2016-01-07T00:00
AUTHORSLERNER JEFFREY
ABSTRACTTo determine a peak signal corresponding to a particular part of a sample, a data signal for a cluster can be transformed into a set of localized functions. For example, the data signal can be transformed to obtain coefficients of a set of wavelets, which can span a variety of scales that define an exponential decay of the wavelet. The coefficients in a time region for which a peak signal is to be obtained can be replaced with coefficients that model the behavior of tails of other peaks. The tail model can be determined using nonlinear regression, which may need to only be performed once. An inverse transform can then provide a background signal that can be subtracted from the input data signal to provide the desired peak signal. More... »
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/2008/08",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"type": "DefinedTerm"
},
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0801",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"type": "DefinedTerm"
}
],
"author": [
{
"name": "LERNER JEFFREY",
"type": "Person"
}
],
"citation": [
{
"id": "sg:pub.10.1038/nmeth.2027",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1052385246",
"https://doi.org/10.1038/nmeth.2027"
],
"type": "CreativeWork"
}
],
"datePublished": "2016-01-07T00:00",
"description": "To determine a peak signal corresponding to a particular part of a sample, a data signal for a cluster can be transformed into a set of localized functions. For example, the data signal can be transformed to obtain coefficients of a set of wavelets, which can span a variety of scales that define an exponential decay of the wavelet. The coefficients in a time region for which a peak signal is to be obtained can be replaced with coefficients that model the behavior of tails of other peaks. The tail model can be determined using nonlinear regression, which may need to only be performed once. An inverse transform can then provide a background signal that can be subtracted from the input data signal to provide the desired peak signal.
",
"id": "sg:patent.US-20160004815-A1",
"name": "DECONSTRUCTING OVERLAPPED PEAKS IN EXPERIMENTAL DATA",
"recipient": [
{
"id": "http://www.grid.ac/institutes/grid.418312.d",
"type": "Organization"
}
],
"sameAs": [
"https://app.dimensions.ai/details/patent/US-20160004815-A1"
],
"sdDataset": "patents",
"sdDatePublished": "2022-05-20T07:52",
"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/patent/patent_23.jsonl",
"type": "Patent"
}
]
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/patent.US-20160004815-A1'
N-Triples is a line-based linked data format ideal for batch operations.
curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/patent.US-20160004815-A1'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/patent.US-20160004815-A1'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/patent.US-20160004815-A1'
This table displays all metadata directly associated to this object as RDF triples.
29 TRIPLES
14 PREDICATES
16 URIs
8 LITERALS
2 BLANK NODES
Subject | Predicate | Object | |
---|---|---|---|
1 | sg:patent.US-20160004815-A1 | schema:about | anzsrc-for:08 |
2 | ″ | ″ | anzsrc-for:0801 |
3 | ″ | schema:author | Nf662eebdca0d4429823234546feb4651 |
4 | ″ | schema:citation | sg:pub.10.1038/nmeth.2027 |
5 | ″ | schema:datePublished | 2016-01-07T00:00 |
6 | ″ | schema:description | <p id="p-0001" num="0000">To determine a peak signal corresponding to a particular part of a sample, a data signal for a cluster can be transformed into a set of localized functions. For example, the data signal can be transformed to obtain coefficients of a set of wavelets, which can span a variety of scales that define an exponential decay of the wavelet. The coefficients in a time region for which a peak signal is to be obtained can be replaced with coefficients that model the behavior of tails of other peaks. The tail model can be determined using nonlinear regression, which may need to only be performed once. An inverse transform can then provide a background signal that can be subtracted from the input data signal to provide the desired peak signal.</p> |
7 | ″ | schema:name | DECONSTRUCTING OVERLAPPED PEAKS IN EXPERIMENTAL DATA |
8 | ″ | schema:recipient | grid-institutes:grid.418312.d |
9 | ″ | schema:sameAs | https://app.dimensions.ai/details/patent/US-20160004815-A1 |
10 | ″ | schema:sdDatePublished | 2022-05-20T07:52 |
11 | ″ | schema:sdLicense | https://scigraph.springernature.com/explorer/license/ |
12 | ″ | schema:sdPublisher | Nf14c9d7a4e9a4f148065b9b8ceec522d |
13 | ″ | sgo:license | sg:explorer/license/ |
14 | ″ | sgo:sdDataset | patents |
15 | ″ | rdf:type | sgo:Patent |
16 | Ndf5e9e2a97ee46c8b46c08c117e1cbf1 | schema:name | LERNER JEFFREY |
17 | ″ | rdf:type | schema:Person |
18 | Nf14c9d7a4e9a4f148065b9b8ceec522d | schema:name | Springer Nature - SN SciGraph project |
19 | ″ | rdf:type | schema:Organization |
20 | Nf662eebdca0d4429823234546feb4651 | rdf:first | Ndf5e9e2a97ee46c8b46c08c117e1cbf1 |
21 | ″ | rdf:rest | rdf:nil |
22 | anzsrc-for:08 | schema:inDefinedTermSet | anzsrc-for: |
23 | ″ | rdf:type | schema:DefinedTerm |
24 | anzsrc-for:0801 | schema:inDefinedTermSet | anzsrc-for: |
25 | ″ | rdf:type | schema:DefinedTerm |
26 | sg:pub.10.1038/nmeth.2027 | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1052385246 |
27 | ″ | ″ | https://doi.org/10.1038/nmeth.2027 |
28 | ″ | rdf:type | schema:CreativeWork |
29 | grid-institutes:grid.418312.d | ″ | schema:Organization |