Cell Identity Codes: Understanding Cell Identity from Gene Expression Profiles using Deep Neural Networks View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2019-12

AUTHORS

Farzad Abdolhosseini, Behrooz Azarkhalili, Abbas Maazallahi, Aryan Kamal, Seyed Abolfazl Motahari, Ali Sharifi-Zarchi, Hamidreza Chitsaz

ABSTRACT

Understanding cell identity is an important task in many biomedical areas. Expression patterns of specific marker genes have been used to characterize some limited cell types, but exclusive markers are not available for many cell types. A second approach is to use machine learning to discriminate cell types based on the whole gene expression profiles (GEPs). The accuracies of simple classification algorithms such as linear discriminators or support vector machines are limited due to the complexity of biological systems. We used deep neural networks to analyze 1040 GEPs from 16 different human tissues and cell types. After comparing different architectures, we identified a specific structure of deep autoencoders that can encode a GEP into a vector of 30 numeric values, which we call the cell identity code (CIC). The original GEP can be reproduced from the CIC with an accuracy comparable to technical replicates of the same experiment. Although we use an unsupervised approach to train the autoencoder, we show different values of the CIC are connected to different biological aspects of the cell, such as different pathways or biological processes. This network can use CIC to reproduce the GEP of the cell types it has never seen during the training. It also can resist some noise in the measurement of the GEP. Furthermore, we introduce classifier autoencoder, an architecture that can accurately identify cell type based on the GEP or the CIC. More... »

PAGES

2342

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/s41598-019-38798-y

DOI

http://dx.doi.org/10.1038/s41598-019-38798-y

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1112225914

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30787315


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": "Sharif University of Technology", 
          "id": "https://www.grid.ac/institutes/grid.412553.4", 
          "name": [
            "Department of Computer Engineering, Sharif University of Technology, Tehran, Iran"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Abdolhosseini", 
        "givenName": "Farzad", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Royan Institute", 
          "id": "https://www.grid.ac/institutes/grid.419336.a", 
          "name": [
            "Royan Institute for Stem Cell Biology and Technology, ACECR, Tehran, Iran"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Azarkhalili", 
        "givenName": "Behrooz", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Sharif University of Technology", 
          "id": "https://www.grid.ac/institutes/grid.412553.4", 
          "name": [
            "Department of Computer Engineering, Sharif University of Technology, Tehran, Iran"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Maazallahi", 
        "givenName": "Abbas", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Sharif University of Technology", 
          "id": "https://www.grid.ac/institutes/grid.412553.4", 
          "name": [
            "Department of Computer Engineering, Sharif University of Technology, Tehran, Iran"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kamal", 
        "givenName": "Aryan", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Sharif University of Technology", 
          "id": "https://www.grid.ac/institutes/grid.412553.4", 
          "name": [
            "Department of Computer Engineering, Sharif University of Technology, Tehran, Iran"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Motahari", 
        "givenName": "Seyed Abolfazl", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Sharif University of Technology", 
          "id": "https://www.grid.ac/institutes/grid.412553.4", 
          "name": [
            "Department of Computer Engineering, Sharif University of Technology, Tehran, Iran"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Sharifi-Zarchi", 
        "givenName": "Ali", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Colorado State University", 
          "id": "https://www.grid.ac/institutes/grid.47894.36", 
          "name": [
            "Department of Computer Science, Colorado State University, Fort Collins, CO, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Chitsaz", 
        "givenName": "Hamidreza", 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1515/bc.2008.098", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000674398"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/btv643", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003390673"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1126/science.1127647", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004607132"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/srep17573", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004798155", 
          "https://doi.org/10.1038/srep17573"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.cell.2014.07.020", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005495413"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.ymeth.2016.06.001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1007575035"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.ymeth.2016.06.001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1007575035"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/srep11476", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010621663", 
          "https://doi.org/10.1038/srep11476"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1371/journal.pcbi.1003189", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1012400529"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/nar/gkv007", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016098431"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1073/pnas.2536479100", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017393030"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1142/9789814644730_0014", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018424047"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.cell.2008.02.008", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020546692"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bmb/ldr027", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020997508"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/nar/gkw226", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025519948"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.eswa.2010.06.065", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025831182"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/s12859-015-0852-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026957001", 
          "https://doi.org/10.1186/s12859-015-0852-1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1177/0278364914549607", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1029816433"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1177/0278364914549607", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1029816433"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nature14236", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030517994", 
          "https://doi.org/10.1038/nature14236"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/1390156.1390294", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034603392"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/nar/gks1193", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035551539"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1073/pnas.0506580102", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037705714"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1073/pnas.0506580102", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037705714"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1073/pnas.082099299", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037994416"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nature16961", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039427823", 
          "https://doi.org/10.1038/nature16961"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/nar/gkq418", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041947444"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/355457a0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044143627", 
          "https://doi.org/10.1038/355457a0"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2105-14-128", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044758756", 
          "https://doi.org/10.1186/1471-2105-14-128"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nbt.3300", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045313781", 
          "https://doi.org/10.1038/nbt.3300"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/btw074", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048356689"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s12015-010-9113-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052067408", 
          "https://doi.org/10.1007/s12015-010-9113-x"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s12015-010-9113-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052067408", 
          "https://doi.org/10.1007/s12015-010-9113-x"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/2505515.2505665", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052509117"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1128/msystems.00025-15", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052933466"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nsmb.2510", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053107109", 
          "https://doi.org/10.1038/nsmb.2510"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-319-10377-8_1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053663136", 
          "https://doi.org/10.1007/978-3-319-10377-8_1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1198/016214501753382129", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064197905"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/cvpr.2014.244", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1093406535"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/icassp.2013.6639344", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1094292119"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2019-12", 
    "datePublishedReg": "2019-12-01", 
    "description": "Understanding cell identity is an important task in many biomedical areas. Expression patterns of specific marker genes have been used to characterize some limited cell types, but exclusive markers are not available for many cell types. A second approach is to use machine learning to discriminate cell types based on the whole gene expression profiles (GEPs). The accuracies of simple classification algorithms such as linear discriminators or support vector machines are limited due to the complexity of biological systems. We used deep neural networks to analyze 1040 GEPs from 16 different human tissues and cell types. After comparing different architectures, we identified a specific structure of deep autoencoders that can encode a GEP into a vector of 30 numeric values, which we call the cell identity code (CIC). The original GEP can be reproduced from the CIC with an accuracy comparable to technical replicates of the same experiment. Although we use an unsupervised approach to train the autoencoder, we show different values of the CIC are connected to different biological aspects of the cell, such as different pathways or biological processes. This network can use CIC to reproduce the GEP of the cell types it has never seen during the training. It also can resist some noise in the measurement of the GEP. Furthermore, we introduce classifier autoencoder, an architecture that can accurately identify cell type based on the GEP or the CIC.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1038/s41598-019-38798-y", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isPartOf": [
      {
        "id": "sg:journal.1045337", 
        "issn": [
          "2045-2322"
        ], 
        "name": "Scientific Reports", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "9"
      }
    ], 
    "name": "Cell Identity Codes: Understanding Cell Identity from Gene Expression Profiles using Deep Neural Networks", 
    "pagination": "2342", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "7750a010c65e3e60dbaf25adfc560bef51b66e1feb737e5b64ed05951e70a2cf"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "30787315"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "101563288"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1038/s41598-019-38798-y"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1112225914"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1038/s41598-019-38798-y", 
      "https://app.dimensions.ai/details/publication/pub.1112225914"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T10:29", 
    "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/v90/0000000349_0000000349/records_113641_00000005.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://www.nature.com/articles/s41598-019-38798-y"
  }
]
 

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.10.1038/s41598-019-38798-y'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1038/s41598-019-38798-y'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1038/s41598-019-38798-y'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1038/s41598-019-38798-y'


 

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

228 TRIPLES      21 PREDICATES      65 URIs      21 LITERALS      9 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1038/s41598-019-38798-y schema:about anzsrc-for:08
2 anzsrc-for:0801
3 schema:author N7a23701e41bb402ab2060a3796c38245
4 schema:citation sg:pub.10.1007/978-3-319-10377-8_1
5 sg:pub.10.1007/s12015-010-9113-x
6 sg:pub.10.1038/355457a0
7 sg:pub.10.1038/nature14236
8 sg:pub.10.1038/nature16961
9 sg:pub.10.1038/nbt.3300
10 sg:pub.10.1038/nsmb.2510
11 sg:pub.10.1038/srep11476
12 sg:pub.10.1038/srep17573
13 sg:pub.10.1186/1471-2105-14-128
14 sg:pub.10.1186/s12859-015-0852-1
15 https://doi.org/10.1016/j.cell.2008.02.008
16 https://doi.org/10.1016/j.cell.2014.07.020
17 https://doi.org/10.1016/j.eswa.2010.06.065
18 https://doi.org/10.1016/j.ymeth.2016.06.001
19 https://doi.org/10.1073/pnas.0506580102
20 https://doi.org/10.1073/pnas.082099299
21 https://doi.org/10.1073/pnas.2536479100
22 https://doi.org/10.1093/bioinformatics/btv643
23 https://doi.org/10.1093/bioinformatics/btw074
24 https://doi.org/10.1093/bmb/ldr027
25 https://doi.org/10.1093/nar/gkq418
26 https://doi.org/10.1093/nar/gks1193
27 https://doi.org/10.1093/nar/gkv007
28 https://doi.org/10.1093/nar/gkw226
29 https://doi.org/10.1109/cvpr.2014.244
30 https://doi.org/10.1109/icassp.2013.6639344
31 https://doi.org/10.1126/science.1127647
32 https://doi.org/10.1128/msystems.00025-15
33 https://doi.org/10.1142/9789814644730_0014
34 https://doi.org/10.1145/1390156.1390294
35 https://doi.org/10.1145/2505515.2505665
36 https://doi.org/10.1177/0278364914549607
37 https://doi.org/10.1198/016214501753382129
38 https://doi.org/10.1371/journal.pcbi.1003189
39 https://doi.org/10.1515/bc.2008.098
40 schema:datePublished 2019-12
41 schema:datePublishedReg 2019-12-01
42 schema:description Understanding cell identity is an important task in many biomedical areas. Expression patterns of specific marker genes have been used to characterize some limited cell types, but exclusive markers are not available for many cell types. A second approach is to use machine learning to discriminate cell types based on the whole gene expression profiles (GEPs). The accuracies of simple classification algorithms such as linear discriminators or support vector machines are limited due to the complexity of biological systems. We used deep neural networks to analyze 1040 GEPs from 16 different human tissues and cell types. After comparing different architectures, we identified a specific structure of deep autoencoders that can encode a GEP into a vector of 30 numeric values, which we call the cell identity code (CIC). The original GEP can be reproduced from the CIC with an accuracy comparable to technical replicates of the same experiment. Although we use an unsupervised approach to train the autoencoder, we show different values of the CIC are connected to different biological aspects of the cell, such as different pathways or biological processes. This network can use CIC to reproduce the GEP of the cell types it has never seen during the training. It also can resist some noise in the measurement of the GEP. Furthermore, we introduce classifier autoencoder, an architecture that can accurately identify cell type based on the GEP or the CIC.
43 schema:genre research_article
44 schema:inLanguage en
45 schema:isAccessibleForFree true
46 schema:isPartOf N484b6dedb084455cb8ffcc43ad03e46e
47 N75432c2dfecc4ab8b5664471b18b3942
48 sg:journal.1045337
49 schema:name Cell Identity Codes: Understanding Cell Identity from Gene Expression Profiles using Deep Neural Networks
50 schema:pagination 2342
51 schema:productId N008e63b542dd4cc1912a8657109ecb88
52 N0df6cc9b415f4f8990b9f4426ffd56d8
53 N258c1f27316148669e58d197cc7323aa
54 N8cd861bc8eba417dad93fd2fc20512e0
55 Na4f2690682b6495294c19c809ddc738c
56 schema:sameAs https://app.dimensions.ai/details/publication/pub.1112225914
57 https://doi.org/10.1038/s41598-019-38798-y
58 schema:sdDatePublished 2019-04-11T10:29
59 schema:sdLicense https://scigraph.springernature.com/explorer/license/
60 schema:sdPublisher N8f245edecaa34fd8b26318245720f996
61 schema:url https://www.nature.com/articles/s41598-019-38798-y
62 sgo:license sg:explorer/license/
63 sgo:sdDataset articles
64 rdf:type schema:ScholarlyArticle
65 N008e63b542dd4cc1912a8657109ecb88 schema:name pubmed_id
66 schema:value 30787315
67 rdf:type schema:PropertyValue
68 N0df6cc9b415f4f8990b9f4426ffd56d8 schema:name doi
69 schema:value 10.1038/s41598-019-38798-y
70 rdf:type schema:PropertyValue
71 N1b4b91cc576443899d24d06d7392e269 schema:affiliation https://www.grid.ac/institutes/grid.412553.4
72 schema:familyName Abdolhosseini
73 schema:givenName Farzad
74 rdf:type schema:Person
75 N258c1f27316148669e58d197cc7323aa schema:name dimensions_id
76 schema:value pub.1112225914
77 rdf:type schema:PropertyValue
78 N35251e32b096479390603a4cd5a11bae schema:affiliation https://www.grid.ac/institutes/grid.412553.4
79 schema:familyName Sharifi-Zarchi
80 schema:givenName Ali
81 rdf:type schema:Person
82 N3a98cd9f8b5f4fdb8691950dc29ddaae schema:affiliation https://www.grid.ac/institutes/grid.419336.a
83 schema:familyName Azarkhalili
84 schema:givenName Behrooz
85 rdf:type schema:Person
86 N475aa1efbe9846bab7c0ccbc12217633 rdf:first N35251e32b096479390603a4cd5a11bae
87 rdf:rest N55a0e991df3e4046929a8e1c8bd16508
88 N484b6dedb084455cb8ffcc43ad03e46e schema:volumeNumber 9
89 rdf:type schema:PublicationVolume
90 N55a0e991df3e4046929a8e1c8bd16508 rdf:first N96d2e802e3804823b6f74aa767c18630
91 rdf:rest rdf:nil
92 N6b0c11da33e04160becb5012af92f70c schema:affiliation https://www.grid.ac/institutes/grid.412553.4
93 schema:familyName Maazallahi
94 schema:givenName Abbas
95 rdf:type schema:Person
96 N6b4ea2facd844719a83a3615af255ee0 rdf:first Na0a44918d6fa44f6b1b5fab15f2dc9f1
97 rdf:rest N475aa1efbe9846bab7c0ccbc12217633
98 N75225f2f0b434c55bdd815d3f9b98621 rdf:first Nbd6ef596b9554749a691f9522215514b
99 rdf:rest N6b4ea2facd844719a83a3615af255ee0
100 N75432c2dfecc4ab8b5664471b18b3942 schema:issueNumber 1
101 rdf:type schema:PublicationIssue
102 N7a23701e41bb402ab2060a3796c38245 rdf:first N1b4b91cc576443899d24d06d7392e269
103 rdf:rest N8bbe1be759ab494d90db063b61214534
104 N8bbe1be759ab494d90db063b61214534 rdf:first N3a98cd9f8b5f4fdb8691950dc29ddaae
105 rdf:rest Necd392c221e040d49bc97b9d5a813412
106 N8cd861bc8eba417dad93fd2fc20512e0 schema:name nlm_unique_id
107 schema:value 101563288
108 rdf:type schema:PropertyValue
109 N8f245edecaa34fd8b26318245720f996 schema:name Springer Nature - SN SciGraph project
110 rdf:type schema:Organization
111 N96d2e802e3804823b6f74aa767c18630 schema:affiliation https://www.grid.ac/institutes/grid.47894.36
112 schema:familyName Chitsaz
113 schema:givenName Hamidreza
114 rdf:type schema:Person
115 Na0a44918d6fa44f6b1b5fab15f2dc9f1 schema:affiliation https://www.grid.ac/institutes/grid.412553.4
116 schema:familyName Motahari
117 schema:givenName Seyed Abolfazl
118 rdf:type schema:Person
119 Na4f2690682b6495294c19c809ddc738c schema:name readcube_id
120 schema:value 7750a010c65e3e60dbaf25adfc560bef51b66e1feb737e5b64ed05951e70a2cf
121 rdf:type schema:PropertyValue
122 Nbd6ef596b9554749a691f9522215514b schema:affiliation https://www.grid.ac/institutes/grid.412553.4
123 schema:familyName Kamal
124 schema:givenName Aryan
125 rdf:type schema:Person
126 Necd392c221e040d49bc97b9d5a813412 rdf:first N6b0c11da33e04160becb5012af92f70c
127 rdf:rest N75225f2f0b434c55bdd815d3f9b98621
128 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
129 schema:name Information and Computing Sciences
130 rdf:type schema:DefinedTerm
131 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
132 schema:name Artificial Intelligence and Image Processing
133 rdf:type schema:DefinedTerm
134 sg:journal.1045337 schema:issn 2045-2322
135 schema:name Scientific Reports
136 rdf:type schema:Periodical
137 sg:pub.10.1007/978-3-319-10377-8_1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053663136
138 https://doi.org/10.1007/978-3-319-10377-8_1
139 rdf:type schema:CreativeWork
140 sg:pub.10.1007/s12015-010-9113-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1052067408
141 https://doi.org/10.1007/s12015-010-9113-x
142 rdf:type schema:CreativeWork
143 sg:pub.10.1038/355457a0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044143627
144 https://doi.org/10.1038/355457a0
145 rdf:type schema:CreativeWork
146 sg:pub.10.1038/nature14236 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030517994
147 https://doi.org/10.1038/nature14236
148 rdf:type schema:CreativeWork
149 sg:pub.10.1038/nature16961 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039427823
150 https://doi.org/10.1038/nature16961
151 rdf:type schema:CreativeWork
152 sg:pub.10.1038/nbt.3300 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045313781
153 https://doi.org/10.1038/nbt.3300
154 rdf:type schema:CreativeWork
155 sg:pub.10.1038/nsmb.2510 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053107109
156 https://doi.org/10.1038/nsmb.2510
157 rdf:type schema:CreativeWork
158 sg:pub.10.1038/srep11476 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010621663
159 https://doi.org/10.1038/srep11476
160 rdf:type schema:CreativeWork
161 sg:pub.10.1038/srep17573 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004798155
162 https://doi.org/10.1038/srep17573
163 rdf:type schema:CreativeWork
164 sg:pub.10.1186/1471-2105-14-128 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044758756
165 https://doi.org/10.1186/1471-2105-14-128
166 rdf:type schema:CreativeWork
167 sg:pub.10.1186/s12859-015-0852-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026957001
168 https://doi.org/10.1186/s12859-015-0852-1
169 rdf:type schema:CreativeWork
170 https://doi.org/10.1016/j.cell.2008.02.008 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020546692
171 rdf:type schema:CreativeWork
172 https://doi.org/10.1016/j.cell.2014.07.020 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005495413
173 rdf:type schema:CreativeWork
174 https://doi.org/10.1016/j.eswa.2010.06.065 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025831182
175 rdf:type schema:CreativeWork
176 https://doi.org/10.1016/j.ymeth.2016.06.001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007575035
177 rdf:type schema:CreativeWork
178 https://doi.org/10.1073/pnas.0506580102 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037705714
179 rdf:type schema:CreativeWork
180 https://doi.org/10.1073/pnas.082099299 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037994416
181 rdf:type schema:CreativeWork
182 https://doi.org/10.1073/pnas.2536479100 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017393030
183 rdf:type schema:CreativeWork
184 https://doi.org/10.1093/bioinformatics/btv643 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003390673
185 rdf:type schema:CreativeWork
186 https://doi.org/10.1093/bioinformatics/btw074 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048356689
187 rdf:type schema:CreativeWork
188 https://doi.org/10.1093/bmb/ldr027 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020997508
189 rdf:type schema:CreativeWork
190 https://doi.org/10.1093/nar/gkq418 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041947444
191 rdf:type schema:CreativeWork
192 https://doi.org/10.1093/nar/gks1193 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035551539
193 rdf:type schema:CreativeWork
194 https://doi.org/10.1093/nar/gkv007 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016098431
195 rdf:type schema:CreativeWork
196 https://doi.org/10.1093/nar/gkw226 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025519948
197 rdf:type schema:CreativeWork
198 https://doi.org/10.1109/cvpr.2014.244 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093406535
199 rdf:type schema:CreativeWork
200 https://doi.org/10.1109/icassp.2013.6639344 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094292119
201 rdf:type schema:CreativeWork
202 https://doi.org/10.1126/science.1127647 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004607132
203 rdf:type schema:CreativeWork
204 https://doi.org/10.1128/msystems.00025-15 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052933466
205 rdf:type schema:CreativeWork
206 https://doi.org/10.1142/9789814644730_0014 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018424047
207 rdf:type schema:CreativeWork
208 https://doi.org/10.1145/1390156.1390294 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034603392
209 rdf:type schema:CreativeWork
210 https://doi.org/10.1145/2505515.2505665 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052509117
211 rdf:type schema:CreativeWork
212 https://doi.org/10.1177/0278364914549607 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029816433
213 rdf:type schema:CreativeWork
214 https://doi.org/10.1198/016214501753382129 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064197905
215 rdf:type schema:CreativeWork
216 https://doi.org/10.1371/journal.pcbi.1003189 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012400529
217 rdf:type schema:CreativeWork
218 https://doi.org/10.1515/bc.2008.098 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000674398
219 rdf:type schema:CreativeWork
220 https://www.grid.ac/institutes/grid.412553.4 schema:alternateName Sharif University of Technology
221 schema:name Department of Computer Engineering, Sharif University of Technology, Tehran, Iran
222 rdf:type schema:Organization
223 https://www.grid.ac/institutes/grid.419336.a schema:alternateName Royan Institute
224 schema:name Royan Institute for Stem Cell Biology and Technology, ACECR, Tehran, Iran
225 rdf:type schema:Organization
226 https://www.grid.ac/institutes/grid.47894.36 schema:alternateName Colorado State University
227 schema:name Department of Computer Science, Colorado State University, Fort Collins, CO, USA
228 rdf:type schema:Organization
 




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


...