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pmid:33335112

Detecting methylation signatures in neurodegenerative disease by density-based clustering of applications with reducing noise.

Saurav Mallik · Zhongming Zhao

Scientific reports · 2020 doi:10.1038/s41598-020-78463-3

Paper

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Detecting methylation signatures in neurodegenerative disease by density-based clustering of applications with reducing noise.
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10.1038/s41598-020-78463-3
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33335112
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Saurav Mallik
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Zhongming Zhao
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Scientific reports
year
2020
abstract
There have been numerous genetic and epigenetic datasets generated for the study of complex disease including neurodegenerative disease. However, analysis of such data often suffers from detecting the outliers of the samples, which subsequently affects the extraction of the true biological signals involved in the disease. To address this critical issue, we developed a novel framework for identifying methylation signatures using consecutive adaptation of a well-known outlier detection algorithm, density based clustering of applications with reducing noise (DBSCAN) followed by hierarchical clustering. We applied the framework to two representative neurodegenerative diseases, Alzheimer's disease (AD) and Down syndrome (DS), using DNA methylation datasets from public sources (Gene Expression Omnibus, GEO accession ID: GSE74486). We first applied DBSCAN algorithm to eliminate outliers, and then used Limma statistical method to determine differentially methylated genes. Next, hierarchical clustering technique was applied to detect gene modules. Our analysis identified a methylation signature comprising 21 genes for AD and a methylation signature comprising 89 genes for DS, respectively. Our evaluation indicated that these two signatures could lead to high classification accuracy values (92% and 70%) for these two diseases. In summary, this framework will be useful to better detect outlier-free genetic and epigenetic signatures in various complex diseases and their developmental stages.
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We used a CpG methylation profile (GEO ID GSE74486) in the tissue of human FC neurons61 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE74486, Accessed date: November 25, 2018).
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We used a CpG methylation profile (GEO ID GSE74486) in the tissue of human FC neurons61 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE74486, Accessed date: November 25, 2018).
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We used a CpG methylation profile (GEO ID GSE74486) in the tissue of human FC neurons61 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE74486, Accessed date: November 25, 2018).
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GSM3945256
[510]
GSM3945257
[511]
GSM3945258
[512]
GSM3945259
[513]
GSM3945260
[514]
GSM3945261
[515]
GSM3945262
[516]
GSM3945263
[517]
GSM3945264
[518]
GSM3945265
[519]
GSM3945266
[520]
GSM3945267
[521]
GSM3945268
[522]
GSM3945269
[523]
GSM3945270
[524]
GSM3945271
[525]
GSM3945272
[526]
GSM3945273
[527]
GSM3945274
[528]
GSM3945275
[529]
GSM3945276
[530]
GSM3945277
[531]
GSM3945278
[532]
GSM3945279
[533]
GSM3945280
[534]
GSM3945281
[535]
GSM3945282
[536]
GSM3945283
[537]
GSM3945284
[538]
GSM3945285
[539]
GSM3945286
[540]
GSM3945287
[541]
GSM3945288
[542]
GSM3945289
[543]
GSM3945290
[544]
GSM3945291
[545]
GSM3945292
[546]
GSM3945293
[547]
GSM3945294
[548]
GSM3945295
[549]
GSM3945296
[550]
GSM3945297
[551]
GSM3945298
[552]
GSM3945299
[553]
GSM3945300
[554]
GSM3945301
[555]
GSM3945302
[556]
GSM3945303
[557]
GSM3945304
[558]
GSM3945305
[559]
GSM3945306
[560]
GSM3945307
[561]
GSM3945308
[562]
GSM3945309
[563]
GSM3945310
[564]
GSM3945311
[565]
GSM3945312
[566]
GSM3945313
[567]
GSM3945314
[568]
GSM3945315
[569]
GSM3945316
[570]
GSM3945317
[571]
GSM3945318
[572]
GSM3945319
[573]
GSM3945320
[574]
GSM3945321
[575]
GSM3945322
[576]
GSM3945323
[577]
GSM3945324
[578]
GSM3945325
[579]
GSM3945326
[580]
GSM3945327
[581]
GSM3945328
[582]
GSM3945329
[583]
GSM3945330
[584]
GSM3945331
[585]
GSM3945332
[586]
GSM3945333
[587]
GSM3945334
[588]
GSM3945335
[589]
GSM3945336
[590]
GSM3945337
[591]
GSM3945338
[592]
GSM3945339
[593]
GSM3945340
[594]
GSM3945341
[595]
GSM3945342
[596]
GSM3945343
[597]
GSM3945344
[598]
GSM3945345
[599]
GSM3945346
[600]
GSM3945347
[601]
GSM3945348
[602]
GSM3945349
[603]
GSM3945350
[604]
GSM3945351
[605]
GSM3945352
[606]
GSM3945353
[607]
GSM3945354
[608]
GSM3945355
[609]
GSM3945356
[610]
GSM3945357
[611]
GSM3945358
[612]
GSM3945359
[613]
GSM3945360
[614]
GSM3945361
[615]
GSM3945362
[616]
GSM3945363
[617]
GSM3945364
[618]
GSM3945365
[619]
GSM3945366
[620]
GSM3945367
[621]
GSM3945368
[622]
GSM3945369
[623]
GSM3945370
[624]
GSM3945371
[625]
GSM3945372
[626]
GSM3945373
[627]
GSM3945374
[628]
GSM3945375
[629]
GSM3945376
[630]
GSM3945377
[631]
GSM3945378
[632]
GSM3945379
[633]
GSM3945380
[634]
GSM3945381
[635]
GSM3945382
[636]
GSM3945383
[637]
GSM3945384
[638]
GSM3945385
[639]
GSM3945386
[640]
GSM3945387
[641]
GSM3945388
[642]
GSM3945389
[643]
GSM3945390
[644]
GSM3945391
[645]
GSM3945392
[646]
GSM3945393
[647]
GSM3945394
[648]
GSM3945395
[649]
GSM3945396
[650]
GSM3945397
[651]
GSM3945398
[652]
GSM3945399
[653]
GSM3945400
[654]
GSM3945401
[655]
GSM3945402
[656]
GSM3945403
[657]
GSM3945404
[658]
GSM3945405
[659]
GSM3945406
[660]
GSM3945407
[661]
GSM3945408
[662]
GSM3945409
[663]
GSM3945410
[664]
GSM3945411
[665]
GSM3945412
[666]
GSM3945413
[667]
GSM3945414
[668]
GSM3945415
[669]
GSM3945416
[670]
GSM3945417
[671]
GSM3945418
[672]
GSM3945419
[673]
GSM3945420
[674]
GSM3945421
[675]
GSM3945422
[676]
GSM3945423
[677]
GSM3945424
[678]
GSM3945425
[679]
GSM3945426
[680]
GSM3945427
[681]
GSM3945428
[682]
GSM3945429
[683]
GSM3945430
[684]
GSM3945431
[685]
GSM3945432
[686]
GSM3945433
[687]
GSM3945434
[688]
GSM3945435
[689]
GSM3945436
[690]
GSM3945437
[691]
GSM3945438
[692]
GSM3945439
[693]
GSM3945440
[694]
GSM3945441
[695]
GSM3945442
[696]
GSM3945443
[697]
GSM3945444
[698]
GSM3945445
[699]
GSM3945446
[700]
GSM3945447
[701]
GSM3945448
[702]
GSM3945449
[703]
GSM3945450
[704]
GSM3945451
[705]
GSM3945452
[706]
GSM3945453
[707]
GSM3945454
[708]
GSM3945455
[709]
GSM3945456
[710]
GSM3945457
[711]
GSM3945458
[712]
GSM3945459
[713]
GSM3945460
[714]
GSM3945461
[715]
GSM3945462
[716]
GSM3945463
[717]
GSM3945464
[718]
GSM3945465
[719]
GSM3945466
[720]
GSM3945467
[721]
GSM3945468
[722]
GSM3945469
[723]
GSM3945470
[724]
GSM3945471
[725]
GSM3945472
[726]
GSM3945473
[727]
GSM3945474
[728]
GSM3945475
[729]
GSM3945476
[730]
GSM3945477
[731]
GSM3945478
[732]
GSM3945479
[733]
GSM3945480
[734]
GSM3945481
[735]
GSM3945482
[736]
GSM3945483
[737]
GSM3945484
[738]
GSM3945485
[739]
GSM3945486
[740]
GSM3945487
[741]
GSM3945488
[742]
GSM3945489
[743]
GSM3945490
[744]
GSM3945491
[745]
GSM3945492
[746]
GSM3945493
[747]
GSM3945494
[748]
GSM3945495
[749]
GSM3945496
[750]
GSM3945497
[751]
GSM3945498
[752]
GSM3945499
[753]
GSM3945500
[754]
GSM3945501
[755]
GSM3945502
[756]
GSM3945503
[757]
GSM3945504
[758]
GSM3945505
[759]
GSM3945506
[760]
GSM3945507
[761]
GSM3945508
[762]
GSM3945509
[763]
GSM3945510
[764]
GSM3945511
[765]
GSM3945512
[766]
GSM3945513
[767]
GSM3945514
[768]
GSM3945515
[769]
GSM3945516
[770]
GSM3945517
[771]
GSM3945518
[772]
GSM3945519
[773]
GSM3945520
[774]
GSM3945521
[775]
GSM3945522
[776]
GSM3945523
[777]
GSM3945524
[778]
GSM3945525
[779]
GSM3945526
[780]
GSM3945527
[781]
GSM3945528
[782]
GSM3945529
[783]
GSM3945530
[784]
GSM3945531
[785]
GSM3945532
[786]
GSM3945533
[787]
GSM3945534
[788]
GSM3945535
[789]
GSM3945536
[790]
GSM3945537
[791]
GSM3945538
[792]
GSM3945539
[793]
GSM3945540
[794]
GSM3945541
[795]
GSM3945542
[796]
GSM3945543
[797]
GSM3945544
[798]
GSM3945545
[799]
GSM3945546
[800]
GSM3945547
[801]
GSM3945548
[802]
GSM3945549
[803]
GSM3945550
[804]
GSM3945551
[805]
GSM3945552
[806]
GSM3945553
[807]
GSM3945554
sample_count
808
group_sizes
null
group_sizes_basis
null
platform
13534
taxon
Homo sapiens
title
Illumina 450K Methylation Data of Alzheimer's Disease: Middle Temporal Gyrus and Cerebellum
resolution_checked_at
2026-11-01T15:39:54Z
resolution_check
identifier_resolution_only_no_download_no_licence_read
downloaded
false
licence_read
false
[2] object · 15 fields
id
data:geo:GSE74486
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=GSE74486
sha256
null
bytes
null
access
open
state
resolved
reader
null
reader_status
reader_required
identifier
GSE74486
version
null
note
We used a CpG methylation profile (GEO ID GSE74486) in the tissue of human FC neurons61 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE74486, Accessed date: November 25, 2018).
structure object · 15 fields
repository
GEO
access_route
GEO accession URL
access_statement_verbatim
We used a CpG methylation profile (GEO ID GSE74486) in the tissue of human FC neurons61 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE74486, Accessed date: November 25, 2018).
access_statement_kind
repository_route_statement
sample_accessions array · 131 items
[0]
GSM1921482
[1]
GSM1921483
[2]
GSM1921484
[3]
GSM1921485
[4]
GSM1921486
[5]
GSM1921487
[6]
GSM1921488
[7]
GSM1921489
[8]
GSM1921490
[9]
GSM1921491
[10]
GSM1921492
[11]
GSM1921493
[12]
GSM1921494
[13]
GSM1921495
[14]
GSM1921496
[15]
GSM1921497
[16]
GSM1921498
[17]
GSM1921499
[18]
GSM1921500
[19]
GSM1921501
[20]
GSM1921502
[21]
GSM1921503
[22]
GSM1921504
[23]
GSM1921505
[24]
GSM1921506
[25]
GSM1921507
[26]
GSM1921508
[27]
GSM1921509
[28]
GSM1921510
[29]
GSM1921511
[30]
GSM1921512
[31]
GSM1921513
[32]
GSM1921514
[33]
GSM1921515
[34]
GSM1921516
[35]
GSM1921517
[36]
GSM1921518
[37]
GSM1921519
[38]
GSM1921520
[39]
GSM1921521
[40]
GSM1921522
[41]
GSM1921523
[42]
GSM1921524
[43]
GSM1921525
[44]
GSM1921526
[45]
GSM1921527
[46]
GSM1921528
[47]
GSM1921529
[48]
GSM1921530
[49]
GSM1921531
[50]
GSM1921532
[51]
GSM1921533
[52]
GSM1921534
[53]
GSM1921535
[54]
GSM1921536
[55]
GSM1921537
[56]
GSM1921538
[57]
GSM1921539
[58]
GSM1921540
[59]
GSM1921541
[60]
GSM1921542
[61]
GSM1921543
[62]
GSM1921544
[63]
GSM1921545
[64]
GSM1921546
[65]
GSM1921547
[66]
GSM1921548
[67]
GSM1921549
[68]
GSM1921550
[69]
GSM1921551
[70]
GSM1921552
[71]
GSM1921553
[72]
GSM1921554
[73]
GSM1921555
[74]
GSM1921556
[75]
GSM1921557
[76]
GSM1921558
[77]
GSM1921559
[78]
GSM1921560
[79]
GSM1921561
[80]
GSM1921562
[81]
GSM1921563
[82]
GSM1921564
[83]
GSM1921565
[84]
GSM1921566
[85]
GSM1921567
[86]
GSM1921568
[87]
GSM1921569
[88]
GSM1921570
[89]
GSM1921571
[90]
GSM1921572
[91]
GSM1921573
[92]
GSM1921574
[93]
GSM1921575
[94]
GSM1921576
[95]
GSM1921577
[96]
GSM1921578
[97]
GSM1921579
[98]
GSM1921580
[99]
GSM1921581
[100]
GSM1921582
[101]
GSM1921583
[102]
GSM1921584
[103]
GSM1921585
[104]
GSM1921586
[105]
GSM1921587
[106]
GSM1921588
[107]
GSM1921589
[108]
GSM1921590
[109]
GSM1921591
[110]
GSM1921592
[111]
GSM1921593
[112]
GSM1921594
[113]
GSM1921595
[114]
GSM1921596
[115]
GSM1921597
[116]
GSM1921598
[117]
GSM1921599
[118]
GSM1921600
[119]
GSM1921601
[120]
GSM1921602
[121]
GSM1921603
[122]
GSM1921604
[123]
GSM1921605
[124]
GSM1921606
[125]
GSM1921607
[126]
GSM1921608
[127]
GSM1921609
[128]
GSM1921610
[129]
GSM1921611
[130]
GSM1921612
sample_count
131
group_sizes
null
group_sizes_basis
null
platform
13534
taxon
Homo sapiens
title
Trans-effects of chromosome aneuploidies on DNA methylation patterns: DNA methylation analysis of Down syndrome in human brain tissues and cells
resolution_checked_at
2026-11-01T15:39:55Z
resolution_check
identifier_resolution_only_no_download_no_licence_read
downloaded
false
licence_read
false
[3] object · 16 fields
id
asset:table:pmid:33335112:1
role
table
format
jats-table
media_type
application/vnd.jats.table+json
uri
null
sha256
sha256:6def5b6a57cba246a0888cb057d56c1032b6b9c94cabb706bd6569e0855459a4
bytes
1183
access
open
state
verified
reader
read_table
reader_status
available
identifier
section[17]/table-wrap[1]#Tab5
version
null
note
Top significant GO terms enriched with the Alzheimer’s disease specific genes.
structure object · 5 fields
pmcid
PMC7747741
section_index
9
section_title
Gene set enrichment analysis
row_count
7
column_count
3
table object · 8 fields
label
Table 5
caption
Top significant GO terms enriched with the Alzheimer’s disease specific genes.
rows array · 7 items
[0] array · 3 items
[0]
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox {Gsig}^{\text{a}}$$\end{document}Gsiga
[1]
GO \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox {term}^{\text{b}}$$\end{document}termb
[2]
Enrichment p value
[1] array · 3 items
[0]
CYSLTR2
[1]
GO-CC: GO:0016021 Integral component of membrane
[2]
0.031
[2] array · 3 items
[0]
LCE1F
[1]
GO-MF: GO:0005198 Structural molecule activity
[2]
0.011
[3] array · 2 items
[0]
GO-CC: GO:0001533 Cornified envelope
[1]
0.027
[4] array · 2 items
[0]
GO-BP: GO:0031424 Keratinization
[1]
0.031
[5] array · 2 items
[0]
GO-BP: GO:0016021 Integral component of membrane
[1]
0.031
[6] array · 2 items
[0]
GO-BP: GO:0018149 Peptide cross-linking
[1]
0.033
locator
section[17]/table-wrap[1]#Tab5
[4] object · 16 fields
id
asset:table:pmid:33335112:2
role
table
format
jats-table
media_type
application/vnd.jats.table+json
uri
null
sha256
sha256:821fe4c6a574b8616871095389a12c7ae9b6e399282aaacc21f9c36e4fdba4e1
bytes
7603
access
open
state
verified
reader
read_table
reader_status
available
identifier
section[17]/table-wrap[2]#Tab6
version
null
note
Top significant KEGG pathways and GO terms enriched with Down syndrome specific genes.
structure object · 5 fields
pmcid
PMC7747741
section_index
9
section_title
Gene set enrichment analysis
row_count
22
column_count
3
table object · 8 fields
label
Table 6
caption
Top significant KEGG pathways and GO terms enriched with Down syndrome specific genes.
rows array · 22 items
[0] array · 3 items
[0]
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox {Gsig}^{\text{a}}$$\end{document}Gsiga
[1]
KEGG pathway or GO \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox {term}^{\text{b}}$$\end{document}termb
[2]
Enrichment p value
[1] array · 3 items
[0]
ADH1B
[1]
KEGG pathway: hsa05204: Chemical carcinogenesis
[2]
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$3.24\times 10^{-3}$$\end{document}3.24×10-3
[2] array · 2 items
[0]
KEGG pathway: hsa00982: Drug metabolism-cytochrome P450
[1]
0.011
[3] array · 3 items
[0]
CALML6
[1]
KEGG pathway: hsa04740: Olfactory transduction
[2]
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$5.51\times 10^{-5}$$\end{document}5.51×10-5
[4] array · 3 items
[0]
CD33
[1]
GO-MF: GO:0004872 Receptor activity
[2]
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$2.21\times 10^{-5}$$\end{document}2.21×10-5
[5] array · 3 items
[0]
CLEC2B
[1]
GO-MF: GO:0030246 Carbohydrate binding
[2]
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$5.26\times 10^{-9}$$\end{document}5.26×10-9
[6] array · 3 items
[0]
CX3CR1
[1]
GO-BP: GO:0006935 Chemotaxis
[2]
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$8.73\times 10^{-6}$$\end{document}8.73×10-6
[7] array · 2 items
[0]
GO-BP: GO:0070098 Chemokine-mediated signaling pathway
[1]
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$2.60\times 10^{-5}$$\end{document}2.60×10-5
[8] array · 2 items
[0]
KEGG pathway: hsa04060: Cytokine–cytokine receptor interaction
[1]
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$7.49\times 10^{-4}$$\end{document}7.49×10-4
[9] array · 2 items
[0]
KEGG pathway: hsa04062: Chemokine signaling pathway
[1]
0.044
[10] array · 3 items
[0]
HAMP
[1]
GO-BP: GO:0006955 Immune response
[2]
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$3.25\times 10^{-10}$$\end{document}3.25×10-10
[11] array · 3 items
[0]
LCE3A
[1]
GO-BP: GO:0030216 Keratinocyte differentiation
[2]
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$9.70\times 10^{-4}$$\end{document}9.70×10-4
[12] array · 3 items
[0]
OR8G1
[1]
KEGG pathway: hsa04740: Olfactory transduction
[2]
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$5.51\times 10^{-5}$$\end{document}5.51×10-5
[13] array · 3 items
[0]
OR52H1
[1]
KEGG pathway: hsa04740: Olfactory transduction
[2]
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$5.51\times 10^{-5}$$\end{document}5.51×10-5
[14] array · 3 items
[0]
PGLYRP1
[1]
GO-BP: GO:0006955 Immune response
[2]
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$3.25\times 10^{-10}$$\end{document}3.25×10-10
[15] array · 2 items
[0]
GO-CC: GO:0005576 Extracellular region
[1]
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$2.40\times 10^{-9}$$\end{document}2.40×10-9
[16] array · 3 items
[0]
RARRES1
[1]
GO-CC: GO:0016021 Integral component of membrane
[2]
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$1.59\times 10^{-11}$$\end{document}1.59×10-11
[17] array · 3 items
[0]
TAS2R41
[1]
GO-BP: GO:0001580 Detection of chemical stimulus involved in sensory perception of bitter taste
[2]
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$2.52\times 10^{-5}$$\end{document}2.52×10-5
[18] array · 3 items
[0]
TINAG
[1]
GO-BP: GO:0006955 Immune response
[2]
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$3.25\times 10^{-10}$$\end{document}3.25×10-10
[19] array · 3 items
[0]
TINAGL1
[1]
GO-BP: GO:0006955 Immune response
[2]
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$3.25\times 10^{-10}$$\end{document}3.25×10-10
[20] array · 3 items
[0]
TNFRSF17
[1]
GO-MF: GO:0004872 Receptor activity
[2]
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$2.21\times 10^{-5}$$\end{document}2.21×10-5
[21] array · 2 items
[0]
KEGG pathway: hsa04060: Cytokine–cytokine receptor interaction
[1]
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$7.49\times 10^{-4}$$\end{document}7.49×10-4
locator
section[17]/table-wrap[2]#Tab6
[5] object · 16 fields
id
asset:table:pmid:33335112:3
role
table
format
jats-table
media_type
application/vnd.jats.table+json
uri
null
sha256
sha256:ae0685e9f5a304e6f9ab8a19bc160a09e2b7551e12c2cf1eba5a3945f035ef15
bytes
1145
access
open
state
verified
reader
read_table
reader_status
available
identifier
section[12]/table-wrap[1]#Tab1
version
null
note
Summary of DBSCAN clustering for outlier (noisy feature) removal prior to statistical test.
structure object · 5 fields
pmcid
PMC7747741
section_index
12
section_title
Filtering noise-free features using DBSCAN clustering
row_count
8
column_count
6
table object · 8 fields
label
Table 1
caption
Summary of DBSCAN clustering for outlier (noisy feature) removal prior to statistical test.
rows array · 8 items
[0] array · 4 items
[0]
Comparison
[1]
Features
[2]
# outliers
[3]
# features
[1] array · 3 items
[0]
Cluster 1
[1]
Cluster 2
[2]
Cluster 3
[2] array · 6 items
[0]
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox {AD}$$\end{document}AD vs control
[1]
# border features
[2]
439
[3]
206
[4]
0
[5]
–
[3] array · 5 items
[0]
# seed features
[1]
0
[2]
19,592
[3]
10
[4]
–
[4] array · 5 items
[0]
Total
[1]
439
[2]
19,798
[3]
10
[4]
–
[5] array · 6 items
[0]
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox {DS}$$\end{document}DS vs control
[1]
# border features
[2]
1525
[3]
559
[4]
0
[5]
0
[6] array · 5 items
[0]
# seed features
[1]
0
[2]
18,148
[3]
10
[4]
5
[7] array · 5 items
[0]
Total
[1]
1525
[2]
18,707
[3]
10
[4]
5
locator
section[12]/table-wrap[1]#Tab1
[6] object · 16 fields
id
asset:table:pmid:33335112:4
role
table
format
jats-table
media_type
application/vnd.jats.table+json
uri
null
sha256
sha256:543e81b21e0d10e1da0d1c7626c2dc5ded109d5c67ab8dab3899a9267a00c193
bytes
557
access
open
state
verified
reader
read_table
reader_status
available
identifier
section[14]/table-wrap[1]#Tab2
version
null
note
Comparison of various cluster validity indices between our proposed method and k-means clustering for AD vs control.
structure object · 5 fields
pmcid
PMC7747741
section_index
14
section_title
Detection of gene modules
row_count
9
column_count
3
table object · 8 fields
label
Table 2
caption
Comparison of various cluster validity indices between our proposed method and k-means clustering for AD vs control.
rows array · 9 items
[0] array · 3 items
[0]
Cluster validity index
[1]
Proposed method
[2]
K-means clustering
[1] array · 3 items
[0]
Ball_hall
[1]
0.331
[2]
0.111
[2] array · 3 items
[0]
Davies_bouldin
[1]
5.205
[2]
2.090
[3] array · 3 items
[0]
Dunn
[1]
0.082
[2]
0.009
[4] array · 3 items
[0]
G_plus
[1]
0.136
[2]
0.070
[5] array · 3 items
[0]
Gdi11
[1]
0.082
[2]
0.009
[6] array · 3 items
[0]
Gdi12
[1]
0.363
[2]
0.068
[7] array · 3 items
[0]
Gdi31
[1]
0.394
[2]
0.209
[8] array · 3 items
[0]
Ray_turi
[1]
14.018
[2]
2.229
locator
section[14]/table-wrap[1]#Tab2
[7] object · 16 fields
id
asset:table:pmid:33335112:5
role
table
format
jats-table
media_type
application/vnd.jats.table+json
uri
null
sha256
sha256:ebb0622c189b45765c50d7cf1ea184ac1158203355f1c0879fc27edf920df2d9
bytes
556
access
open
state
verified
reader
read_table
reader_status
available
identifier
section[14]/table-wrap[2]#Tab3
version
null
note
Comparison of various cluster validity indices between our proposed method and k-means clustering for DS vs control.
structure object · 5 fields
pmcid
PMC7747741
section_index
14
section_title
Detection of gene modules
row_count
9
column_count
3
table object · 8 fields
label
Table 3
caption
Comparison of various cluster validity indices between our proposed method and k-means clustering for DS vs control.
rows array · 9 items
[0] array · 3 items
[0]
Cluster validity index
[1]
Proposed method
[2]
K-means clustering
[1] array · 3 items
[0]
Ball_hall
[1]
0.221
[2]
0.192
[2] array · 3 items
[0]
Davies_bouldin
[1]
3.040
[2]
2.259
[3] array · 3 items
[0]
Dunn
[1]
0.071
[2]
0.014
[4] array · 3 items
[0]
G_plus
[1]
0.089
[2]
0.084
[5] array · 3 items
[0]
Gdi11
[1]
0.071
[2]
0.014
[6] array · 3 items
[0]
Gdi12
[1]
0.325
[2]
0.085
[7] array · 3 items
[0]
Gdi31
[1]
0.257
[2]
0.233
[8] array · 3 items
[0]
Ray_turi
[1]
5.746
[2]
2.770
locator
section[14]/table-wrap[2]#Tab3
[8] object · 16 fields
id
asset:table:pmid:33335112:6
role
table
format
jats-table
media_type
application/vnd.jats.table+json
uri
null
sha256
sha256:8958020b24a1dfe733395106c32b25d83a341bb7b7f52240c411086100830cd5
bytes
1635
access
open
state
verified
reader
read_table
reader_status
available
identifier
section[16]/table-wrap[1]#Tab4
version
null
note
Classification accuracy, area under the curve (AUC), and precision by cross-validation (CV) in two comparisons.
structure object · 5 fields
pmcid
PMC7747741
section_index
16
section_title
Classification analysis of methylation signature
row_count
7
column_count
5
table object · 8 fields
label
Table 4
caption
Classification accuracy, area under the curve (AUC), and precision by cross-validation (CV) in two comparisons.
rows array · 7 items
[0] array · 5 items
[0]
Comparison
[1]
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox {CV}$$\end{document}CV fold
[2]
Avg accuracy
[3]
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox {AUC}$$\end{document}AUC
[4]
Avg. precision
[1] array · 5 items
[0]
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox {AD}$$\end{document}AD vs control
[1]
2 fold
[2]
0.921
[3]
0.795
[4]
0.967
[2] array · 4 items
[0]
4 fold
[1]
0.929
[2]
0.783
[3]
1
[3] array · 4 items
[0]
5 fold
[1]
0.929
[2]
0.771
[3]
1
[4] array · 5 items
[0]
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox {DS}$$\end{document}DS vs control
[1]
2 fold
[2]
0.700
[3]
0.664
[4]
0.736
[5] array · 4 items
[0]
5 fold
[1]
0.705
[2]
0.676
[3]
0.783
[6] array · 4 items
[0]
8 fold
[1]
0.700
[2]
0.673
[3]
0.754
locator
section[16]/table-wrap[1]#Tab4
[9] object · 16 fields
id
asset:table:pmid:33335112:7
role
table
format
jats-table
media_type
application/vnd.jats.table+json
uri
null
sha256
sha256:6def5b6a57cba246a0888cb057d56c1032b6b9c94cabb706bd6569e0855459a4
bytes
1183
access
open
state
verified
reader
read_table
reader_status
available
identifier
section[17]/table-wrap[1]#Tab5
version
null
note
Top significant GO terms enriched with the Alzheimer’s disease specific genes.
structure object · 5 fields
pmcid
PMC7747741
section_index
17
section_title
Gene set enrichment analysis
row_count
7
column_count
3
table object · 8 fields
label
Table 5
caption
Top significant GO terms enriched with the Alzheimer’s disease specific genes.
rows array · 7 items
[0] array · 3 items
[0]
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox {Gsig}^{\text{a}}$$\end{document}Gsiga
[1]
GO \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox {term}^{\text{b}}$$\end{document}termb
[2]
Enrichment p value
[1] array · 3 items
[0]
CYSLTR2
[1]
GO-CC: GO:0016021 Integral component of membrane
[2]
0.031
[2] array · 3 items
[0]
LCE1F
[1]
GO-MF: GO:0005198 Structural molecule activity
[2]
0.011
[3] array · 2 items
[0]
GO-CC: GO:0001533 Cornified envelope
[1]
0.027
[4] array · 2 items
[0]
GO-BP: GO:0031424 Keratinization
[1]
0.031
[5] array · 2 items
[0]
GO-BP: GO:0016021 Integral component of membrane
[1]
0.031
[6] array · 2 items
[0]
GO-BP: GO:0018149 Peptide cross-linking
[1]
0.033
locator
section[17]/table-wrap[1]#Tab5
[10] object · 16 fields
id
asset:table:pmid:33335112:8
role
table
format
jats-table
media_type
application/vnd.jats.table+json
uri
null
sha256
sha256:821fe4c6a574b8616871095389a12c7ae9b6e399282aaacc21f9c36e4fdba4e1
bytes
7603
access
open
state
verified
reader
read_table
reader_status
available
identifier
section[17]/table-wrap[2]#Tab6
version
null
note
Top significant KEGG pathways and GO terms enriched with Down syndrome specific genes.
structure object · 5 fields
pmcid
PMC7747741
section_index
17
section_title
Gene set enrichment analysis
row_count
22
column_count
3
table object · 8 fields
label
Table 6
caption
Top significant KEGG pathways and GO terms enriched with Down syndrome specific genes.
rows array · 22 items
[0] array · 3 items
[0]
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox {Gsig}^{\text{a}}$$\end{document}Gsiga
[1]
KEGG pathway or GO \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox {term}^{\text{b}}$$\end{document}termb
[2]
Enrichment p value
[1] array · 3 items
[0]
ADH1B
[1]
KEGG pathway: hsa05204: Chemical carcinogenesis
[2]
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$3.24\times 10^{-3}$$\end{document}3.24×10-3
[2] array · 2 items
[0]
KEGG pathway: hsa00982: Drug metabolism-cytochrome P450
[1]
0.011
[3] array · 3 items
[0]
CALML6
[1]
KEGG pathway: hsa04740: Olfactory transduction
[2]
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$5.51\times 10^{-5}$$\end{document}5.51×10-5
[4] array · 3 items
[0]
CD33
[1]
GO-MF: GO:0004872 Receptor activity
[2]
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$2.21\times 10^{-5}$$\end{document}2.21×10-5
[5] array · 3 items
[0]
CLEC2B
[1]
GO-MF: GO:0030246 Carbohydrate binding
[2]
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$5.26\times 10^{-9}$$\end{document}5.26×10-9
[6] array · 3 items
[0]
CX3CR1
[1]
GO-BP: GO:0006935 Chemotaxis
[2]
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$8.73\times 10^{-6}$$\end{document}8.73×10-6
[7] array · 2 items
[0]
GO-BP: GO:0070098 Chemokine-mediated signaling pathway
[1]
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$2.60\times 10^{-5}$$\end{document}2.60×10-5
[8] array · 2 items
[0]
KEGG pathway: hsa04060: Cytokine–cytokine receptor interaction
[1]
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$7.49\times 10^{-4}$$\end{document}7.49×10-4
[9] array · 2 items
[0]
KEGG pathway: hsa04062: Chemokine signaling pathway
[1]
0.044
[10] array · 3 items
[0]
HAMP
[1]
GO-BP: GO:0006955 Immune response
[2]
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$3.25\times 10^{-10}$$\end{document}3.25×10-10
[11] array · 3 items
[0]
LCE3A
[1]
GO-BP: GO:0030216 Keratinocyte differentiation
[2]
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$9.70\times 10^{-4}$$\end{document}9.70×10-4
[12] array · 3 items
[0]
OR8G1
[1]
KEGG pathway: hsa04740: Olfactory transduction
[2]
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$5.51\times 10^{-5}$$\end{document}5.51×10-5
[13] array · 3 items
[0]
OR52H1
[1]
KEGG pathway: hsa04740: Olfactory transduction
[2]
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$5.51\times 10^{-5}$$\end{document}5.51×10-5
[14] array · 3 items
[0]
PGLYRP1
[1]
GO-BP: GO:0006955 Immune response
[2]
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$3.25\times 10^{-10}$$\end{document}3.25×10-10
[15] array · 2 items
[0]
GO-CC: GO:0005576 Extracellular region
[1]
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$2.40\times 10^{-9}$$\end{document}2.40×10-9
[16] array · 3 items
[0]
RARRES1
[1]
GO-CC: GO:0016021 Integral component of membrane
[2]
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$1.59\times 10^{-11}$$\end{document}1.59×10-11
[17] array · 3 items
[0]
TAS2R41
[1]
GO-BP: GO:0001580 Detection of chemical stimulus involved in sensory perception of bitter taste
[2]
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$2.52\times 10^{-5}$$\end{document}2.52×10-5
[18] array · 3 items
[0]
TINAG
[1]
GO-BP: GO:0006955 Immune response
[2]
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$3.25\times 10^{-10}$$\end{document}3.25×10-10
[19] array · 3 items
[0]
TINAGL1
[1]
GO-BP: GO:0006955 Immune response
[2]
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$3.25\times 10^{-10}$$\end{document}3.25×10-10
[20] array · 3 items
[0]
TNFRSF17
[1]
GO-MF: GO:0004872 Receptor activity
[2]
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$2.21\times 10^{-5}$$\end{document}2.21×10-5
[21] array · 2 items
[0]
KEGG pathway: hsa04060: Cytokine–cytokine receptor interaction
[1]
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$7.49\times 10^{-4}$$\end{document}7.49×10-4
locator
section[17]/table-wrap[2]#Tab6
gaps array · 4 items
[0]
asset:data:geo:GSE134379 not downloaded
[1]
asset:data:geo:GSE134379 licence not read
[2]
asset:data:geo:GSE74486 not downloaded
[3]
asset:data:geo:GSE74486 licence not read
analysis object · 4 fields
state
complete
methods array · 5 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
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count_matrix_with_condition
evidence object · 3 fields
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0.7
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Limma
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id
method-2
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machine_learning
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random_forest
label
Machine learning
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missing_operations array · 1 item
[0]
machine_learning_random_forest
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false
parameters object · 1 field
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t (RF) classifier using k-fold cross-validation (CV) (
data_shapes array · 1 item
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evidence object · 3 fields
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0.8
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Random Forest
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clustering
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Clustering
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[0]
kmeans_fit
missing_operations array · 0 items
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[0]
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evidence object · 3 fields
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0.8
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high
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k-means
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id
method-4
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clustering
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hierarchical
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Clustering
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[0]
hierarchical_cluster
missing_operations array · 0 items
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data_shapes array · 1 item
[0]
feature_matrix
evidence object · 3 fields
score
0.7
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medium
quote
hierarchical clustering
[4] object · 10 fields
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method-5
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clustering
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dbscan
label
Clustering
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[0]
kmeans_fit
[1]
hierarchical_cluster
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evidence object · 3 fields
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0.7
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medium
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DBSCAN
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capability object · 3 fields
state
complete
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[0] object · 6 fields
capability_id
capability:computational_analysis
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Computational analysis platform
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[0]
meta_analysis
[1]
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survival_analysis
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statistical_regression
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[6]
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candidate
Automated Assays Laravel application and simulated adapters
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readiness object · 8 fields
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complete
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true
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normalise_counts
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differential_expression_fit
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fdr_bh
missing_operations array · 0 items
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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
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capability:computational_analysis
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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
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GSE74486
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GSE134379
resolved_accessions array · 2 items
[0]
GSE74486
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GSE134379
statements array · 1 item
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We used a CpG methylation profile (GEO ID GSE74486) in the tissue of human FC neurons61 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE74486, Accessed date: November 25, 2018).
basis
The paper names GSE74486, GSE134379 in Gene Expression Omnibus (GEO) and the accession resolves in the repository. No dataset was downloaded and no licence was read.
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data:geo:GSE134379
gaps array · 0 items
review object · 5 fields
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partial
machine_critique object · 5 fields
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extraction_review
reader object · 5 fields
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MiniMax-M3.1-Flash
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minimax
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minimax
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2026-10-31T19:19:24Z
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single_reader
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null
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null
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1540
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extraction
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pmid:33335112
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paper
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pmid:33335112
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source:pmid:33335112
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manuscript
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PMID 33335112 full text
[2] object · 3 fields
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data:geo:GSE134379
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dataset
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GEO GSE134379
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[0] object · 4 fields
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activity-reading-sci-2026-11-01
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pmid:33335112
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GEO GSE134379
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agent:corpus-assay-engine
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agent:corpus-assay-engine
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agent:corpus-assay-engine
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agent:minimax-reader
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343fb30756ba6ec3
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Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:33335112", "kind": "science-record", "revision": 8, "content_hash": "sha256:15705d56800e12b0b758c35f4d660faa6604bf27d50b812b8d7bbad10c7b0b4e", "created_at": "2026-11-01T15:41:20Z", "updated_at": "2026-11-01T22:02:46Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Detecting methylation signatures in neurodegenerative disease by density-based clustering of applications with reducing noise.", "doi": "10.1038/s41598-020-78463-3", "pmid": "33335112", "authors": ["Saurav Mallik", "Zhongming Zhao"], "journal": "Scientific reports", "year": 2020, "abstract": "There have been numerous genetic and epigenetic datasets generated for the study of complex disease including neurodegenerative disease. However, analysis of such data often suffers from detecting the outliers of the samples, which subsequently affects the extraction of the true biological signals involved in the disease. To address this critical issue, we developed a novel framework for identifying methylation signatures using consecutive adaptation of a well-known outlier detection algorithm, density based clustering of applications with reducing noise (DBSCAN) followed by hierarchical clustering. We applied the framework to two representative neurodegenerative diseases, Alzheimer's disease (AD) and Down syndrome (DS), using DNA methylation datasets from public sources (Gene Expression Omnibus, GEO accession ID: GSE74486). We first applied DBSCAN algorithm to eliminate outliers, and then used Limma statistical method to determine differentially methylated genes. Next, hierarchical clustering technique was applied to detect gene modules. Our analysis identified a methylation signature comprising 21 genes for AD and a methylation signature comprising 89 genes for DS, respectively. Our evaluation indicated that these two signatures could lead to high classification accuracy values (92% and 70%) for these two diseases. In summary, this framework will be useful to better detect outlier-free genetic and epigenetic signatures in various complex diseases and their developmental stages.", "claims": [{"id": "claim-data-access", "statement": "We used a CpG methylation profile (GEO ID GSE74486) in the tissue of human FC neurons61 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE74486, Accessed date: November 25, 2018).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33335112", "locator": "sections[3] \"Data collection and preliminary filtering\" paragraph 2", "quote": "We used a CpG methylation profile (GEO ID GSE74486) in the tissue of human FC neurons61 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE74486, Accessed date: November 25, 2018)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": null, "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:33335112"], "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. 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"caption": "Top significant KEGG pathways and GO terms enriched with Down syndrome specific genes.", "rows": [["\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hbox {Gsig}^{\\text{a}}$$\\end{document}Gsiga", "KEGG pathway or GO \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hbox {term}^{\\text{b}}$$\\end{document}termb", "Enrichment p value"], ["ADH1B", "KEGG pathway: hsa05204: Chemical carcinogenesis", "\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$3.24\\times 10^{-3}$$\\end{document}3.24×10-3"], ["KEGG pathway: hsa00982: Drug metabolism-cytochrome P450", "0.011"], ["CALML6", "KEGG pathway: hsa04740: Olfactory transduction", "\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$5.51\\times 10^{-5}$$\\end{document}5.51×10-5"], ["CD33", "GO-MF: GO:0004872 Receptor activity", "\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$2.21\\times 10^{-5}$$\\end{document}2.21×10-5"], ["CLEC2B", "GO-MF: GO:0030246 Carbohydrate binding", "\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$5.26\\times 10^{-9}$$\\end{document}5.26×10-9"], ["CX3CR1", "GO-BP: GO:0006935 Chemotaxis", "\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$8.73\\times 10^{-6}$$\\end{document}8.73×10-6"], ["GO-BP: GO:0070098 Chemokine-mediated signaling pathway", "\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$2.60\\times 10^{-5}$$\\end{document}2.60×10-5"], ["KEGG pathway: hsa04060: Cytokine–cytokine receptor interaction", "\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$7.49\\times 10^{-4}$$\\end{document}7.49×10-4"], ["KEGG pathway: hsa04062: Chemokine signaling pathway", "0.044"], ["HAMP", "GO-BP: GO:0006955 Immune response", "\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$3.25\\times 10^{-10}$$\\end{document}3.25×10-10"], ["LCE3A", "GO-BP: GO:0030216 Keratinocyte differentiation", "\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$9.70\\times 10^{-4}$$\\end{document}9.70×10-4"], ["OR8G1", "KEGG pathway: hsa04740: Olfactory transduction", "\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$5.51\\times 10^{-5}$$\\end{document}5.51×10-5"], ["OR52H1", "KEGG pathway: hsa04740: Olfactory transduction", "\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$5.51\\times 10^{-5}$$\\end{document}5.51×10-5"], ["PGLYRP1", "GO-BP: GO:0006955 Immune response", "\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$3.25\\times 10^{-10}$$\\end{document}3.25×10-10"], ["GO-CC: GO:0005576 Extracellular region", "\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$2.40\\times 10^{-9}$$\\end{document}2.40×10-9"], ["RARRES1", "GO-CC: GO:0016021 Integral component of membrane", "\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$1.59\\times 10^{-11}$$\\end{document}1.59×10-11"], ["TAS2R41", "GO-BP: GO:0001580 Detection of chemical stimulus involved in sensory perception of bitter taste", "\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} 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\\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$9.70\\times 10^{-4}$$\\end{document}9.70×10-4"], ["OR8G1", "KEGG pathway: hsa04740: Olfactory transduction", "\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$5.51\\times 10^{-5}$$\\end{document}5.51×10-5"], ["OR52H1", "KEGG pathway: hsa04740: Olfactory transduction", "\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$5.51\\times 10^{-5}$$\\end{document}5.51×10-5"], ["PGLYRP1", "GO-BP: GO:0006955 Immune response", "\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} 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