 https://doi.org/10.2337/dc07-1957 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046618093
202 rdf:type schema:CreativeWork
203 https://doi.org/10.2337/diacare.28.11.2750 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053307067
204 rdf:type schema:CreativeWork
205 https://www.grid.ac/institutes/grid.38348.34 schema:alternateName National Tsing Hua University
206 schema:name Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu, Taiwan
207 Room 820, Engineering Building I, 101, Sec. 2, Kuang Fu Road, 30013, Hsinchu, Taiwan, Republic of China
208 rdf:type schema:Organization
209 https://www.grid.ac/institutes/grid.413535.5 schema:alternateName Cathay General Hospital
210 schema:name Department of Otolaryngology, Cathay General Hospital, Fu Jen Catholic University School of Medicine, Taipei, Taiwan
211 rdf:type schema:Organization
 




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


...