Ontology type: schema:ScholarlyArticle
2021-11-19
AUTHORSOmar Ibrahim, Heidi G. Sutherland, Rodney A. Lea, Fatima Nasrallah, Neven Maksemous, Robert A. Smith, Larisa M. Haupt, Lyn R. Griffiths
ABSTRACTA percentage of the population suffers prolonged and persistent post-concussion symptoms (PCS) following average head injuries or develops severe neurological dysfunction following minor head trauma. Genetic variants that may contribute to individual response to head trauma have been investigated in some studies, but to date none have explored the use of machine learning (ML) methods with genomic data to specifically explore outcomes of head trauma. Whole exome sequencing (WES) was completed for three groups of individuals (N = 60): (a) 16 individuals with severe neurological responses to minor head trauma, (b) 26 individuals with persistent PCS and (c) 18 individuals with normal recovery from concussion or mTBI. Gradient boosted tree algorithms were applied to the data using XGBoost. By using variants with CADD scores above 15 in the training set (randomly sampled 70%), we identified signatures that accurately distinguish to accurately distinguish the test groups with an average area under the curve (AUC) of 0.8 (SE = 0.019). Metrics including positive and negative prediction values, as well as kappa were all within acceptable range to support the prediction accuracy. This study illustrates how ML methods in combination with WES data have the potential to predict severe or prolonged responses to head trauma from healthy recovery. Key messagesLinear association analysis has been inconclusive in concussion genetics. Non-linear methods as boosted trees can offer better insights in small samples. Strong discrimination trends can be achieved from exome data of cases and controls. More... »
PAGES303-312
http://scigraph.springernature.com/pub.10.1007/s00109-021-02158-z
DOIhttp://dx.doi.org/10.1007/s00109-021-02158-z
DIMENSIONShttps://app.dimensions.ai/details/publication/pub.1142692604
PUBMEDhttps://www.ncbi.nlm.nih.gov/pubmed/34797388
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/2008/03",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Chemical Sciences",
"type": "DefinedTerm"
},
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0304",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Medicinal and Biomolecular Chemistry",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Adult",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Craniocerebral Trauma",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Female",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Genomics",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Humans",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Machine Learning",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Male",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Middle Aged",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Trauma Severity Indices",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Whole Exome Sequencing",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Young Adult",
"type": "DefinedTerm"
}
],
"author": [
{
"affiliation": {
"alternateName": "Genomics Research Centre, Centre for Genomics and Personalised Health, School of Biomedical Sciences, Queensland University of Technology, 60 Musk Ave, 4059, Kelvin Grove, QLD, Australia",
"id": "http://www.grid.ac/institutes/grid.1024.7",
"name": [
"Genomics Research Centre, Centre for Genomics and Personalised Health, School of Biomedical Sciences, Queensland University of Technology, 60 Musk Ave, 4059, Kelvin Grove, QLD, Australia"
],
"type": "Organization"
},
"familyName": "Ibrahim",
"givenName": "Omar",
"id": "sg:person.012266776767.75",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012266776767.75"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Genomics Research Centre, Centre for Genomics and Personalised Health, School of Biomedical Sciences, Queensland University of Technology, 60 Musk Ave, 4059, Kelvin Grove, QLD, Australia",
"id": "http://www.grid.ac/institutes/grid.1024.7",
"name": [
"Genomics Research Centre, Centre for Genomics and Personalised Health, School of Biomedical Sciences, Queensland University of Technology, 60 Musk Ave, 4059, Kelvin Grove, QLD, Australia"
],
"type": "Organization"
},
"familyName": "Sutherland",
"givenName": "Heidi G.",
"id": "sg:person.0642646046.42",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0642646046.42"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Genomics Research Centre, Centre for Genomics and Personalised Health, School of Biomedical Sciences, Queensland University of Technology, 60 Musk Ave, 4059, Kelvin Grove, QLD, Australia",
"id": "http://www.grid.ac/institutes/grid.1024.7",
"name": [
"Genomics Research Centre, Centre for Genomics and Personalised Health, School of Biomedical Sciences, Queensland University of Technology, 60 Musk Ave, 4059, Kelvin Grove, QLD, Australia"
],
"type": "Organization"
},
"familyName": "Lea",
"givenName": "Rodney A.",
"id": "sg:person.012075027362.12",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012075027362.12"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "The Queensland Brain Institute, The University of Queensland, Brisbane, Australia",
"id": "http://www.grid.ac/institutes/grid.1003.2",
"name": [
"The Queensland Brain Institute, The University of Queensland, Brisbane, Australia"
],
"type": "Organization"
},
"familyName": "Nasrallah",
"givenName": "Fatima",
"id": "sg:person.01033155751.49",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01033155751.49"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Genomics Research Centre, Centre for Genomics and Personalised Health, School of Biomedical Sciences, Queensland University of Technology, 60 Musk Ave, 4059, Kelvin Grove, QLD, Australia",
"id": "http://www.grid.ac/institutes/grid.1024.7",
"name": [
"Genomics Research Centre, Centre for Genomics and Personalised Health, School of Biomedical Sciences, Queensland University of Technology, 60 Musk Ave, 4059, Kelvin Grove, QLD, Australia"
],
"type": "Organization"
},
"familyName": "Maksemous",
"givenName": "Neven",
"id": "sg:person.0762233475.72",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0762233475.72"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Genomics Research Centre, Centre for Genomics and Personalised Health, School of Biomedical Sciences, Queensland University of Technology, 60 Musk Ave, 4059, Kelvin Grove, QLD, Australia",
"id": "http://www.grid.ac/institutes/grid.1024.7",
"name": [
"Genomics Research Centre, Centre for Genomics and Personalised Health, School of Biomedical Sciences, Queensland University of Technology, 60 Musk Ave, 4059, Kelvin Grove, QLD, Australia"
],
"type": "Organization"
},
"familyName": "Smith",
"givenName": "Robert A.",
"id": "sg:person.01130665536.98",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01130665536.98"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Genomics Research Centre, Centre for Genomics and Personalised Health, School of Biomedical Sciences, Queensland University of Technology, 60 Musk Ave, 4059, Kelvin Grove, QLD, Australia",
"id": "http://www.grid.ac/institutes/grid.1024.7",
"name": [
"Genomics Research Centre, Centre for Genomics and Personalised Health, School of Biomedical Sciences, Queensland University of Technology, 60 Musk Ave, 4059, Kelvin Grove, QLD, Australia"
],
"type": "Organization"
},
"familyName": "Haupt",
"givenName": "Larisa M.",
"id": "sg:person.0755160300.17",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0755160300.17"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Genomics Research Centre, Centre for Genomics and Personalised Health, School of Biomedical Sciences, Queensland University of Technology, 60 Musk Ave, 4059, Kelvin Grove, QLD, Australia",
"id": "http://www.grid.ac/institutes/grid.1024.7",
"name": [
"Genomics Research Centre, Centre for Genomics and Personalised Health, School of Biomedical Sciences, Queensland University of Technology, 60 Musk Ave, 4059, Kelvin Grove, QLD, Australia"
],
"type": "Organization"
},
"familyName": "Griffiths",
"givenName": "Lyn R.",
"id": "sg:person.01216564673.39",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01216564673.39"
],
"type": "Person"
}
],
"citation": [
{
"id": "sg:pub.10.1038/s41598-017-13056-1",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1092023331",
"https://doi.org/10.1038/s41598-017-13056-1"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1186/s13073-019-0618-x",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1112168838",
"https://doi.org/10.1186/s13073-019-0618-x"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/ng.608",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1015057090",
"https://doi.org/10.1038/ng.608"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/tp.2015.65",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1051546512",
"https://doi.org/10.1038/tp.2015.65"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/s41598-019-46649-z",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1118044228",
"https://doi.org/10.1038/s41598-019-46649-z"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s12640-009-9044-y",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1044590250",
"https://doi.org/10.1007/s12640-009-9044-y"
],
"type": "CreativeWork"
}
],
"datePublished": "2021-11-19",
"datePublishedReg": "2021-11-19",
"description": "A percentage of the population suffers prolonged and persistent post-concussion symptoms (PCS) following average head injuries or develops severe neurological dysfunction following minor head trauma. Genetic variants that may contribute to individual response to head trauma have been investigated in some studies, but to date none have explored the use of machine learning (ML) methods with genomic data to specifically explore outcomes of head trauma. Whole exome sequencing (WES) was completed for three groups of individuals (N\u2009=\u200960): (a) 16 individuals with severe neurological responses to minor head trauma, (b) 26 individuals with persistent PCS and (c) 18 individuals with normal recovery from concussion or mTBI. Gradient boosted tree algorithms were applied to the data using XGBoost. By using variants with CADD scores above 15 in the training set (randomly sampled 70%), we identified signatures that accurately distinguish to accurately distinguish the test groups with an average area under the curve (AUC) of 0.8 (SE\u2009=\u20090.019). Metrics including positive and negative prediction values, as well as kappa were all within acceptable range to support the prediction accuracy. This study illustrates how ML methods in combination with WES data have the potential to predict severe or prolonged responses to head trauma from healthy recovery.\nKey messagesLinear association analysis has been inconclusive in concussion genetics. Non-linear methods as boosted trees can offer better insights in small samples. Strong discrimination trends can be achieved from exome data of cases and controls.",
"genre": "article",
"id": "sg:pub.10.1007/s00109-021-02158-z",
"inLanguage": "en",
"isAccessibleForFree": false,
"isFundedItemOf": [
{
"id": "sg:grant.7877842",
"type": "MonetaryGrant"
}
],
"isPartOf": [
{
"id": "sg:journal.1057918",
"issn": [
"0946-2716",
"1432-1440"
],
"name": "Journal of Molecular Medicine",
"publisher": "Springer Nature",
"type": "Periodical"
},
{
"issueNumber": "2",
"type": "PublicationIssue"
},
{
"type": "PublicationVolume",
"volumeNumber": "100"
}
],
"keywords": [
"persistent post-concussion symptoms",
"persistent PCS",
"post-concussion symptoms",
"group of individuals",
"healthy recovery",
"neurological responses",
"discrimination trends",
"individual responses",
"individuals",
"minor head trauma",
"head trauma",
"trauma outcomes",
"trauma",
"learning methods",
"whole-exome sequencing",
"machine learning methods",
"machine learning",
"mTBI",
"small samples",
"prolonged response",
"learning",
"training set",
"severe neurological dysfunction",
"ML methods",
"head injury",
"neurological dysfunction",
"outcomes",
"concussion",
"negative prediction value",
"scores",
"normal recovery",
"exome sequencing",
"test group",
"symptoms",
"response",
"prediction accuracy",
"group",
"genetic variants",
"study",
"WES data",
"exome data",
"CADD scores",
"better insight",
"dysfunction",
"samples",
"data",
"injury",
"insights",
"recovery",
"accuracy",
"association analysis",
"variants",
"average area",
"kappa",
"control",
"date",
"XGBoost",
"population",
"set",
"acceptable range",
"percentage",
"use",
"metrics",
"sequencing",
"cases",
"analysis",
"prediction value",
"area",
"genetics",
"potential",
"method",
"combination",
"non-linear methods",
"curves",
"range",
"trends",
"signatures",
"genomic data",
"values",
"genomics",
"gradient",
"tree algorithm",
"algorithm",
"trees"
],
"name": "Discriminating head trauma outcomes using machine learning and genomics",
"pagination": "303-312",
"productId": [
{
"name": "dimensions_id",
"type": "PropertyValue",
"value": [
"pub.1142692604"
]
},
{
"name": "doi",
"type": "PropertyValue",
"value": [
"10.1007/s00109-021-02158-z"
]
},
{
"name": "pubmed_id",
"type": "PropertyValue",
"value": [
"34797388"
]
}
],
"sameAs": [
"https://doi.org/10.1007/s00109-021-02158-z",
"https://app.dimensions.ai/details/publication/pub.1142692604"
],
"sdDataset": "articles",
"sdDatePublished": "2022-05-20T07:39",
"sdLicense": "https://scigraph.springernature.com/explorer/license/",
"sdPublisher": {
"name": "Springer Nature - SN SciGraph project",
"type": "Organization"
},
"sdSource": "s3://com-springernature-scigraph/baseset/20220519/entities/gbq_results/article/article_911.jsonl",
"type": "ScholarlyArticle",
"url": "https://doi.org/10.1007/s00109-021-02158-z"
}
]Download the RDF metadata as: json-ld nt turtle xml License info
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/s00109-021-02158-z'
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/s00109-021-02158-z'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00109-021-02158-z'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00109-021-02158-z'
This table displays all metadata directly associated to this object as RDF triples.
268 TRIPLES
22 PREDICATES
127 URIs
113 LITERALS
18 BLANK NODES