Applications of node-based resilience graph theoretic framework to clustering autism spectrum disorders phenotypes View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2018-12

AUTHORS

John Matta, Junya Zhao, Gunes Ercal, Tayo Obafemi-Ajayi

ABSTRACT

With the growing ubiquity of data in network form, clustering in the context of a network, represented as a graph, has become increasingly important. Clustering is a very useful data exploratory machine learning tool that allows us to make better sense of heterogeneous data by grouping data with similar attributes based on some criteria. This paper investigates the application of a novel graph theoretic clustering method, Node-Based Resilience clustering (NBR-Clust), to address the heterogeneity of Autism Spectrum Disorder (ASD) and identify meaningful subgroups. The hypothesis is that analysis of these subgroups would reveal relevant biomarkers that would provide a better understanding of ASD phenotypic heterogeneity useful for further ASD studies. We address appropriate graph constructions suited for representing the ASD phenotype data. The sample population is drawn from a very large rigorous dataset: Simons Simplex Collection (SSC). Analysis of the results performed using graph quality measures, internal cluster validation measures, and clinical analysis outcome demonstrate the potential usefulness of resilience measure clustering for biomedical datasets. We also conduct feature extraction analysis to characterize relevant biomarkers that delineate the resulting subgroups. The optimal results obtained favored predominantly a 5-cluster configuration. More... »

PAGES

38

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s41109-018-0093-0

DOI

http://dx.doi.org/10.1007/s41109-018-0093-0

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1106417588

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30839816


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/2008/0801", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Artificial Intelligence and Image Processing", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Information and Computing Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Southern Illinois University Edwardsville", 
          "id": "https://www.grid.ac/institutes/grid.263857.d", 
          "name": [
            "Department of Computer Science, Southern Illinois University Edwardsville, Edwardsville, IL, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Matta", 
        "givenName": "John", 
        "id": "sg:person.015226045673.42", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015226045673.42"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Missouri State University", 
          "id": "https://www.grid.ac/institutes/grid.260126.1", 
          "name": [
            "Department of Computer Science, Missouri State University, Springfield, MO, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Zhao", 
        "givenName": "Junya", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Southern Illinois University Edwardsville", 
          "id": "https://www.grid.ac/institutes/grid.263857.d", 
          "name": [
            "Department of Computer Science, Southern Illinois University Edwardsville, Edwardsville, IL, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Ercal", 
        "givenName": "Gunes", 
        "id": "sg:person.010713316073.48", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010713316073.48"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Missouri State University", 
          "id": "https://www.grid.ac/institutes/grid.260126.1", 
          "name": [
            "Engineering Program, Missouri State University, Springfield, MO, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Obafemi-Ajayi", 
        "givenName": "Tayo", 
        "id": "sg:person.0617532531.24", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0617532531.24"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/s10803-014-2290-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1001744885", 
          "https://doi.org/10.1007/s10803-014-2290-8"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2217/npy.13.8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003708782"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1469-7610.2012.02588.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011377672"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1073/pnas.0601602103", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016125157"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf02172145", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017747169", 
          "https://doi.org/10.1007/bf02172145"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf02172145", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017747169", 
          "https://doi.org/10.1007/bf02172145"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.neuron.2010.10.006", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018941250"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/1502793.1502794", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021367426"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf02172209", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024658676", 
          "https://doi.org/10.1007/bf02172209"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf02172209", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024658676", 
          "https://doi.org/10.1007/bf02172209"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s40474-013-0003-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027385742", 
          "https://doi.org/10.1007/s40474-013-0003-1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10803-012-1719-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028107829", 
          "https://doi.org/10.1007/s10803-012-1719-1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/1656274.1656278", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028526411"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10803-007-0469-y", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030826570", 
          "https://doi.org/10.1007/s10803-007-0469-y"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf02211841", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031175125", 
          "https://doi.org/10.1007/bf02211841"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf02211841", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031175125", 
          "https://doi.org/10.1007/bf02211841"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/0022250x.2001.9990249", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032164704"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.patcog.2006.06.026", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1033731968"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1097/00004583-200003000-00017", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034120374"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1097/00004583-200003000-00017", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034120374"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1097/00004583-200003000-00017", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034120374"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/gbb.12117", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034206569"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.patcog.2012.07.021", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036814301"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/sam.10080", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040315874"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/sam.10080", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040315874"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1001/archgenpsychiatry.2011.148", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041606585"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1097/wco.0000000000000300", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042065607"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1097/wco.0000000000000300", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042065607"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.biopsych.2014.09.017", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042386877"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/2350190.2350193", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045641931"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1542/peds.2013-0763", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046272385"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1542/peds.2013-0763", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046272385"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10803-011-1402-y", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047366866", 
          "https://doi.org/10.1007/s10803-011-1402-y"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0166-218x(98)00083-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049878122"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tsmcb.2012.2220543", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061797573"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tsmcb.2012.2223671", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061797580"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/icdmw.2008.39", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1078184921"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1078886835", 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1051/ro/2017008", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1083427310"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/embc.2016.7591440", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1084498533"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/med/9780195371826.003.0046", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1089154572"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jrp.2017.11.003", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1092637274"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-319-72150-7_1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1093022510", 
          "https://doi.org/10.1007/978-3-319-72150-7_1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jbi.2017.11.016", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1093055098"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jbi.2017.11.016", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1093055098"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/icdm.2016.0043", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1094394929"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/icdm.2010.35", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1094710123"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/cibcb.2015.7300337", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1095172156"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1176/appi.books.9780890425596", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1097032812"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/ssci.2017.8280937", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1100856522"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1098/rsos.171592", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1101525141"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.paid.2018.04.003", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1103188183"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.paid.2018.04.003", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1103188183"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/3154524", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1103387506"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.neuroimage.2018.05.005", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1103796034"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.neuroimage.2018.05.005", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1103796034"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.neuroimage.2018.05.005", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1103796034"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/cibcb.2018.8404962", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1105445483"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/cibcb.2018.8404960", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1105446976"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/3233547.3233602", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1106353758"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/3233547.3233602", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1106353758"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1201/b15410", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1109727642"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2018-12", 
    "datePublishedReg": "2018-12-01", 
    "description": "With the growing ubiquity of data in network form, clustering in the context of a network, represented as a graph, has become increasingly important. Clustering is a very useful data exploratory machine learning tool that allows us to make better sense of heterogeneous data by grouping data with similar attributes based on some criteria. This paper investigates the application of a novel graph theoretic clustering method, Node-Based Resilience clustering (NBR-Clust), to address the heterogeneity of Autism Spectrum Disorder (ASD) and identify meaningful subgroups. The hypothesis is that analysis of these subgroups would reveal relevant biomarkers that would provide a better understanding of ASD phenotypic heterogeneity useful for further ASD studies. We address appropriate graph constructions suited for representing the ASD phenotype data. The sample population is drawn from a very large rigorous dataset: Simons Simplex Collection (SSC). Analysis of the results performed using graph quality measures, internal cluster validation measures, and clinical analysis outcome demonstrate the potential usefulness of resilience measure clustering for biomedical datasets. We also conduct feature extraction analysis to characterize relevant biomarkers that delineate the resulting subgroups. The optimal results obtained favored predominantly a 5-cluster configuration.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s41109-018-0093-0", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1158525", 
        "issn": [
          "2364-8228"
        ], 
        "name": "Applied Network Science", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "3"
      }
    ], 
    "name": "Applications of node-based resilience graph theoretic framework to clustering autism spectrum disorders phenotypes", 
    "pagination": "38", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "f03fdd2923fc9bfe90d6357c44da6abbf84fd0596b33ae209449c9a9fc26bb0e"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "30839816"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "101732938"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s41109-018-0093-0"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1106417588"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s41109-018-0093-0", 
      "https://app.dimensions.ai/details/publication/pub.1106417588"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T11:18", 
    "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/v90/0000000354_0000000354/records_11701_00000002.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1007/s41109-018-0093-0"
  }
]
 

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/s41109-018-0093-0'

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/s41109-018-0093-0'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s41109-018-0093-0'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s41109-018-0093-0'


 

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

247 TRIPLES      21 PREDICATES      78 URIs      21 LITERALS      9 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s41109-018-0093-0 schema:about anzsrc-for:08
2 anzsrc-for:0801
3 schema:author N7d539e3c3a79420ab4ec895279a4cbea
4 schema:citation sg:pub.10.1007/978-3-319-72150-7_1
5 sg:pub.10.1007/bf02172145
6 sg:pub.10.1007/bf02172209
7 sg:pub.10.1007/bf02211841
8 sg:pub.10.1007/s10803-007-0469-y
9 sg:pub.10.1007/s10803-011-1402-y
10 sg:pub.10.1007/s10803-012-1719-1
11 sg:pub.10.1007/s10803-014-2290-8
12 sg:pub.10.1007/s40474-013-0003-1
13 https://app.dimensions.ai/details/publication/pub.1078886835
14 https://doi.org/10.1001/archgenpsychiatry.2011.148
15 https://doi.org/10.1002/sam.10080
16 https://doi.org/10.1016/j.biopsych.2014.09.017
17 https://doi.org/10.1016/j.jbi.2017.11.016
18 https://doi.org/10.1016/j.jrp.2017.11.003
19 https://doi.org/10.1016/j.neuroimage.2018.05.005
20 https://doi.org/10.1016/j.neuron.2010.10.006
21 https://doi.org/10.1016/j.paid.2018.04.003
22 https://doi.org/10.1016/j.patcog.2006.06.026
23 https://doi.org/10.1016/j.patcog.2012.07.021
24 https://doi.org/10.1016/s0166-218x(98)00083-3
25 https://doi.org/10.1051/ro/2017008
26 https://doi.org/10.1073/pnas.0601602103
27 https://doi.org/10.1080/0022250x.2001.9990249
28 https://doi.org/10.1093/med/9780195371826.003.0046
29 https://doi.org/10.1097/00004583-200003000-00017
30 https://doi.org/10.1097/wco.0000000000000300
31 https://doi.org/10.1098/rsos.171592
32 https://doi.org/10.1109/cibcb.2015.7300337
33 https://doi.org/10.1109/cibcb.2018.8404960
34 https://doi.org/10.1109/cibcb.2018.8404962
35 https://doi.org/10.1109/embc.2016.7591440
36 https://doi.org/10.1109/icdm.2010.35
37 https://doi.org/10.1109/icdm.2016.0043
38 https://doi.org/10.1109/icdmw.2008.39
39 https://doi.org/10.1109/ssci.2017.8280937
40 https://doi.org/10.1109/tsmcb.2012.2220543
41 https://doi.org/10.1109/tsmcb.2012.2223671
42 https://doi.org/10.1111/gbb.12117
43 https://doi.org/10.1111/j.1469-7610.2012.02588.x
44 https://doi.org/10.1145/1502793.1502794
45 https://doi.org/10.1145/1656274.1656278
46 https://doi.org/10.1145/2350190.2350193
47 https://doi.org/10.1145/3154524
48 https://doi.org/10.1145/3233547.3233602
49 https://doi.org/10.1176/appi.books.9780890425596
50 https://doi.org/10.1201/b15410
51 https://doi.org/10.1542/peds.2013-0763
52 https://doi.org/10.2217/npy.13.8
53 schema:datePublished 2018-12
54 schema:datePublishedReg 2018-12-01
55 schema:description With the growing ubiquity of data in network form, clustering in the context of a network, represented as a graph, has become increasingly important. Clustering is a very useful data exploratory machine learning tool that allows us to make better sense of heterogeneous data by grouping data with similar attributes based on some criteria. This paper investigates the application of a novel graph theoretic clustering method, Node-Based Resilience clustering (NBR-Clust), to address the heterogeneity of Autism Spectrum Disorder (ASD) and identify meaningful subgroups. The hypothesis is that analysis of these subgroups would reveal relevant biomarkers that would provide a better understanding of ASD phenotypic heterogeneity useful for further ASD studies. We address appropriate graph constructions suited for representing the ASD phenotype data. The sample population is drawn from a very large rigorous dataset: Simons Simplex Collection (SSC). Analysis of the results performed using graph quality measures, internal cluster validation measures, and clinical analysis outcome demonstrate the potential usefulness of resilience measure clustering for biomedical datasets. We also conduct feature extraction analysis to characterize relevant biomarkers that delineate the resulting subgroups. The optimal results obtained favored predominantly a 5-cluster configuration.
56 schema:genre research_article
57 schema:inLanguage en
58 schema:isAccessibleForFree false
59 schema:isPartOf N6d8b378c8100430faf437f67906c2dd7
60 Ne64313d79a534f28a996e92601bc37ff
61 sg:journal.1158525
62 schema:name Applications of node-based resilience graph theoretic framework to clustering autism spectrum disorders phenotypes
63 schema:pagination 38
64 schema:productId N03a53d2995914837a0d425fb9dbc0361
65 N2b63116c37054ff6a984d2ce91d735a9
66 N377f400d3f204f4fa7db6e9a37d9f9dc
67 N99153955990847928dddec2150c5a865
68 Nc38c7d45db6044d183ccc69335fde802
69 schema:sameAs https://app.dimensions.ai/details/publication/pub.1106417588
70 https://doi.org/10.1007/s41109-018-0093-0
71 schema:sdDatePublished 2019-04-11T11:18
72 schema:sdLicense https://scigraph.springernature.com/explorer/license/
73 schema:sdPublisher N9cd63c80ab9848e8adaf68dde85bcd41
74 schema:url http://link.springer.com/10.1007/s41109-018-0093-0
75 sgo:license sg:explorer/license/
76 sgo:sdDataset articles
77 rdf:type schema:ScholarlyArticle
78 N03a53d2995914837a0d425fb9dbc0361 schema:name doi
79 schema:value 10.1007/s41109-018-0093-0
80 rdf:type schema:PropertyValue
81 N2b63116c37054ff6a984d2ce91d735a9 schema:name nlm_unique_id
82 schema:value 101732938
83 rdf:type schema:PropertyValue
84 N377f400d3f204f4fa7db6e9a37d9f9dc schema:name readcube_id
85 schema:value f03fdd2923fc9bfe90d6357c44da6abbf84fd0596b33ae209449c9a9fc26bb0e
86 rdf:type schema:PropertyValue
87 N5f059268f6ae4ec3b0dc9ffcf4a01ac8 rdf:first sg:person.0617532531.24
88 rdf:rest rdf:nil
89 N6d8b378c8100430faf437f67906c2dd7 schema:volumeNumber 3
90 rdf:type schema:PublicationVolume
91 N7d539e3c3a79420ab4ec895279a4cbea rdf:first sg:person.015226045673.42
92 rdf:rest Nb8c39931d6eb4f7db781bdd71e7fbfb7
93 N99153955990847928dddec2150c5a865 schema:name dimensions_id
94 schema:value pub.1106417588
95 rdf:type schema:PropertyValue
96 N99a95bf483c242c28cb659afebc47025 rdf:first sg:person.010713316073.48
97 rdf:rest N5f059268f6ae4ec3b0dc9ffcf4a01ac8
98 N9cd63c80ab9848e8adaf68dde85bcd41 schema:name Springer Nature - SN SciGraph project
99 rdf:type schema:Organization
100 Nb8c39931d6eb4f7db781bdd71e7fbfb7 rdf:first Ne9c60ac343104276b33178f7738351be
101 rdf:rest N99a95bf483c242c28cb659afebc47025
102 Nc38c7d45db6044d183ccc69335fde802 schema:name pubmed_id
103 schema:value 30839816
104 rdf:type schema:PropertyValue
105 Ne64313d79a534f28a996e92601bc37ff schema:issueNumber 1
106 rdf:type schema:PublicationIssue
107 Ne9c60ac343104276b33178f7738351be schema:affiliation https://www.grid.ac/institutes/grid.260126.1
108 schema:familyName Zhao
109 schema:givenName Junya
110 rdf:type schema:Person
111 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
112 schema:name Information and Computing Sciences
113 rdf:type schema:DefinedTerm
114 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
115 schema:name Artificial Intelligence and Image Processing
116 rdf:type schema:DefinedTerm
117 sg:journal.1158525 schema:issn 2364-8228
118 schema:name Applied Network Science
119 rdf:type schema:Periodical
120 sg:person.010713316073.48 schema:affiliation https://www.grid.ac/institutes/grid.263857.d
121 schema:familyName Ercal
122 schema:givenName Gunes
123 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010713316073.48
124 rdf:type schema:Person
125 sg:person.015226045673.42 schema:affiliation https://www.grid.ac/institutes/grid.263857.d
126 schema:familyName Matta
127 schema:givenName John
128 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015226045673.42
129 rdf:type schema:Person
130 sg:person.0617532531.24 schema:affiliation https://www.grid.ac/institutes/grid.260126.1
131 schema:familyName Obafemi-Ajayi
132 schema:givenName Tayo
133 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0617532531.24
134 rdf:type schema:Person
135 sg:pub.10.1007/978-3-319-72150-7_1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093022510
136 https://doi.org/10.1007/978-3-319-72150-7_1
137 rdf:type schema:CreativeWork
138 sg:pub.10.1007/bf02172145 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017747169
139 https://doi.org/10.1007/bf02172145
140 rdf:type schema:CreativeWork
141 sg:pub.10.1007/bf02172209 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024658676
142 https://doi.org/10.1007/bf02172209
143 rdf:type schema:CreativeWork
144 sg:pub.10.1007/bf02211841 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031175125
145 https://doi.org/10.1007/bf02211841
146 rdf:type schema:CreativeWork
147 sg:pub.10.1007/s10803-007-0469-y schema:sameAs https://app.dimensions.ai/details/publication/pub.1030826570
148 https://doi.org/10.1007/s10803-007-0469-y
149 rdf:type schema:CreativeWork
150 sg:pub.10.1007/s10803-011-1402-y schema:sameAs https://app.dimensions.ai/details/publication/pub.1047366866
151 https://doi.org/10.1007/s10803-011-1402-y
152 rdf:type schema:CreativeWork
153 sg:pub.10.1007/s10803-012-1719-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028107829
154 https://doi.org/10.1007/s10803-012-1719-1
155 rdf:type schema:CreativeWork
156 sg:pub.10.1007/s10803-014-2290-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001744885
157 https://doi.org/10.1007/s10803-014-2290-8
158 rdf:type schema:CreativeWork
159 sg:pub.10.1007/s40474-013-0003-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027385742
160 https://doi.org/10.1007/s40474-013-0003-1
161 rdf:type schema:CreativeWork
162 https://app.dimensions.ai/details/publication/pub.1078886835 schema:CreativeWork
163 https://doi.org/10.1001/archgenpsychiatry.2011.148 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041606585
164 rdf:type schema:CreativeWork
165 https://doi.org/10.1002/sam.10080 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040315874
166 rdf:type schema:CreativeWork
167 https://doi.org/10.1016/j.biopsych.2014.09.017 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042386877
168 rdf:type schema:CreativeWork
169 https://doi.org/10.1016/j.jbi.2017.11.016 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093055098
170 rdf:type schema:CreativeWork
171 https://doi.org/10.1016/j.jrp.2017.11.003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1092637274
172 rdf:type schema:CreativeWork
173 https://doi.org/10.1016/j.neuroimage.2018.05.005 schema:sameAs https://app.dimensions.ai/details/publication/pub.1103796034
174 rdf:type schema:CreativeWork
175 https://doi.org/10.1016/j.neuron.2010.10.006 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018941250
176 rdf:type schema:CreativeWork
177 https://doi.org/10.1016/j.paid.2018.04.003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1103188183
178 rdf:type schema:CreativeWork
179 https://doi.org/10.1016/j.patcog.2006.06.026 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033731968
180 rdf:type schema:CreativeWork
181 https://doi.org/10.1016/j.patcog.2012.07.021 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036814301
182 rdf:type schema:CreativeWork
183 https://doi.org/10.1016/s0166-218x(98)00083-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049878122
184 rdf:type schema:CreativeWork
185 https://doi.org/10.1051/ro/2017008 schema:sameAs https://app.dimensions.ai/details/publication/pub.1083427310
186 rdf:type schema:CreativeWork
187 https://doi.org/10.1073/pnas.0601602103 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016125157
188 rdf:type schema:CreativeWork
189 https://doi.org/10.1080/0022250x.2001.9990249 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032164704
190 rdf:type schema:CreativeWork
191 https://doi.org/10.1093/med/9780195371826.003.0046 schema:sameAs https://app.dimensions.ai/details/publication/pub.1089154572
192 rdf:type schema:CreativeWork
193 https://doi.org/10.1097/00004583-200003000-00017 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034120374
194 rdf:type schema:CreativeWork
195 https://doi.org/10.1097/wco.0000000000000300 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042065607
196 rdf:type schema:CreativeWork
197 https://doi.org/10.1098/rsos.171592 schema:sameAs https://app.dimensions.ai/details/publication/pub.1101525141
198 rdf:type schema:CreativeWork
199 https://doi.org/10.1109/cibcb.2015.7300337 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095172156
200 rdf:type schema:CreativeWork
201 https://doi.org/10.1109/cibcb.2018.8404960 schema:sameAs https://app.dimensions.ai/details/publication/pub.1105446976
202 rdf:type schema:CreativeWork
203 https://doi.org/10.1109/cibcb.2018.8404962 schema:sameAs https://app.dimensions.ai/details/publication/pub.1105445483
204 rdf:type schema:CreativeWork
205 https://doi.org/10.1109/embc.2016.7591440 schema:sameAs https://app.dimensions.ai/details/publication/pub.1084498533
206 rdf:type schema:CreativeWork
207 https://doi.org/10.1109/icdm.2010.35 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094710123
208 rdf:type schema:CreativeWork
209 https://doi.org/10.1109/icdm.2016.0043 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094394929
210 rdf:type schema:CreativeWork
211 https://doi.org/10.1109/icdmw.2008.39 schema:sameAs https://app.dimensions.ai/details/publication/pub.1078184921
212 rdf:type schema:CreativeWork
213 https://doi.org/10.1109/ssci.2017.8280937 schema:sameAs https://app.dimensions.ai/details/publication/pub.1100856522
214 rdf:type schema:CreativeWork
215 https://doi.org/10.1109/tsmcb.2012.2220543 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061797573
216 rdf:type schema:CreativeWork
217 https://doi.org/10.1109/tsmcb.2012.2223671 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061797580
218 rdf:type schema:CreativeWork
219 https://doi.org/10.1111/gbb.12117 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034206569
220 rdf:type schema:CreativeWork
221 https://doi.org/10.1111/j.1469-7610.2012.02588.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1011377672
222 rdf:type schema:CreativeWork
223 https://doi.org/10.1145/1502793.1502794 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021367426
224 rdf:type schema:CreativeWork
225 https://doi.org/10.1145/1656274.1656278 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028526411
226 rdf:type schema:CreativeWork
227 https://doi.org/10.1145/2350190.2350193 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045641931
228 rdf:type schema:CreativeWork
229 https://doi.org/10.1145/3154524 schema:sameAs https://app.dimensions.ai/details/publication/pub.1103387506
230 rdf:type schema:CreativeWork
231 https://doi.org/10.1145/3233547.3233602 schema:sameAs https://app.dimensions.ai/details/publication/pub.1106353758
232 rdf:type schema:CreativeWork
233 https://doi.org/10.1176/appi.books.9780890425596 schema:sameAs https://app.dimensions.ai/details/publication/pub.1097032812
234 rdf:type schema:CreativeWork
235 https://doi.org/10.1201/b15410 schema:sameAs https://app.dimensions.ai/details/publication/pub.1109727642
236 rdf:type schema:CreativeWork
237 https://doi.org/10.1542/peds.2013-0763 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046272385
238 rdf:type schema:CreativeWork
239 https://doi.org/10.2217/npy.13.8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003708782
240 rdf:type schema:CreativeWork
241 https://www.grid.ac/institutes/grid.260126.1 schema:alternateName Missouri State University
242 schema:name Department of Computer Science, Missouri State University, Springfield, MO, USA
243 Engineering Program, Missouri State University, Springfield, MO, USA
244 rdf:type schema:Organization
245 https://www.grid.ac/institutes/grid.263857.d schema:alternateName Southern Illinois University Edwardsville
246 schema:name Department of Computer Science, Southern Illinois University Edwardsville, Edwardsville, IL, USA
247 rdf:type schema:Organization
 




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


...