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pmid:37779906

Machine learning and bioinformatics analysis to identify autophagy-related biomarkers in peripheral blood for rheumatoid arthritis.

Guoqi Dong · Hui Gao · Yingqi Chen · Huayuan Yang

Frontiers in genetics · 2023 doi:10.3389/fgene.2023.1238407

Paper

complete

Experiment

complete

Data

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Analysis

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2026-11-01T19:02:37Z
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title
Machine learning and bioinformatics analysis to identify autophagy-related biomarkers in peripheral blood for rheumatoid arthritis.
doi
10.3389/fgene.2023.1238407
pmid
37779906
authors array · 4 items
[0]
Guoqi Dong
[1]
Hui Gao
[2]
Yingqi Chen
[3]
Huayuan Yang
journal
Frontiers in genetics
year
2023
abstract
Background: Although rheumatoid arthritis (RA) is a common autoimmune disease, the precise pathogenesis of the disease remains unclear. Recent research has unraveled the role of autophagy in the development of RA. This research aims to explore autophagy-related diagnostic biomarkers in the peripheral blood of RA patients. Methods: The gene expression profiles of GSE17755 were retrieved from the gene expression ontology (GEO) database. Differentially expressed autophagy-related genes (DE-ARGs) were identified for the subsequent research by inserting autophagy-related genes and differentially expressed genes (DEGs). Three machine learning algorithms, including random forest, support vector machine recursive feature elimination (SVM-RFE), and least absolute shrinkage and selection operator (LASSO), were employed to identify diagnostic biomarkers. A nomogram model was constructed to assess the diagnostic value of the biomarkers. The CIBERSORT algorithm was performed to investigate the correlation of the diagnostic biomarkers with immune cells and immune factors. Finally, the diagnostic efficacy and differential expression trend of diagnostic biomarkers were validated in multiple cohorts containing different tissues and diseases. Results: In this study, 25 DE-ARGs were identified between RA and healthy individuals. In addition to "macroautophagy" and "autophagy-animal," DE-ARGs were also associated with several types of programmed cell death and immune-related pathways according to GO and KEGG analysis. Three diagnostic biomarkers, EEF2, HSP90AB1 and TNFSF10, were identified by the random forest, SVM-RFE, and LASSO. The nomogram model demonstrated excellent diagnostic value in GSE17755 (AUC = 0.995, 95% CI: 0.988-0.999). Furthermore, immune infiltration analysis showed a remarkable association between EEF2, HSP90AB1, and TNFSF10 expression with various immune cells and immune factors. The three diagnostic biomarkers also exhibited good diagnostic efficacy and demonstrated the same trend of differential expression in multiple validation cohorts. Conclusion: This study identified autophagy-related diagnostic biomarkers based on three machine learning algorithms, providing promising targets for the diagnosis and treatment of RA.
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Differential expression analysis was performed by the “limma” package (version 3.56.1) with the following threshold: p_adj < 0.05 and abs (logFC) > 0.45.
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sections[4] "2.2 Identification of differentially expressed autophagy-related genes (DE-ARGs)" paragraph 2
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Differential expression analysis was performed by the “limma” package (version 3.56.1) with the following threshold: p_adj < 0.05 and abs (logFC) > 0.45.
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The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.
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The nomogram model demonstrated excellent diagnostic value in GSE17755 (AUC = 0.995, 95% CI: 0.988–0.999).
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The nomogram model demonstrated excellent diagnostic value in GSE17755 (AUC = 0.995, 95% CI: 0.988–0.999).
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The nomogram model demonstrated excellent diagnostic value in GSE17755 (AUC = 0.995, 95% CI: 0.988–0.999).
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Transcriptional Signature Associated with Early Rheumatoid Arthritis and Healthy Individuals at high risk to develop the disease.
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The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.
access_statement_kind
data_availability_statement
sample_accessions array · 22 items
[0]
GSM966252
[1]
GSM966253
[2]
GSM966254
[3]
GSM966255
[4]
GSM966256
[5]
GSM966257
[6]
GSM966258
[7]
GSM966259
[8]
GSM966260
[9]
GSM966261
[10]
GSM966262
[11]
GSM966263
[12]
GSM966264
[13]
GSM966265
[14]
GSM966266
[15]
GSM966267
[16]
GSM966268
[17]
GSM966269
[18]
GSM966270
[19]
GSM966271
[20]
GSM966272
[21]
GSM966273
sample_count
22
group_sizes
null
group_sizes_basis
null
platform
10558
taxon
Homo sapiens
title
Indentification of special gene expressions in synovial tissues of rheumatoid arthritis
resolution_checked_at
2026-11-01T19:09:46Z
resolution_check
identifier_resolution_only_no_download_no_licence_read
downloaded
false
licence_read
false
[6] object · 15 fields
id
data:geo:GSE55235
role
raw
format
geo-series
media_type
application/vnd.ncbi.geo-series+json
uri
https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE55235
sha256
null
bytes
null
access
open
state
resolved
reader
null
reader_status
reader_required
identifier
GSE55235
version
null
note
The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.
structure object · 15 fields
repository
GEO
access_route
GEO accession URL
access_statement_verbatim
The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.
access_statement_kind
data_availability_statement
sample_accessions array · 30 items
[0]
GSM1332201
[1]
GSM1332202
[2]
GSM1332203
[3]
GSM1332204
[4]
GSM1332205
[5]
GSM1332206
[6]
GSM1332207
[7]
GSM1332208
[8]
GSM1332209
[9]
GSM1332210
[10]
GSM1332211
[11]
GSM1332212
[12]
GSM1332213
[13]
GSM1332214
[14]
GSM1332215
[15]
GSM1332216
[16]
GSM1332217
[17]
GSM1332218
[18]
GSM1332219
[19]
GSM1332220
[20]
GSM1332221
[21]
GSM1332222
[22]
GSM1332223
[23]
GSM1332224
[24]
GSM1332225
[25]
GSM1332226
[26]
GSM1332227
[27]
GSM1332228
[28]
GSM1332229
[29]
GSM1332230
sample_count
30
group_sizes
null
group_sizes_basis
null
platform
96
taxon
Homo sapiens
title
Identification of rheumatoid arthritis and osteoarthritis patients by transcriptome-based rule set generation
resolution_checked_at
2026-11-01T15:39:54Z
resolution_check
identifier_resolution_only_no_download_no_licence_read
downloaded
false
licence_read
false
[7] object · 15 fields
id
data:geo:GSE89408
role
raw
format
geo-series
media_type
application/vnd.ncbi.geo-series+json
uri
https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE89408
sha256
null
bytes
null
access
open
state
resolved
reader
null
reader_status
reader_required
identifier
GSE89408
version
null
note
The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.
structure object · 15 fields
repository
GEO
access_route
GEO accession URL
access_statement_verbatim
The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.
access_statement_kind
data_availability_statement
sample_accessions array · 218 items
[0]
GSM2370970
[1]
GSM2370971
[2]
GSM2370972
[3]
GSM2370973
[4]
GSM2370974
[5]
GSM2370975
[6]
GSM2370976
[7]
GSM2370977
[8]
GSM2370978
[9]
GSM2370979
[10]
GSM2370980
[11]
GSM2370981
[12]
GSM2370982
[13]
GSM2370983
[14]
GSM2370984
[15]
GSM2370985
[16]
GSM2370986
[17]
GSM2370987
[18]
GSM2370988
[19]
GSM2370989
[20]
GSM2370990
[21]
GSM2370991
[22]
GSM2370992
[23]
GSM2370993
[24]
GSM2370994
[25]
GSM2370995
[26]
GSM2370996
[27]
GSM2370997
[28]
GSM2370998
[29]
GSM2370999
[30]
GSM2371000
[31]
GSM2371001
[32]
GSM2371002
[33]
GSM2371003
[34]
GSM2371004
[35]
GSM2371005
[36]
GSM2371006
[37]
GSM2371007
[38]
GSM2371008
[39]
GSM2371009
[40]
GSM2371010
[41]
GSM2371011
[42]
GSM2371012
[43]
GSM2371013
[44]
GSM2371014
[45]
GSM2371015
[46]
GSM2371016
[47]
GSM2371017
[48]
GSM2371018
[49]
GSM2371019
[50]
GSM2371020
[51]
GSM2371021
[52]
GSM2371022
[53]
GSM2371023
[54]
GSM2371024
[55]
GSM2371025
[56]
GSM2371026
[57]
GSM2371027
[58]
GSM2371028
[59]
GSM2371029
[60]
GSM2371030
[61]
GSM2371031
[62]
GSM2371032
[63]
GSM2371033
[64]
GSM2371034
[65]
GSM2371035
[66]
GSM2371036
[67]
GSM2371037
[68]
GSM2371038
[69]
GSM2371039
[70]
GSM2371040
[71]
GSM2371041
[72]
GSM2371042
[73]
GSM2371043
[74]
GSM2371044
[75]
GSM2371045
[76]
GSM2371046
[77]
GSM2371047
[78]
GSM2371048
[79]
GSM2371049
[80]
GSM2371050
[81]
GSM2371051
[82]
GSM2371052
[83]
GSM2371053
[84]
GSM2371054
[85]
GSM2371055
[86]
GSM2371056
[87]
GSM2371057
[88]
GSM2371058
[89]
GSM2371059
[90]
GSM2371060
[91]
GSM2371061
[92]
GSM2371062
[93]
GSM2371063
[94]
GSM2371064
[95]
GSM2371065
[96]
GSM2371066
[97]
GSM2371067
[98]
GSM2371068
[99]
GSM2371069
[100]
GSM2371070
[101]
GSM2371071
[102]
GSM2371072
[103]
GSM2371073
[104]
GSM2371074
[105]
GSM2371075
[106]
GSM2371076
[107]
GSM2371077
[108]
GSM2371078
[109]
GSM2371079
[110]
GSM2371080
[111]
GSM2371081
[112]
GSM2371082
[113]
GSM2371083
[114]
GSM2371084
[115]
GSM2371085
[116]
GSM2371086
[117]
GSM2371087
[118]
GSM2371088
[119]
GSM2371089
[120]
GSM2371090
[121]
GSM2371091
[122]
GSM2371092
[123]
GSM2371093
[124]
GSM2371094
[125]
GSM2371095
[126]
GSM2371096
[127]
GSM2371097
[128]
GSM2371098
[129]
GSM2371099
[130]
GSM2371100
[131]
GSM2371101
[132]
GSM2371102
[133]
GSM2371103
[134]
GSM2371104
[135]
GSM2371105
[136]
GSM2371106
[137]
GSM2371107
[138]
GSM2371108
[139]
GSM2371109
[140]
GSM2371110
[141]
GSM2371111
[142]
GSM2371112
[143]
GSM2371113
[144]
GSM2371114
[145]
GSM2371115
[146]
GSM2371116
[147]
GSM2371117
[148]
GSM2371118
[149]
GSM2371119
[150]
GSM2371120
[151]
GSM2371121
[152]
GSM2371122
[153]
GSM2371123
[154]
GSM2371124
[155]
GSM2371125
[156]
GSM2371126
[157]
GSM2371127
[158]
GSM2371128
[159]
GSM2371129
[160]
GSM2371130
[161]
GSM2371131
[162]
GSM2371132
[163]
GSM2371133
[164]
GSM2371134
[165]
GSM2371135
[166]
GSM2371136
[167]
GSM2371137
[168]
GSM2371138
[169]
GSM2371139
[170]
GSM2371140
[171]
GSM2371141
[172]
GSM2371142
[173]
GSM2371143
[174]
GSM2371144
[175]
GSM2371145
[176]
GSM2371146
[177]
GSM2371147
[178]
GSM2371148
[179]
GSM2371149
[180]
GSM2371150
[181]
GSM2371151
[182]
GSM2371152
[183]
GSM2371153
[184]
GSM2371154
[185]
GSM2371155
[186]
GSM2371156
[187]
GSM2371157
[188]
GSM2371158
[189]
GSM2371159
[190]
GSM2371160
[191]
GSM2371161
[192]
GSM2371162
[193]
GSM2371163
[194]
GSM2371164
[195]
GSM2371165
[196]
GSM2371166
[197]
GSM2371167
[198]
GSM2371168
[199]
GSM2371169
[200]
GSM2371170
[201]
GSM2371171
[202]
GSM2371172
[203]
GSM2371173
[204]
GSM2371174
[205]
GSM2371175
[206]
GSM2371176
[207]
GSM2371177
[208]
GSM2371178
[209]
GSM2371179
[210]
GSM2371180
[211]
GSM2371181
[212]
GSM2371182
[213]
GSM2371183
[214]
GSM2371184
[215]
GSM2371185
[216]
GSM2371186
[217]
GSM2371187
sample_count
218
group_sizes
null
group_sizes_basis
null
platform
11154
taxon
Homo sapiens
title
RNAseq study of synovial biopsies
resolution_checked_at
2026-11-01T15:39:55Z
resolution_check
identifier_resolution_only_no_download_no_licence_read
downloaded
false
licence_read
false
[8] object · 15 fields
id
data:geo:GSE93272
role
raw
format
geo-series
media_type
application/vnd.ncbi.geo-series+json
uri
https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE93272
sha256
null
bytes
null
access
open
state
resolved
reader
null
reader_status
reader_required
identifier
GSE93272
version
null
note
The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.
structure object · 15 fields
repository
GEO
access_route
GEO accession URL
access_statement_verbatim
The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.
access_statement_kind
data_availability_statement
sample_accessions array · 275 items
[0]
GSM2449608
[1]
GSM2449609
[2]
GSM2449610
[3]
GSM2449611
[4]
GSM2449612
[5]
GSM2449613
[6]
GSM2449614
[7]
GSM2449615
[8]
GSM2449616
[9]
GSM2449617
[10]
GSM2449618
[11]
GSM2449619
[12]
GSM2449620
[13]
GSM2449621
[14]
GSM2449622
[15]
GSM2449623
[16]
GSM2449624
[17]
GSM2449625
[18]
GSM2449626
[19]
GSM2449627
[20]
GSM2449628
[21]
GSM2449629
[22]
GSM2449630
[23]
GSM2449631
[24]
GSM2449632
[25]
GSM2449633
[26]
GSM2449634
[27]
GSM2449635
[28]
GSM2449636
[29]
GSM2449637
[30]
GSM2449638
[31]
GSM2449639
[32]
GSM2449640
[33]
GSM2449641
[34]
GSM2449642
[35]
GSM2449643
[36]
GSM2449644
[37]
GSM2449645
[38]
GSM2449646
[39]
GSM2449647
[40]
GSM2449648
[41]
GSM2449649
[42]
GSM2449650
[43]
GSM2449651
[44]
GSM2449652
[45]
GSM2449653
[46]
GSM2449654
[47]
GSM2449655
[48]
GSM2449656
[49]
GSM2449657
[50]
GSM2449658
[51]
GSM2449659
[52]
GSM2449660
[53]
GSM2449661
[54]
GSM2449662
[55]
GSM2449663
[56]
GSM2449664
[57]
GSM2449665
[58]
GSM2449666
[59]
GSM2449667
[60]
GSM2449668
[61]
GSM2449669
[62]
GSM2449670
[63]
GSM2449671
[64]
GSM2449672
[65]
GSM2449673
[66]
GSM2449674
[67]
GSM2449675
[68]
GSM2449676
[69]
GSM2449677
[70]
GSM2449678
[71]
GSM2449679
[72]
GSM2449680
[73]
GSM2449682
[74]
GSM2449683
[75]
GSM2449684
[76]
GSM2449685
[77]
GSM2449686
[78]
GSM2449687
[79]
GSM2449688
[80]
GSM2449689
[81]
GSM2449690
[82]
GSM2449691
[83]
GSM2449692
[84]
GSM2449693
[85]
GSM2449694
[86]
GSM2449695
[87]
GSM2449696
[88]
GSM2449697
[89]
GSM2449698
[90]
GSM2449699
[91]
GSM2449700
[92]
GSM2449701
[93]
GSM2449702
[94]
GSM2449703
[95]
GSM2449704
[96]
GSM2449705
[97]
GSM2449706
[98]
GSM2449707
[99]
GSM2449708
[100]
GSM2449709
[101]
GSM2449710
[102]
GSM2449711
[103]
GSM2449712
[104]
GSM2449713
[105]
GSM2449714
[106]
GSM2449715
[107]
GSM2449716
[108]
GSM2449717
[109]
GSM2449718
[110]
GSM2449719
[111]
GSM2449720
[112]
GSM2449721
[113]
GSM2449722
[114]
GSM2449723
[115]
GSM2449724
[116]
GSM2449725
[117]
GSM2449726
[118]
GSM2449727
[119]
GSM2449728
[120]
GSM2449729
[121]
GSM2449730
[122]
GSM2449731
[123]
GSM2449732
[124]
GSM2449733
[125]
GSM2449734
[126]
GSM2449735
[127]
GSM2449736
[128]
GSM2449737
[129]
GSM2449738
[130]
GSM2449739
[131]
GSM2449740
[132]
GSM2449741
[133]
GSM2449742
[134]
GSM2449743
[135]
GSM2449744
[136]
GSM2449745
[137]
GSM2449746
[138]
GSM2449747
[139]
GSM2449748
[140]
GSM2449749
[141]
GSM2449750
[142]
GSM2449751
[143]
GSM2449752
[144]
GSM2449753
[145]
GSM2449754
[146]
GSM2449755
[147]
GSM2449756
[148]
GSM2449757
[149]
GSM2449758
[150]
GSM2449759
[151]
GSM2449760
[152]
GSM2449761
[153]
GSM2449762
[154]
GSM2449763
[155]
GSM2449764
[156]
GSM2449765
[157]
GSM2449766
[158]
GSM2449767
[159]
GSM2449768
[160]
GSM2449769
[161]
GSM2449770
[162]
GSM2449771
[163]
GSM2449772
[164]
GSM2449773
[165]
GSM2449774
[166]
GSM2449775
[167]
GSM2449776
[168]
GSM2449777
[169]
GSM2449778
[170]
GSM2449779
[171]
GSM2449780
[172]
GSM2449781
[173]
GSM2449782
[174]
GSM2449783
[175]
GSM2449784
[176]
GSM2449785
[177]
GSM2449786
[178]
GSM2449789
[179]
GSM2449790
[180]
GSM2449791
[181]
GSM2449792
[182]
GSM2449793
[183]
GSM2449794
[184]
GSM2449795
[185]
GSM2449796
[186]
GSM2449797
[187]
GSM2449798
[188]
GSM2449799
[189]
GSM2449800
[190]
GSM2449801
[191]
GSM2449802
[192]
GSM2449803
[193]
GSM2449804
[194]
GSM2449805
[195]
GSM2449806
[196]
GSM2449807
[197]
GSM2449808
[198]
GSM2449809
[199]
GSM2449810
[200]
GSM2449811
[201]
GSM2449812
[202]
GSM2449813
[203]
GSM2449814
[204]
GSM2449815
[205]
GSM2449816
[206]
GSM2449817
[207]
GSM2449818
[208]
GSM2449819
[209]
GSM2449820
[210]
GSM2449821
[211]
GSM2449822
[212]
GSM2449823
[213]
GSM2449824
[214]
GSM2449825
[215]
GSM2449826
[216]
GSM2449827
[217]
GSM2449828
[218]
GSM2449829
[219]
GSM2449830
[220]
GSM2449831
[221]
GSM2449832
[222]
GSM2449833
[223]
GSM2449834
[224]
GSM2449835
[225]
GSM2449836
[226]
GSM2449837
[227]
GSM2449839
[228]
GSM2449840
[229]
GSM2449841
[230]
GSM2449842
[231]
GSM2449843
[232]
GSM2449844
[233]
GSM2449845
[234]
GSM2449846
[235]
GSM2449847
[236]
GSM2449848
[237]
GSM2449849
[238]
GSM2449850
[239]
GSM2449851
[240]
GSM2449852
[241]
GSM2449853
[242]
GSM2449854
[243]
GSM2449855
[244]
GSM2449856
[245]
GSM2449857
[246]
GSM2449858
[247]
GSM2449859
[248]
GSM2449860
[249]
GSM2449861
[250]
GSM2449863
[251]
GSM2449864
[252]
GSM2449865
[253]
GSM2449866
[254]
GSM2449867
[255]
GSM2449868
[256]
GSM2449869
[257]
GSM2449870
[258]
GSM2449871
[259]
GSM2449872
[260]
GSM2449873
[261]
GSM2449874
[262]
GSM2449875
[263]
GSM2449876
[264]
GSM2449877
[265]
GSM2449878
[266]
GSM2449879
[267]
GSM2449880
[268]
GSM2449881
[269]
GSM2449882
[270]
GSM2449883
[271]
GSM2449884
[272]
GSM2449885
[273]
GSM2449886
[274]
GSM2449887
sample_count
275
group_sizes
null
group_sizes_basis
null
platform
570
taxon
Homo sapiens
title
Whole blood gene expression of rheumatoid arthritis
resolution_checked_at
2026-11-01T17:30:42Z
resolution_check
identifier_resolution_only_no_download_no_licence_read
downloaded
false
licence_read
false
gaps array · 16 items
[0]
asset:data:geo:GSE100191 not downloaded
[1]
asset:data:geo:GSE100191 licence not read
[2]
asset:data:geo:GSE12021 not downloaded
[3]
asset:data:geo:GSE12021 licence not read
[4]
asset:data:geo:GSE17755 not downloaded
[5]
asset:data:geo:GSE17755 licence not read
[6]
asset:data:geo:GSE205962 not downloaded
[7]
asset:data:geo:GSE205962 licence not read
[8]
asset:data:geo:GSE39340 not downloaded
[9]
asset:data:geo:GSE39340 licence not read
[10]
asset:data:geo:GSE55235 not downloaded
[11]
asset:data:geo:GSE55235 licence not read
[12]
asset:data:geo:GSE89408 not downloaded
[13]
asset:data:geo:GSE89408 licence not read
[14]
asset:data:geo:GSE93272 not downloaded
[15]
asset:data:geo:GSE93272 licence not read
analysis object · 4 fields
state
complete
methods array · 3 items
[0] object · 10 fields
id
method-1
family
differential_expression
specific
limma_voom
label
Differential expression analysis
dsl_operations array · 3 items
[0]
normalise_counts
[1]
differential_expression_fit
[2]
fdr_bh
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
count_matrix_with_condition
evidence object · 3 fields
score
1
band
verbatim
quote
Differential expression analysis was performed by the “limma” package (version 3.56.1) with the following threshold: p_adj < 0.05 and abs (logFC) > 0.45.
[1] object · 10 fields
id
method-2
family
machine_learning
specific
random_forest
label
Machine learning
dsl_operations array · 0 items
missing_operations array · 1 item
[0]
machine_learning_random_forest
expressible
false
parameters object · 0 fields
data_shapes array · 1 item
[0]
labelled_feature_matrix
evidence object · 3 fields
score
0.7
band
medium
quote
random forest
[2] object · 10 fields
id
method-3
family
machine_learning
specific
support_vector_machine
label
Machine learning
dsl_operations array · 3 items
[0]
train_classifier
[1]
cross_validate
[2]
roc_auc
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
labelled_feature_matrix
evidence object · 3 fields
score
0.7
band
medium
quote
support vector machine
targets array · 2 items
[0] object · 8 fields
id
target-published-estimate
claim_id
claim-target-result
operation
cross_validate
unit
per cent
tolerance
null
state
complete
note
The published estimate as the reader quoted it from the paper. No tolerance is published because the paper states none.
[1] object · 8 fields
id
target-published-estimate-auc
claim_id
claim-target-result
operation
cross_validate
unit
auc
tolerance
null
state
complete
note
The published estimate as the reader quoted it from the paper. No tolerance is published because the paper states none.
gaps array · 0 items
capability object · 3 fields
state
complete
requirements array · 1 item
[0] object · 6 fields
capability_id
capability:computational_analysis
label
Computational analysis platform
assay_families array · 9 items
[0]
meta_analysis
[1]
machine_learning
[2]
survival_analysis
[3]
statistical_regression
[4]
pca
[5]
molecular_docking
[6]
network_analysis
[7]
differential_expression
[8]
gwas
candidate
Automated Assays Laravel application and simulated adapters
gaps array · 0 items
readiness object · 8 fields
state
complete
tier
R1
blocked_on
nothing:replicable_now
axes object · 3 fields
method object · 5 fields
tier
R1
expressible
true
required_operations array · 3 items
[0]
normalise_counts
[1]
differential_expression_fit
[2]
fdr_bh
missing_operations array · 0 items
basis
The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: limma_voom.
capability object · 4 fields
tier
R1
capability_id
capability:computational_analysis
availability_state
owned
basis
The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here.
data object · 6 fields
tier
R1
reachable
true
accessions array · 8 items
[0]
GSE17755
[1]
GSE93272
[2]
GSE100191
[3]
GSE205962
[4]
GSE12021
[5]
GSE55235
[6]
GSE89408
[7]
GSE39340
resolved_accessions array · 8 items
[0]
GSE17755
[1]
GSE93272
[2]
GSE100191
[3]
GSE205962
[4]
GSE12021
[5]
GSE55235
[6]
GSE89408
[7]
GSE39340
statements array · 1 item
[0]
The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.
basis
The paper names GSE17755, GSE93272, GSE100191, GSE205962, GSE12021, GSE55235, GSE89408, GSE39340 in Gene Expression Omnibus (GEO) and the accession resolves in the repository. No dataset was downloaded and no licence was read.
can_run object · 3 fields
dsl_operations array · 3 items
[0]
normalise_counts
[1]
differential_expression_fit
[2]
fdr_bh
assets array · 2 items
[0]
source:pmid:37779906
[1]
data:geo:GSE100191
steps array · 1 item
[0]
run_analysis
must_build object · 5 fields
dsl_operations array · 0 items
converters array · 0 items
readers array · 0 items
capabilities array · 0 items
data_gaps array · 1 item
[0]
data:geo:GSE100191
gaps array · 0 items
review object · 5 fields
state
partial
machine_critique object · 5 fields
kind
extraction_review
reader object · 5 fields
model
MiniMax-M3.1-Flash
provider
minimax
provider_family
minimax
read_at
2026-11-01T18:54:48Z
independence
single_reader
evidence_check object · 2 fields
method
verbatim
availability
verbatim
calls
5
usage object · 3 fields
prompt_tokens
14137
completion_tokens
2835
cached_tokens
264
stages array · 1 item
[0] object · 4 fields
id
extraction
state
complete
reader
MiniMax-M3.1-Flash
provider
minimax
gaps array · 3 items
[0]
review.independent_reader
[1]
review.machine_critique
[2]
review.human_critique
execution object · 5 fields
state
absent
status
not_run
mode
null
runs array · 0 items
gaps array · 1 item
[0]
execution.not_run
provenance object · 7 fields
entities array · 3 items
[0] object · 3 fields
id
pmid:37779906
type
paper
label
pmid:37779906
[1] object · 3 fields
id
source:pmid:37779906
type
manuscript
label
PMID 37779906 full text
[2] object · 3 fields
id
data:geo:GSE100191
type
dataset
label
GEO GSE100191
activities array · 3 items
[0] object · 4 fields
id
activity-reading-sci-2026-11-01
type
classification
used array · 3 items
[0] object · 3 fields
id
pmid:37779906
type
paper
label
pmid:37779906
[1] object · 3 fields
id
source:pmid:37779906
type
manuscript
label
PMID 37779906 full text
[2] object · 3 fields
id
data:geo:GSE100191
type
dataset
label
GEO GSE100191
agent
agent:corpus-assay-engine
[1] object · 4 fields
id
activity-read-37779906
type
extraction
used array · 1 item
[0]
source:pmid:37779906
agent
agent:minimax-reader
[2] object · 4 fields
id
activity-resolve-GSE100191
type
accession_resolution
used array · 1 item
[0]
data:geo:GSE100191
agent
agent:corpus-assay-engine
agents array · 2 items
[0] object · 3 fields
id
agent:corpus-assay-engine
type
software
label
Corpus assay engine
[1] object · 3 fields
id
agent:minimax-reader
type
software
label
MiniMax literature reader
source_run_id
reading-sci-2026-11-01
dsl_catalogue_fingerprint
343fb30756ba6ec3
capability_table_fingerprint
2cb3e42f047d2ecf
snapshot_id
ba34b49490d9bfaf
lineage object · 4 fields
parent_id
pmid:37779906
parent_hash
sha256:fcb36dadbcb27df5652508da0e94639cd997425f3efe27fa9180d25056ceaffb
fork_of
null
gaps array · 20 items
[0]
asset:data:geo:GSE100191 licence not read
[1]
asset:data:geo:GSE100191 not downloaded
[2]
asset:data:geo:GSE12021 licence not read
[3]
asset:data:geo:GSE12021 not downloaded
[4]
asset:data:geo:GSE17755 licence not read
[5]
asset:data:geo:GSE17755 not downloaded
[6]
asset:data:geo:GSE205962 licence not read
[7]
asset:data:geo:GSE205962 not downloaded
[8]
asset:data:geo:GSE39340 licence not read
[9]
asset:data:geo:GSE39340 not downloaded
[10]
asset:data:geo:GSE55235 licence not read
[11]
asset:data:geo:GSE55235 not downloaded
[12]
asset:data:geo:GSE89408 licence not read
[13]
asset:data:geo:GSE89408 not downloaded
[14]
asset:data:geo:GSE93272 licence not read
[15]
asset:data:geo:GSE93272 not downloaded
[16]
execution.not_run
[17]
review.human_critique
[18]
review.independent_reader
[19]
review.machine_critique
assays array · 2 items
[0] object · 11 fields
role
data_generation
definition_id
apoptosis_assay
definition_version
99e1195c7d6f
required_operations array · 0 items
support_state
partial
source_hashes object · 1 field
assay_lexicon
99e1195c7d6f
confidence
0.99
evidence array · 2 items
[0]
ts of KEGG, DE-ARGs were mainly enriched in “apoptosis,” “pathogenic Escherichia coli infection” an
[1]
nts (CC). CC mainly contained “melanosome,” “caspase complex,” and “focal adhesion” (Figure 3B).
order_basis
paper_text_order:3366
id
assay:1
[1] object · 11 fields
role
analysis
definition_id
differential_expression
definition_version
f2132e96ae6c7a4e
required_operations array · 3 items
[0]
normalise_counts
[1]
differential_expression_fit
[2]
fdr_bh
support_state
supported
source_hashes object · 1 field
dsl_catalogue
f2132e96ae6c7a4e
evidence array · 1 item
[0]
Differential expression analysis was performed by the “limma” package (version 3.56.1) with the following threshold: p_adj < 0.05 and abs (logFC) > 0.45.
order_basis
consumes_earlier_output
id
assay:2
Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37779906", "kind": "science-record", "revision": 5, "content_hash": "sha256:85291049cffb67188b5404e872b7a4abe03597155ca7ef4180e788841419f0a5", "created_at": "2026-11-01T19:02:37Z", "updated_at": "2026-11-01T22:52:49Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Machine learning and bioinformatics analysis to identify autophagy-related biomarkers in peripheral blood for rheumatoid arthritis.", "doi": "10.3389/fgene.2023.1238407", "pmid": "37779906", "authors": ["Guoqi Dong", "Hui Gao", "Yingqi Chen", "Huayuan Yang"], "journal": "Frontiers in genetics", "year": 2023, "abstract": "Background: Although rheumatoid arthritis (RA) is a common autoimmune disease, the precise pathogenesis of the disease remains unclear. Recent research has unraveled the role of autophagy in the development of RA. This research aims to explore autophagy-related diagnostic biomarkers in the peripheral blood of RA patients. Methods: The gene expression profiles of GSE17755 were retrieved from the gene expression ontology (GEO) database. Differentially expressed autophagy-related genes (DE-ARGs) were identified for the subsequent research by inserting autophagy-related genes and differentially expressed genes (DEGs). Three machine learning algorithms, including random forest, support vector machine recursive feature elimination (SVM-RFE), and least absolute shrinkage and selection operator (LASSO), were employed to identify diagnostic biomarkers. A nomogram model was constructed to assess the diagnostic value of the biomarkers. The CIBERSORT algorithm was performed to investigate the correlation of the diagnostic biomarkers with immune cells and immune factors. Finally, the diagnostic efficacy and differential expression trend of diagnostic biomarkers were validated in multiple cohorts containing different tissues and diseases. Results: In this study, 25 DE-ARGs were identified between RA and healthy individuals. In addition to \"macroautophagy\" and \"autophagy-animal,\" DE-ARGs were also associated with several types of programmed cell death and immune-related pathways according to GO and KEGG analysis. Three diagnostic biomarkers, EEF2, HSP90AB1 and TNFSF10, were identified by the random forest, SVM-RFE, and LASSO. The nomogram model demonstrated excellent diagnostic value in GSE17755 (AUC = 0.995, 95% CI: 0.988-0.999). Furthermore, immune infiltration analysis showed a remarkable association between EEF2, HSP90AB1, and TNFSF10 expression with various immune cells and immune factors. The three diagnostic biomarkers also exhibited good diagnostic efficacy and demonstrated the same trend of differential expression in multiple validation cohorts. Conclusion: This study identified autophagy-related diagnostic biomarkers based on three machine learning algorithms, providing promising targets for the diagnosis and treatment of RA.", "claims": [{"id": "claim-method", "statement": "Differential expression analysis was performed by the “limma” package (version 3.56.1) with the following threshold: p_adj < 0.05 and abs (logFC) > 0.45.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37779906", "locator": "sections[4] \"2.2 Identification of differentially expressed autophagy-related genes (DE-ARGs)\" paragraph 2", "quote": "Differential expression analysis was performed by the “limma” package (version 3.56.1) with the following threshold: p_adj < 0.05 and abs (logFC) > 0.45."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37779906", "locator": "sections[20] \"Data availability statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The nomogram model demonstrated excellent diagnostic value in GSE17755 (AUC = 0.995, 95% CI: 0.988–0.999).", "kind": "quantitative", "target": {"value": 95.0, "unit": "per cent", "tolerance": null, "source": "The nomogram model demonstrated excellent diagnostic value in GSE17755 (AUC = 0.995, 95% CI: 0.988–0.999)."}, "evidence": [{"asset": "source:pmid:37779906", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The nomogram model demonstrated excellent diagnostic value in GSE17755 (AUC = 0.995, 95% CI: 0.988–0.999)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The nomogram model demonstrated excellent diagnostic value in GSE17755 (AUC = 0.995, 95% CI: 0.988–0.999).", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:37779906"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE100191"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE100191"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:37779906", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fgene.2023.1238407", "sha256": "sha256:bd0de59a2a178e9afa3dbaad3455148524f327b2405a6fcf1c3ecff55e1b9fa3", "bytes": 45496, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fgene.2023.1238407", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10533932", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE100191", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE100191", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE100191", "version": null, "note": "The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM2674341", "GSM2674342", "GSM2674343", "GSM2674344", "GSM2674345", "GSM2674346", "GSM2674347", "GSM2674348", "GSM2674349", "GSM2674350", "GSM2674351", "GSM2674352", "GSM2674353", "GSM2674354", "GSM2674355", "GSM2674356", "GSM2674357", "GSM2674358", "GSM2674359"], "sample_count": 19, "group_sizes": null, "group_sizes_basis": null, "platform": "13497", "taxon": "Homo sapiens", "title": "Transcriptional Signature Associated with Early Rheumatoid Arthritis and Healthy Individuals at high risk to develop the disease.", "resolution_checked_at": "2026-11-01T19:09:46Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE12021", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE12021", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE12021", "version": null, "note": "The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM302859", 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