Recovering the Imperfect: Cell Segmentation in the Presence of Dynamically Localized Proteins View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2020-10-02

AUTHORS

Özgün Çiçek , Yassine Marrakchi , Enoch Boasiako Antwi , Barbara Di Ventura , Thomas Brox

ABSTRACT

Deploying off-the-shelf segmentation networks on biomedical data has become common practice, yet if structures of interest in an image sequence are visible only temporarily, existing frame-by-frame methods fail. In this paper, we provide a solution to segmentation of imperfect data through time based on temporal propagation and uncertainty estimation. We integrate uncertainty estimation into Mask R-CNN network and propagate motion-corrected segmentation masks from frames with low uncertainty to those frames with high uncertainty to handle temporary loss of signal for segmentation. We demonstrate the value of this approach over frame-by-frame segmentation and regular temporal propagation on data from human embryonic kidney (HEK293T) cells transiently transfected with a fluorescent protein that moves in and out of the nucleus over time. The method presented here will empower microscopic experiments aimed at understanding molecular and cellular function. More... »

PAGES

85-93

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-030-61166-8_9

DOI

http://dx.doi.org/10.1007/978-3-030-61166-8_9

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1131419466


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/2008/08", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Information and Computing Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "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"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "University of Freiburg, Freiburg, Germany", 
          "id": "http://www.grid.ac/institutes/grid.5963.9", 
          "name": [
            "University of Freiburg, Freiburg, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "\u00c7i\u00e7ek", 
        "givenName": "\u00d6zg\u00fcn", 
        "id": "sg:person.016314276446.01", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016314276446.01"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Signalling Research Centres BIOSS and CIBSS, Freiburg, Germany", 
          "id": "http://www.grid.ac/institutes/grid.5963.9", 
          "name": [
            "University of Freiburg, Freiburg, Germany", 
            "Signalling Research Centres BIOSS and CIBSS, Freiburg, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Marrakchi", 
        "givenName": "Yassine", 
        "id": "sg:person.014511262624.07", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014511262624.07"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Heidelberg Biosciences International Graduate School (HBIGS), Heidelberg, Germany", 
          "id": "http://www.grid.ac/institutes/grid.7700.0", 
          "name": [
            "University of Freiburg, Freiburg, Germany", 
            "Signalling Research Centres BIOSS and CIBSS, Freiburg, Germany", 
            "Heidelberg Biosciences International Graduate School (HBIGS), Heidelberg, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Boasiako Antwi", 
        "givenName": "Enoch", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Signalling Research Centres BIOSS and CIBSS, Freiburg, Germany", 
          "id": "http://www.grid.ac/institutes/grid.5963.9", 
          "name": [
            "University of Freiburg, Freiburg, Germany", 
            "Signalling Research Centres BIOSS and CIBSS, Freiburg, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Di Ventura", 
        "givenName": "Barbara", 
        "id": "sg:person.01232522175.56", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01232522175.56"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Signalling Research Centres BIOSS and CIBSS, Freiburg, Germany", 
          "id": "http://www.grid.ac/institutes/grid.5963.9", 
          "name": [
            "University of Freiburg, Freiburg, Germany", 
            "Signalling Research Centres BIOSS and CIBSS, Freiburg, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Brox", 
        "givenName": "Thomas", 
        "id": "sg:person.012443225372.65", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012443225372.65"
        ], 
        "type": "Person"
      }
    ], 
    "datePublished": "2020-10-02", 
    "datePublishedReg": "2020-10-02", 
    "description": "Deploying off-the-shelf segmentation networks on biomedical data has become common practice, yet if structures of interest in an image sequence are visible only temporarily, existing frame-by-frame methods fail. In this paper, we provide a solution to segmentation of imperfect data through time based on temporal propagation and uncertainty estimation. We integrate uncertainty estimation into Mask R-CNN network and propagate motion-corrected segmentation masks from frames with low uncertainty to those frames with high uncertainty to handle temporary loss of signal for segmentation. We demonstrate the value of this approach over frame-by-frame segmentation and regular temporal propagation on data from human embryonic kidney (HEK293T) cells transiently transfected with a fluorescent protein that moves in and out of the nucleus over time. The method presented here will empower microscopic experiments aimed at understanding molecular and cellular function.", 
    "editor": [
      {
        "familyName": "Cardoso", 
        "givenName": "Jaime", 
        "type": "Person"
      }, 
      {
        "familyName": "Van Nguyen", 
        "givenName": "Hien", 
        "type": "Person"
      }, 
      {
        "familyName": "Heller", 
        "givenName": "Nicholas", 
        "type": "Person"
      }, 
      {
        "familyName": "Henriques Abreu", 
        "givenName": "Pedro", 
        "type": "Person"
      }, 
      {
        "familyName": "Isgum", 
        "givenName": "Ivana", 
        "type": "Person"
      }, 
      {
        "familyName": "Silva", 
        "givenName": "Wilson", 
        "type": "Person"
      }, 
      {
        "familyName": "Cruz", 
        "givenName": "Ricardo", 
        "type": "Person"
      }, 
      {
        "familyName": "Pereira Amorim", 
        "givenName": "Jose", 
        "type": "Person"
      }, 
      {
        "familyName": "Patel", 
        "givenName": "Vishal", 
        "type": "Person"
      }, 
      {
        "familyName": "Roysam", 
        "givenName": "Badri", 
        "type": "Person"
      }, 
      {
        "familyName": "Zhou", 
        "givenName": "Kevin", 
        "type": "Person"
      }, 
      {
        "familyName": "Jiang", 
        "givenName": "Steve", 
        "type": "Person"
      }, 
      {
        "familyName": "Le", 
        "givenName": "Ngan", 
        "type": "Person"
      }, 
      {
        "familyName": "Luu", 
        "givenName": "Khoa", 
        "type": "Person"
      }, 
      {
        "familyName": "Sznitman", 
        "givenName": "Raphael", 
        "type": "Person"
      }, 
      {
        "familyName": "Cheplygina", 
        "givenName": "Veronika", 
        "type": "Person"
      }, 
      {
        "familyName": "Mateus", 
        "givenName": "Diana", 
        "type": "Person"
      }, 
      {
        "familyName": "Trucco", 
        "givenName": "Emanuele", 
        "type": "Person"
      }, 
      {
        "familyName": "Abbasi", 
        "givenName": "Samaneh", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/978-3-030-61166-8_9", 
    "isAccessibleForFree": true, 
    "isPartOf": {
      "isbn": [
        "978-3-030-61165-1", 
        "978-3-030-61166-8"
      ], 
      "name": "Interpretable and Annotation-Efficient Learning for Medical Image Computing", 
      "type": "Book"
    }, 
    "keywords": [
      "Mask R-CNN network", 
      "R-CNN network", 
      "segmentation network", 
      "temporal propagation", 
      "uncertainty estimation", 
      "segmentation masks", 
      "frame segmentation", 
      "biomedical data", 
      "image sequences", 
      "cell segmentation", 
      "imperfect data", 
      "segmentation", 
      "structures of interest", 
      "network", 
      "frame", 
      "frame method", 
      "high uncertainty", 
      "estimation", 
      "common practice", 
      "data", 
      "uncertainty", 
      "method", 
      "mask", 
      "time", 
      "solution", 
      "experiments", 
      "low uncertainty", 
      "temporary loss", 
      "interest", 
      "signals", 
      "propagation", 
      "sequence", 
      "practice", 
      "function", 
      "structure", 
      "values", 
      "loss", 
      "presence", 
      "microscopic experiments", 
      "cellular functions", 
      "cells", 
      "paper", 
      "nucleus", 
      "approach", 
      "protein", 
      "fluorescent protein", 
      "human embryonic kidney cells", 
      "embryonic kidney cells", 
      "kidney cells"
    ], 
    "name": "Recovering the Imperfect: Cell Segmentation in the Presence of Dynamically Localized Proteins", 
    "pagination": "85-93", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1131419466"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/978-3-030-61166-8_9"
        ]
      }
    ], 
    "publisher": {
      "name": "Springer Nature", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/978-3-030-61166-8_9", 
      "https://app.dimensions.ai/details/publication/pub.1131419466"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2022-10-01T06:55", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20221001/entities/gbq_results/chapter/chapter_278.jsonl", 
    "type": "Chapter", 
    "url": "https://doi.org/10.1007/978-3-030-61166-8_9"
  }
]
 

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.10.1007/978-3-030-61166-8_9'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-030-61166-8_9'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-030-61166-8_9'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-030-61166-8_9'


 

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

232 TRIPLES      22 PREDICATES      73 URIs      66 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/978-3-030-61166-8_9 schema:about anzsrc-for:08
2 anzsrc-for:0801
3 schema:author Nb5049f3539784f0691b85949c094d2ae
4 schema:datePublished 2020-10-02
5 schema:datePublishedReg 2020-10-02
6 schema:description Deploying off-the-shelf segmentation networks on biomedical data has become common practice, yet if structures of interest in an image sequence are visible only temporarily, existing frame-by-frame methods fail. In this paper, we provide a solution to segmentation of imperfect data through time based on temporal propagation and uncertainty estimation. We integrate uncertainty estimation into Mask R-CNN network and propagate motion-corrected segmentation masks from frames with low uncertainty to those frames with high uncertainty to handle temporary loss of signal for segmentation. We demonstrate the value of this approach over frame-by-frame segmentation and regular temporal propagation on data from human embryonic kidney (HEK293T) cells transiently transfected with a fluorescent protein that moves in and out of the nucleus over time. The method presented here will empower microscopic experiments aimed at understanding molecular and cellular function.
7 schema:editor N1d2bba216a7a485b98c75090e7451455
8 schema:genre chapter
9 schema:isAccessibleForFree true
10 schema:isPartOf Nb6bf9a88d7214fca94a2def08d9ebd31
11 schema:keywords Mask R-CNN network
12 R-CNN network
13 approach
14 biomedical data
15 cell segmentation
16 cells
17 cellular functions
18 common practice
19 data
20 embryonic kidney cells
21 estimation
22 experiments
23 fluorescent protein
24 frame
25 frame method
26 frame segmentation
27 function
28 high uncertainty
29 human embryonic kidney cells
30 image sequences
31 imperfect data
32 interest
33 kidney cells
34 loss
35 low uncertainty
36 mask
37 method
38 microscopic experiments
39 network
40 nucleus
41 paper
42 practice
43 presence
44 propagation
45 protein
46 segmentation
47 segmentation masks
48 segmentation network
49 sequence
50 signals
51 solution
52 structure
53 structures of interest
54 temporal propagation
55 temporary loss
56 time
57 uncertainty
58 uncertainty estimation
59 values
60 schema:name Recovering the Imperfect: Cell Segmentation in the Presence of Dynamically Localized Proteins
61 schema:pagination 85-93
62 schema:productId N42966a721d384e33a1410ce1e63718fc
63 Nec19066677b6451fb9beb000d0e58082
64 schema:publisher N8f469d4764bb4d119e4ccc9bba7adcde
65 schema:sameAs https://app.dimensions.ai/details/publication/pub.1131419466
66 https://doi.org/10.1007/978-3-030-61166-8_9
67 schema:sdDatePublished 2022-10-01T06:55
68 schema:sdLicense https://scigraph.springernature.com/explorer/license/
69 schema:sdPublisher N10459272611b4f02b16d788a2bf631d3
70 schema:url https://doi.org/10.1007/978-3-030-61166-8_9
71 sgo:license sg:explorer/license/
72 sgo:sdDataset chapters
73 rdf:type schema:Chapter
74 N0081d075c3ff4a448d29007ef19ae644 rdf:first sg:person.014511262624.07
75 rdf:rest N44d57f469ebf41ba8c84abb7fbd42cb7
76 N01601d30554e4b0c9e3f7fe63f299e0c schema:familyName Cruz
77 schema:givenName Ricardo
78 rdf:type schema:Person
79 N077a7794cdfa451c991174c17f57e9bd rdf:first Nc04e2d129fb64563a293651da5cd66af
80 rdf:rest N889a465dffe745ad8f8d243747c04f6b
81 N10459272611b4f02b16d788a2bf631d3 schema:name Springer Nature - SN SciGraph project
82 rdf:type schema:Organization
83 N108683d0dcec48f0b7756776d1db9c45 rdf:first N14e20df73a854ac4bf78870039ac6b5a
84 rdf:rest Na7c5be31690d436db6a90b0ba3b45799
85 N14e20df73a854ac4bf78870039ac6b5a schema:familyName Pereira Amorim
86 schema:givenName Jose
87 rdf:type schema:Person
88 N17f4b8a20c504bf2959e2b0b103ebb92 schema:familyName Patel
89 schema:givenName Vishal
90 rdf:type schema:Person
91 N1cd34ad2c8514cce9f77321adc6b0315 rdf:first N33384978a06a403f9da34872716d5abe
92 rdf:rest N8877b972347846b0bc6584ece45dd6aa
93 N1d2bba216a7a485b98c75090e7451455 rdf:first Nf8cb9965ae804ed49b229102a60b07dd
94 rdf:rest Nd678d6f97ba445d38caf6c63bd5b8179
95 N1f94c34bd76647dab688fb7d051d6dbe schema:familyName Roysam
96 schema:givenName Badri
97 rdf:type schema:Person
98 N33384978a06a403f9da34872716d5abe schema:familyName Le
99 schema:givenName Ngan
100 rdf:type schema:Person
101 N42966a721d384e33a1410ce1e63718fc schema:name doi
102 schema:value 10.1007/978-3-030-61166-8_9
103 rdf:type schema:PropertyValue
104 N44d57f469ebf41ba8c84abb7fbd42cb7 rdf:first N57292c8fa1404f4586e523b2973273de
105 rdf:rest Nbd8665be891d446bba71f7ddb0bb19e6
106 N4b1fa201d5a64c059dffe920c506de0d schema:familyName Mateus
107 schema:givenName Diana
108 rdf:type schema:Person
109 N4bcc3866d2144babb0d5f23b68763866 rdf:first N5e4e709c327748b1aec7ce6bf58718d8
110 rdf:rest N64255a9614ea4b5b9362082c40190273
111 N4f6f64d8d41346928fdc90fda6ca3fe7 schema:familyName Van Nguyen
112 schema:givenName Hien
113 rdf:type schema:Person
114 N51d30477b9a34113a7e7f1d82102d948 schema:familyName Jiang
115 schema:givenName Steve
116 rdf:type schema:Person
117 N57292c8fa1404f4586e523b2973273de schema:affiliation grid-institutes:grid.7700.0
118 schema:familyName Boasiako Antwi
119 schema:givenName Enoch
120 rdf:type schema:Person
121 N5cf3eeabe06545baa4048f245675dc04 rdf:first Ndcd8303babe44a3ab89fab99b7874c01
122 rdf:rest N8b616b96b3244d2f9b321977d783b499
123 N5e4e709c327748b1aec7ce6bf58718d8 schema:familyName Henriques Abreu
124 schema:givenName Pedro
125 rdf:type schema:Person
126 N64255a9614ea4b5b9362082c40190273 rdf:first Nef6bc09ce40346869898c4b3cd677fb0
127 rdf:rest Ne1cca15452334fe59795747fbad3c3f1
128 N6acbf15de8fc4d2985c35f972dd3541c rdf:first N7b4b06892b5e41bda8efb953bfb01527
129 rdf:rest N4bcc3866d2144babb0d5f23b68763866
130 N703358f9a16b4f2c95d7ddeceed9542d rdf:first N01601d30554e4b0c9e3f7fe63f299e0c
131 rdf:rest N108683d0dcec48f0b7756776d1db9c45
132 N7b4b06892b5e41bda8efb953bfb01527 schema:familyName Heller
133 schema:givenName Nicholas
134 rdf:type schema:Person
135 N8877b972347846b0bc6584ece45dd6aa rdf:first Nffcfa33d1eed4e258785ac833c14ce26
136 rdf:rest Neb13b4c5607a46e38a411e1ebb767280
137 N889a465dffe745ad8f8d243747c04f6b rdf:first N51d30477b9a34113a7e7f1d82102d948
138 rdf:rest N1cd34ad2c8514cce9f77321adc6b0315
139 N8a62a7a6c5334c1283cc2a38e62ad427 rdf:first sg:person.012443225372.65
140 rdf:rest rdf:nil
141 N8b616b96b3244d2f9b321977d783b499 rdf:first N4b1fa201d5a64c059dffe920c506de0d
142 rdf:rest Nede7cb0ef48446e28c355f2eeacd5383
143 N8b7f698bb36a4d14a52bc854c35f8d4d rdf:first N961f17339d344d32ac2ed0e6c7e8a564
144 rdf:rest rdf:nil
145 N8f469d4764bb4d119e4ccc9bba7adcde schema:name Springer Nature
146 rdf:type schema:Organisation
147 N961f17339d344d32ac2ed0e6c7e8a564 schema:familyName Abbasi
148 schema:givenName Samaneh
149 rdf:type schema:Person
150 N9ac5b5d4bfa643b08eda02f4f7a8eb2f schema:familyName Trucco
151 schema:givenName Emanuele
152 rdf:type schema:Person
153 N9d794c5dceea47759675a342a22477f3 schema:familyName Sznitman
154 schema:givenName Raphael
155 rdf:type schema:Person
156 Na6a4ed7feba54e1088295e1dfdd835d2 rdf:first N1f94c34bd76647dab688fb7d051d6dbe
157 rdf:rest N077a7794cdfa451c991174c17f57e9bd
158 Na7c5be31690d436db6a90b0ba3b45799 rdf:first N17f4b8a20c504bf2959e2b0b103ebb92
159 rdf:rest Na6a4ed7feba54e1088295e1dfdd835d2
160 Nb5049f3539784f0691b85949c094d2ae rdf:first sg:person.016314276446.01
161 rdf:rest N0081d075c3ff4a448d29007ef19ae644
162 Nb6bf9a88d7214fca94a2def08d9ebd31 schema:isbn 978-3-030-61165-1
163 978-3-030-61166-8
164 schema:name Interpretable and Annotation-Efficient Learning for Medical Image Computing
165 rdf:type schema:Book
166 Nbd8665be891d446bba71f7ddb0bb19e6 rdf:first sg:person.01232522175.56
167 rdf:rest N8a62a7a6c5334c1283cc2a38e62ad427
168 Nc04e2d129fb64563a293651da5cd66af schema:familyName Zhou
169 schema:givenName Kevin
170 rdf:type schema:Person
171 Nc49896c3452d461c9cc9b9dded556aac schema:familyName Silva
172 schema:givenName Wilson
173 rdf:type schema:Person
174 Nd678d6f97ba445d38caf6c63bd5b8179 rdf:first N4f6f64d8d41346928fdc90fda6ca3fe7
175 rdf:rest N6acbf15de8fc4d2985c35f972dd3541c
176 Ndcd8303babe44a3ab89fab99b7874c01 schema:familyName Cheplygina
177 schema:givenName Veronika
178 rdf:type schema:Person
179 Ne1cca15452334fe59795747fbad3c3f1 rdf:first Nc49896c3452d461c9cc9b9dded556aac
180 rdf:rest N703358f9a16b4f2c95d7ddeceed9542d
181 Neb13b4c5607a46e38a411e1ebb767280 rdf:first N9d794c5dceea47759675a342a22477f3
182 rdf:rest N5cf3eeabe06545baa4048f245675dc04
183 Nec19066677b6451fb9beb000d0e58082 schema:name dimensions_id
184 schema:value pub.1131419466
185 rdf:type schema:PropertyValue
186 Nede7cb0ef48446e28c355f2eeacd5383 rdf:first N9ac5b5d4bfa643b08eda02f4f7a8eb2f
187 rdf:rest N8b7f698bb36a4d14a52bc854c35f8d4d
188 Nef6bc09ce40346869898c4b3cd677fb0 schema:familyName Isgum
189 schema:givenName Ivana
190 rdf:type schema:Person
191 Nf8cb9965ae804ed49b229102a60b07dd schema:familyName Cardoso
192 schema:givenName Jaime
193 rdf:type schema:Person
194 Nffcfa33d1eed4e258785ac833c14ce26 schema:familyName Luu
195 schema:givenName Khoa
196 rdf:type schema:Person
197 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
198 schema:name Information and Computing Sciences
199 rdf:type schema:DefinedTerm
200 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
201 schema:name Artificial Intelligence and Image Processing
202 rdf:type schema:DefinedTerm
203 sg:person.01232522175.56 schema:affiliation grid-institutes:grid.5963.9
204 schema:familyName Di Ventura
205 schema:givenName Barbara
206 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01232522175.56
207 rdf:type schema:Person
208 sg:person.012443225372.65 schema:affiliation grid-institutes:grid.5963.9
209 schema:familyName Brox
210 schema:givenName Thomas
211 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012443225372.65
212 rdf:type schema:Person
213 sg:person.014511262624.07 schema:affiliation grid-institutes:grid.5963.9
214 schema:familyName Marrakchi
215 schema:givenName Yassine
216 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014511262624.07
217 rdf:type schema:Person
218 sg:person.016314276446.01 schema:affiliation grid-institutes:grid.5963.9
219 schema:familyName Çiçek
220 schema:givenName Özgün
221 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016314276446.01
222 rdf:type schema:Person
223 grid-institutes:grid.5963.9 schema:alternateName Signalling Research Centres BIOSS and CIBSS, Freiburg, Germany
224 University of Freiburg, Freiburg, Germany
225 schema:name Signalling Research Centres BIOSS and CIBSS, Freiburg, Germany
226 University of Freiburg, Freiburg, Germany
227 rdf:type schema:Organization
228 grid-institutes:grid.7700.0 schema:alternateName Heidelberg Biosciences International Graduate School (HBIGS), Heidelberg, Germany
229 schema:name Heidelberg Biosciences International Graduate School (HBIGS), Heidelberg, Germany
230 Signalling Research Centres BIOSS and CIBSS, Freiburg, Germany
231 University of Freiburg, Freiburg, Germany
232 rdf:type schema:Organization
 




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


...