MAVE-NN: learning genotype-phenotype maps from multiplex assays of variant effect View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2022-04-15

AUTHORS

Ammar Tareen, Mahdi Kooshkbaghi, Anna Posfai, William T. Ireland, David M. McCandlish, Justin B. Kinney

ABSTRACT

Multiplex assays of variant effect (MAVEs) are a family of methods that includes deep mutational scanning experiments on proteins and massively parallel reporter assays on gene regulatory sequences. Despite their increasing popularity, a general strategy for inferring quantitative models of genotype-phenotype maps from MAVE data is lacking. Here we introduce MAVE-NN, a neural-network-based Python package that implements a broadly applicable information-theoretic framework for learning genotype-phenotype maps—including biophysically interpretable models—from MAVE datasets. We demonstrate MAVE-NN in multiple biological contexts, and highlight the ability of our approach to deconvolve mutational effects from otherwise confounding experimental nonlinearities and noise. More... »

PAGES

98

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s13059-022-02661-7

DOI

http://dx.doi.org/10.1186/s13059-022-02661-7

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1147152140

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/35428271


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/2008/05", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Environmental Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/06", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Biological Sciences", 
        "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"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Biological Assay", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Genotype", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Mutation", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Neural Networks, Computer", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Phenotype", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Regeneron Pharmaceuticals, Inc., 10591, Tarrytown, NY, USA", 
          "id": "http://www.grid.ac/institutes/grid.418961.3", 
          "name": [
            "Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, 11724, Cold Spring Harbor, NY, USA", 
            "Regeneron Pharmaceuticals, Inc., 10591, Tarrytown, NY, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Tareen", 
        "givenName": "Ammar", 
        "id": "sg:person.013251564270.26", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013251564270.26"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, 11724, Cold Spring Harbor, NY, USA", 
          "id": "http://www.grid.ac/institutes/grid.225279.9", 
          "name": [
            "Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, 11724, Cold Spring Harbor, NY, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kooshkbaghi", 
        "givenName": "Mahdi", 
        "id": "sg:person.01350413630.34", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01350413630.34"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, 11724, Cold Spring Harbor, NY, USA", 
          "id": "http://www.grid.ac/institutes/grid.225279.9", 
          "name": [
            "Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, 11724, Cold Spring Harbor, NY, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Posfai", 
        "givenName": "Anna", 
        "id": "sg:person.013640327023.51", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013640327023.51"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Applied Physics, Harvard University, 02134, Cambridge, MA, USA", 
          "id": "http://www.grid.ac/institutes/grid.38142.3c", 
          "name": [
            "Department of Physics, California Institute of Technology, 91125, Pasadena, CA, USA", 
            "Department of Applied Physics, Harvard University, 02134, Cambridge, MA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Ireland", 
        "givenName": "William T.", 
        "id": "sg:person.010142655153.01", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010142655153.01"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, 11724, Cold Spring Harbor, NY, USA", 
          "id": "http://www.grid.ac/institutes/grid.225279.9", 
          "name": [
            "Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, 11724, Cold Spring Harbor, NY, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "McCandlish", 
        "givenName": "David M.", 
        "id": "sg:person.01101744274.75", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01101744274.75"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, 11724, Cold Spring Harbor, NY, USA", 
          "id": "http://www.grid.ac/institutes/grid.225279.9", 
          "name": [
            "Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, 11724, Cold Spring Harbor, NY, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kinney", 
        "givenName": "Justin B.", 
        "id": "sg:person.01070345415.05", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01070345415.05"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1186/s13059-017-1272-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1091081281", 
          "https://doi.org/10.1186/s13059-017-1272-5"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-540-30499-9_83", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023523458", 
          "https://doi.org/10.1007/978-3-540-30499-9_83"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nbt.2137", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004566758", 
          "https://doi.org/10.1038/nbt.2137"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nature17995", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008147636", 
          "https://doi.org/10.1038/nature17995"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/s12859-015-0590-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038764045", 
          "https://doi.org/10.1186/s12859-015-0590-4"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nmeth.3027", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031324326", 
          "https://doi.org/10.1038/nmeth.3027"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/s13059-020-02091-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1130150025", 
          "https://doi.org/10.1186/s13059-020-02091-3"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10955-015-1398-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023749949", 
          "https://doi.org/10.1007/s10955-015-1398-3"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/s41467-019-12101-z", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1121003562", 
          "https://doi.org/10.1038/s41467-019-12101-z"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/s12864-016-2533-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049252653", 
          "https://doi.org/10.1186/s12864-016-2533-5"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nrg3684", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025954650", 
          "https://doi.org/10.1038/nrg3684"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/s13059-019-1787-z", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1120775259", 
          "https://doi.org/10.1186/s13059-019-1787-z"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2022-04-15", 
    "datePublishedReg": "2022-04-15", 
    "description": "Multiplex assays of variant effect (MAVEs) are a family of methods that includes deep mutational scanning experiments on proteins and massively parallel reporter assays on gene regulatory sequences. Despite their increasing popularity, a general strategy for inferring quantitative models of genotype-phenotype maps from MAVE data is lacking. Here we introduce MAVE-NN, a neural-network-based Python package that implements a broadly applicable information-theoretic framework for learning genotype-phenotype maps\u2014including biophysically interpretable models\u2014from MAVE datasets. We demonstrate MAVE-NN in multiple biological contexts, and highlight the ability of our approach to deconvolve mutational effects from otherwise confounding experimental nonlinearities and noise.", 
    "genre": "article", 
    "id": "sg:pub.10.1186/s13059-022-02661-7", 
    "inLanguage": "en", 
    "isAccessibleForFree": true, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.8632182", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.8633987", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": [
      {
        "id": "sg:journal.1023439", 
        "issn": [
          "1474-760X", 
          "1465-6906"
        ], 
        "name": "Genome Biology", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "23"
      }
    ], 
    "keywords": [
      "genotype-phenotype map", 
      "deep mutational scanning experiments", 
      "variant effects", 
      "gene regulatory sequences", 
      "multiple biological contexts", 
      "parallel reporter", 
      "regulatory sequences", 
      "mutational effects", 
      "biological context", 
      "multiplex assay", 
      "assays", 
      "reporter", 
      "protein", 
      "scanning experiments", 
      "general strategy", 
      "Python package", 
      "sequence", 
      "family", 
      "experimental nonlinearity", 
      "information-theoretic framework", 
      "quantitative model", 
      "maps", 
      "ability", 
      "effect", 
      "experiments", 
      "strategies", 
      "dataset", 
      "data", 
      "model", 
      "approach", 
      "context", 
      "interpretable models", 
      "package", 
      "method", 
      "family of methods", 
      "framework", 
      "popularity", 
      "noise", 
      "nonlinearity"
    ], 
    "name": "MAVE-NN: learning genotype-phenotype maps from multiplex assays of variant effect", 
    "pagination": "98", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1147152140"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1186/s13059-022-02661-7"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "35428271"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1186/s13059-022-02661-7", 
      "https://app.dimensions.ai/details/publication/pub.1147152140"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2022-06-01T22:25", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20220601/entities/gbq_results/article/article_934.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1186/s13059-022-02661-7"
  }
]
 

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.10.1186/s13059-022-02661-7'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1186/s13059-022-02661-7'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/s13059-022-02661-7'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/s13059-022-02661-7'


 

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

220 TRIPLES      22 PREDICATES      83 URIs      62 LITERALS      12 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1186/s13059-022-02661-7 schema:about N1a35fd7e96644592823185ce69b3f6ea
2 N21a054ecbb444bab90c8728b5c60d7eb
3 N7618ab848ff24344b455390cb28e1b9c
4 N86eb9df2f13e41fcaf7c9dd3cc5945e1
5 Nb669fbaf6a1b4b96a72967caa94b502b
6 anzsrc-for:05
7 anzsrc-for:06
8 anzsrc-for:08
9 schema:author N6f9fefe2c59e47d9b6bacc238b696b00
10 schema:citation sg:pub.10.1007/978-3-540-30499-9_83
11 sg:pub.10.1007/s10955-015-1398-3
12 sg:pub.10.1038/nature17995
13 sg:pub.10.1038/nbt.2137
14 sg:pub.10.1038/nmeth.3027
15 sg:pub.10.1038/nrg3684
16 sg:pub.10.1038/s41467-019-12101-z
17 sg:pub.10.1186/s12859-015-0590-4
18 sg:pub.10.1186/s12864-016-2533-5
19 sg:pub.10.1186/s13059-017-1272-5
20 sg:pub.10.1186/s13059-019-1787-z
21 sg:pub.10.1186/s13059-020-02091-3
22 schema:datePublished 2022-04-15
23 schema:datePublishedReg 2022-04-15
24 schema:description Multiplex assays of variant effect (MAVEs) are a family of methods that includes deep mutational scanning experiments on proteins and massively parallel reporter assays on gene regulatory sequences. Despite their increasing popularity, a general strategy for inferring quantitative models of genotype-phenotype maps from MAVE data is lacking. Here we introduce MAVE-NN, a neural-network-based Python package that implements a broadly applicable information-theoretic framework for learning genotype-phenotype maps—including biophysically interpretable models—from MAVE datasets. We demonstrate MAVE-NN in multiple biological contexts, and highlight the ability of our approach to deconvolve mutational effects from otherwise confounding experimental nonlinearities and noise.
25 schema:genre article
26 schema:inLanguage en
27 schema:isAccessibleForFree true
28 schema:isPartOf N7252b8a5b1984e7d93d31eaba909a85b
29 Nb51c3d844cea4b7ab88ff40bea76f7b6
30 sg:journal.1023439
31 schema:keywords Python package
32 ability
33 approach
34 assays
35 biological context
36 context
37 data
38 dataset
39 deep mutational scanning experiments
40 effect
41 experimental nonlinearity
42 experiments
43 family
44 family of methods
45 framework
46 gene regulatory sequences
47 general strategy
48 genotype-phenotype map
49 information-theoretic framework
50 interpretable models
51 maps
52 method
53 model
54 multiple biological contexts
55 multiplex assay
56 mutational effects
57 noise
58 nonlinearity
59 package
60 parallel reporter
61 popularity
62 protein
63 quantitative model
64 regulatory sequences
65 reporter
66 scanning experiments
67 sequence
68 strategies
69 variant effects
70 schema:name MAVE-NN: learning genotype-phenotype maps from multiplex assays of variant effect
71 schema:pagination 98
72 schema:productId N51b2d3ea66b94a3db7c2f6331629e1ab
73 Ne84a948f5f7e47fca79e479de5740892
74 Neabd9531191c4e1cacf14092e9b1914f
75 schema:sameAs https://app.dimensions.ai/details/publication/pub.1147152140
76 https://doi.org/10.1186/s13059-022-02661-7
77 schema:sdDatePublished 2022-06-01T22:25
78 schema:sdLicense https://scigraph.springernature.com/explorer/license/
79 schema:sdPublisher N1c08a0e2c8be4ea3a8185a2e5515da70
80 schema:url https://doi.org/10.1186/s13059-022-02661-7
81 sgo:license sg:explorer/license/
82 sgo:sdDataset articles
83 rdf:type schema:ScholarlyArticle
84 N1a204097b52645ebbe1eceb619c57a02 rdf:first sg:person.013640327023.51
85 rdf:rest N246e47732c5243c28df602806493e76a
86 N1a35fd7e96644592823185ce69b3f6ea schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
87 schema:name Biological Assay
88 rdf:type schema:DefinedTerm
89 N1c08a0e2c8be4ea3a8185a2e5515da70 schema:name Springer Nature - SN SciGraph project
90 rdf:type schema:Organization
91 N21a054ecbb444bab90c8728b5c60d7eb schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
92 schema:name Neural Networks, Computer
93 rdf:type schema:DefinedTerm
94 N246e47732c5243c28df602806493e76a rdf:first sg:person.010142655153.01
95 rdf:rest N75d15713d6a84bd6a92a9eb4165b4510
96 N51b2d3ea66b94a3db7c2f6331629e1ab schema:name pubmed_id
97 schema:value 35428271
98 rdf:type schema:PropertyValue
99 N6f9fefe2c59e47d9b6bacc238b696b00 rdf:first sg:person.013251564270.26
100 rdf:rest Ned4237d833874875940e9d2945163721
101 N7252b8a5b1984e7d93d31eaba909a85b schema:issueNumber 1
102 rdf:type schema:PublicationIssue
103 N75d15713d6a84bd6a92a9eb4165b4510 rdf:first sg:person.01101744274.75
104 rdf:rest Naa2a1413cf0640599969e50fde2cff81
105 N7618ab848ff24344b455390cb28e1b9c schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
106 schema:name Genotype
107 rdf:type schema:DefinedTerm
108 N86eb9df2f13e41fcaf7c9dd3cc5945e1 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
109 schema:name Mutation
110 rdf:type schema:DefinedTerm
111 Naa2a1413cf0640599969e50fde2cff81 rdf:first sg:person.01070345415.05
112 rdf:rest rdf:nil
113 Nb51c3d844cea4b7ab88ff40bea76f7b6 schema:volumeNumber 23
114 rdf:type schema:PublicationVolume
115 Nb669fbaf6a1b4b96a72967caa94b502b schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
116 schema:name Phenotype
117 rdf:type schema:DefinedTerm
118 Ne84a948f5f7e47fca79e479de5740892 schema:name dimensions_id
119 schema:value pub.1147152140
120 rdf:type schema:PropertyValue
121 Neabd9531191c4e1cacf14092e9b1914f schema:name doi
122 schema:value 10.1186/s13059-022-02661-7
123 rdf:type schema:PropertyValue
124 Ned4237d833874875940e9d2945163721 rdf:first sg:person.01350413630.34
125 rdf:rest N1a204097b52645ebbe1eceb619c57a02
126 anzsrc-for:05 schema:inDefinedTermSet anzsrc-for:
127 schema:name Environmental Sciences
128 rdf:type schema:DefinedTerm
129 anzsrc-for:06 schema:inDefinedTermSet anzsrc-for:
130 schema:name Biological Sciences
131 rdf:type schema:DefinedTerm
132 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
133 schema:name Information and Computing Sciences
134 rdf:type schema:DefinedTerm
135 sg:grant.8632182 http://pending.schema.org/fundedItem sg:pub.10.1186/s13059-022-02661-7
136 rdf:type schema:MonetaryGrant
137 sg:grant.8633987 http://pending.schema.org/fundedItem sg:pub.10.1186/s13059-022-02661-7
138 rdf:type schema:MonetaryGrant
139 sg:journal.1023439 schema:issn 1465-6906
140 1474-760X
141 schema:name Genome Biology
142 schema:publisher Springer Nature
143 rdf:type schema:Periodical
144 sg:person.010142655153.01 schema:affiliation grid-institutes:grid.38142.3c
145 schema:familyName Ireland
146 schema:givenName William T.
147 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010142655153.01
148 rdf:type schema:Person
149 sg:person.01070345415.05 schema:affiliation grid-institutes:grid.225279.9
150 schema:familyName Kinney
151 schema:givenName Justin B.
152 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01070345415.05
153 rdf:type schema:Person
154 sg:person.01101744274.75 schema:affiliation grid-institutes:grid.225279.9
155 schema:familyName McCandlish
156 schema:givenName David M.
157 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01101744274.75
158 rdf:type schema:Person
159 sg:person.013251564270.26 schema:affiliation grid-institutes:grid.418961.3
160 schema:familyName Tareen
161 schema:givenName Ammar
162 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013251564270.26
163 rdf:type schema:Person
164 sg:person.01350413630.34 schema:affiliation grid-institutes:grid.225279.9
165 schema:familyName Kooshkbaghi
166 schema:givenName Mahdi
167 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01350413630.34
168 rdf:type schema:Person
169 sg:person.013640327023.51 schema:affiliation grid-institutes:grid.225279.9
170 schema:familyName Posfai
171 schema:givenName Anna
172 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013640327023.51
173 rdf:type schema:Person
174 sg:pub.10.1007/978-3-540-30499-9_83 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023523458
175 https://doi.org/10.1007/978-3-540-30499-9_83
176 rdf:type schema:CreativeWork
177 sg:pub.10.1007/s10955-015-1398-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023749949
178 https://doi.org/10.1007/s10955-015-1398-3
179 rdf:type schema:CreativeWork
180 sg:pub.10.1038/nature17995 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008147636
181 https://doi.org/10.1038/nature17995
182 rdf:type schema:CreativeWork
183 sg:pub.10.1038/nbt.2137 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004566758
184 https://doi.org/10.1038/nbt.2137
185 rdf:type schema:CreativeWork
186 sg:pub.10.1038/nmeth.3027 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031324326
187 https://doi.org/10.1038/nmeth.3027
188 rdf:type schema:CreativeWork
189 sg:pub.10.1038/nrg3684 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025954650
190 https://doi.org/10.1038/nrg3684
191 rdf:type schema:CreativeWork
192 sg:pub.10.1038/s41467-019-12101-z schema:sameAs https://app.dimensions.ai/details/publication/pub.1121003562
193 https://doi.org/10.1038/s41467-019-12101-z
194 rdf:type schema:CreativeWork
195 sg:pub.10.1186/s12859-015-0590-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038764045
196 https://doi.org/10.1186/s12859-015-0590-4
197 rdf:type schema:CreativeWork
198 sg:pub.10.1186/s12864-016-2533-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049252653
199 https://doi.org/10.1186/s12864-016-2533-5
200 rdf:type schema:CreativeWork
201 sg:pub.10.1186/s13059-017-1272-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1091081281
202 https://doi.org/10.1186/s13059-017-1272-5
203 rdf:type schema:CreativeWork
204 sg:pub.10.1186/s13059-019-1787-z schema:sameAs https://app.dimensions.ai/details/publication/pub.1120775259
205 https://doi.org/10.1186/s13059-019-1787-z
206 rdf:type schema:CreativeWork
207 sg:pub.10.1186/s13059-020-02091-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1130150025
208 https://doi.org/10.1186/s13059-020-02091-3
209 rdf:type schema:CreativeWork
210 grid-institutes:grid.225279.9 schema:alternateName Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, 11724, Cold Spring Harbor, NY, USA
211 schema:name Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, 11724, Cold Spring Harbor, NY, USA
212 rdf:type schema:Organization
213 grid-institutes:grid.38142.3c schema:alternateName Department of Applied Physics, Harvard University, 02134, Cambridge, MA, USA
214 schema:name Department of Applied Physics, Harvard University, 02134, Cambridge, MA, USA
215 Department of Physics, California Institute of Technology, 91125, Pasadena, CA, USA
216 rdf:type schema:Organization
217 grid-institutes:grid.418961.3 schema:alternateName Regeneron Pharmaceuticals, Inc., 10591, Tarrytown, NY, USA
218 schema:name Regeneron Pharmaceuticals, Inc., 10591, Tarrytown, NY, USA
219 Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, 11724, Cold Spring Harbor, NY, USA
220 rdf:type schema:Organization
 




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


...