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pmid:28856086

Identification of novel targets for multiple myeloma through integrative approach with Monte Carlo cross-validation analysis.

Congjian Liu · Xiang Gu · Zhenxian Jiang

Journal of bone oncology · 2017 doi:10.1016/j.jbo.2017.08.001

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Experiment

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Data

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title
Identification of novel targets for multiple myeloma through integrative approach with Monte Carlo cross-validation analysis.
doi
10.1016/j.jbo.2017.08.001
pmid
28856086
authors array · 3 items
[0]
Congjian Liu
[1]
Xiang Gu
[2]
Zhenxian Jiang
journal
Journal of bone oncology
year
2017
abstract
More than one pathway is involved in disease development and progression, and two or more pathways may be interconnected to further affect the disease onset, as functional proteins participate in multiple pathways. Thus, identifying cross-talk among pathways is necessary to understand the molecular mechanisms of multiple myeloma (MM). Based on this, this paper looked at extracting potential pathway cross-talk in MM through an integrative approach using Monte Carlo cross-validation analysis. The gene expression library of MM (accession number: GSE6477) was downloaded from the Gene Expression Omnibus (GEO) database. The integrative approach was then used to identify potential pathway cross-talk, and included four steps: Firstly, differential expression analysis was conducted to identify differentially expressed genes (DEGs). Secondly, the DEGs obtained were mapped to the pathways downloaded from an ingenuity pathways analysis (IPA), to reveal the underlying relationship between the DEGs and pathways enriched by these DEGs. A subset of pathways enriched by the DEGs was then obtained. Thirdly, a discriminating score (DS) value for each paired pathway was computed. Lastly, random forest (RF) classification was used to identify the paired pathways based on area under the curve (AUC) and Monte Carlo cross-validation, which was repeated 50 times to explore the best paired pathways. These paired pathways were tested with another independently published MM microarray data (GSE85837), using in silico validation. Overall, 60 DEGs and 19 differential pathways enriched by DEGs were extracted. Each pathway was sorted based on their AUC values. The paired pathways, inhibition of matrix metalloproteases and EIF2 signaling pathway, indicated the best AUC value of 1.000. Paired pathways consisting of IL-8 and EIF2 signaling pathways with higher AUC of 0.975, were involved in 7 runs. Furthermore, it was validated consistently in separate microarray data sets (GSE85837). Paired pathways (inhibition of matrix metalloproteases and EIF2 signaling, IL-8 signaling and EIF2 signaling) exhibited the best AUC values and higher frequency of validation. Two paired pathways (inhibition of matrix metalloproteases and EIF2 signaling, IL-8 signaling and EIF2 signaling) were used to accurately classify MM and control samples. These paired pathways may be potential bio-signatures for diagnosis and management of MM.
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Thus, in this study, the Monte Carlo cross-validation method was employed to randomly select 60% of the original microarray data comprising of training data, and the remaining 40% was assigned to the testing data.
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Thus, in this study, the Monte Carlo cross-validation method was employed to randomly select 60% of the original microarray data comprising of training data, and the remaining 40% was assigned to the testing data.
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The gene expression library of MM (accession number: GSE6477) [14] was retrieved from Gene Expression Omnibus (GEO) database, based on the GPL96 platform of [HG-U133A]Affymetrix Human Genome U133A Array.
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sections[3] "Acquisition of gene expression profile" paragraph 2
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The gene expression library of MM (accession number: GSE6477) [14] was retrieved from Gene Expression Omnibus (GEO) database, based on the GPL96 platform of [HG-U133A]Affymetrix Human Genome U133A Array.
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In silico validation with independent MM microarray data
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Table 5
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Top 10 pairs of pathways with AUC value, validated using the other MM microarray data.
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In silico validation with independent MM microarray data
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Top 10 pairs of pathways based on occurrence number not less than 5, selected from the validated microarray data.
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List of differentially expressed genes (DEGs).
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CD320
[4]
2.241042
[5]
4.83E-04
[11] array · 6 items
[0]
CAMP
[1]
−3.0549
[2]
9.82E-07
[3]
CD163
[4]
−2.52643
[5]
5.17E-04
[12] array · 6 items
[0]
IGHG1
[1]
−3.7307
[2]
2.23E-06
[3]
LUM
[4]
−3.58203
[5]
5.20E-04
[13] array · 6 items
[0]
CTSH
[1]
−2.87845
[2]
3.55E-06
[3]
CXCL8
[4]
−2.40243
[5]
5.26E-04
[14] array · 6 items
[0]
AIF1
[1]
−2.16656
[2]
4.43E-06
[3]
NOD2
[4]
−2.95453
[5]
5.26E-04
[15] array · 6 items
[0]
LST1
[1]
−2.19262
[2]
8.38E-06
[3]
S100A8
[4]
−2.70373
[5]
5.61E-04
[16] array · 6 items
[0]
IGLJ3
[1]
−4.49176
[2]
1.13E-05
[3]
CXCL2
[4]
−2.71229
[5]
5.61E-04
[17] array · 6 items
[0]
CLPP
[1]
2.32109
[2]
1.65E-05
[3]
IGHD
[4]
−4.07084
[5]
5.84E-04
[18] array · 6 items
[0]
LTF
[1]
−3.89554
[2]
1.65E-05
[3]
HMOX1
[4]
−2.36868
[5]
5.99E-04
[19] array · 6 items
[0]
FABP4
[1]
−2.79511
[2]
3.18E-05
[3]
MS4A4A
[4]
−3.38126
[5]
6.11E-04
[20] array · 6 items
[0]
LYVE1
[1]
−2.9819
[2]
3.18E-05
[3]
HIF1A
[4]
−2.13129
[5]
6.18E-04
[21] array · 6 items
[0]
NRP1
[1]
−2.08155
[2]
3.34E-05
[3]
VNN2
[4]
−2.4071
[5]
6.18E-04
[22] array · 6 items
[0]
S100A12
[1]
−2.16333
[2]
3.34E-05
[3]
MAFB
[4]
−3.4547
[5]
6.41E-04
[23] array · 6 items
[0]
CEACAM8
[1]
−3.52708
[2]
4.20E-05
[3]
ALDH1A3
[4]
−2.01185
[5]
6.80E-04
[24] array · 6 items
[0]
IGLV1-44
[1]
−4.8402
[2]
6.64E-05
[3]
LHFP
[4]
−2.18934
[5]
6.98E-04
[25] array · 6 items
[0]
KCTD12
[1]
−2.56891
[2]
6.99E-05
[3]
S100A9
[4]
−2.3273
[5]
8.20E-04
[26] array · 6 items
[0]
CD14
[1]
−3.15551
[2]
7.82E-05
[3]
MNDA
[4]
−3.14832
[5]
9.14E-04
[27] array · 6 items
[0]
RPS11
[1]
2.079562
[2]
8.44E-05
[3]
HLA-DPA1
[4]
−2.64283
[5]
9.63E-04
[28] array · 6 items
[0]
IGK
[1]
−3.99784
[2]
9.70E-05
[3]
TGFBI
[4]
−2.93660
[5]
9.89E-04
[29] array · 6 items
[0]
P2RY13
[1]
−2.46684
[2]
1.05E-04
[3]
MAGEA4
[4]
2.361871
[5]
9.92E-04
[30] array · 6 items
[0]
DTX2
[1]
2.071491
[2]
1.05E-04
[3]
MROH7
[4]
−2.03615
[5]
9.95E-04
locator
section[10]/table-wrap[1]#t0005
[6] object · 16 fields
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asset:table:pmid:28856086:4
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jats-table
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section[10]/table-wrap[2]#t0010
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Pathways enriched by differentially expressed genes (DEGs).
structure object · 5 fields
pmcid
PMC5565744
section_index
10
section_title
Identification of DEGs and pathway enrichment analysis
row_count
20
column_count
4
table object · 8 fields
label
Table 2
caption
Pathways enriched by differentially expressed genes (DEGs).
rows array · 20 items
[0] array · 4 items
[0]
Pathway
[1]
FDR
[2]
Genes in pathway
[3]
Number of common genes
[1] array · 4 items
[0]
Granulocyte Adhesion and Diapedesis
[1]
6.09E-11
[2]
163
[3]
4
[2] array · 4 items
[0]
EIF2 Signaling
[1]
2.88E-10
[2]
173
[3]
6
[3] array · 4 items
[0]
Atherosclerosis Signaling
[1]
1.32E-07
[2]
119
[3]
1
[4] array · 4 items
[0]
Hepatic Fibrosis / Hepatic Stellate Cell Activation
[1]
1.66E-07
[2]
137
[3]
2
[5] array · 4 items
[0]
LXR/RXR Activation
[1]
1.78E-07
[2]
121
[3]
1
[6] array · 4 items
[0]
T Helper Cell Differentiation
[1]
2.91E-06
[2]
62
[3]
3
[7] array · 4 items
[0]
Bladder Cancer Signaling
[1]
5.83E-06
[2]
86
[3]
5
[8] array · 4 items
[0]
Role of Macrophages
[1]
2.23E-05
[2]
282
[3]
4
[9] array · 4 items
[0]
Complement System
[1]
4.96E-05
[2]
32
[3]
3
[10] array · 4 items
[0]
Altered T Cell and B Cell Signaling in Rheumatoid Arthritis
[1]
6.80E-05
[2]
76
[3]
2
[11] array · 4 items
[0]
IL-8 Signaling
[1]
6.96E-05
[2]
183
[3]
1
[12] array · 4 items
[0]
PI3K Signaling in B Lymphocytes
[1]
1.01E-04
[2]
122
[3]
3
[13] array · 4 items
[0]
Regulation of the Epithelial-Mesenchymal Transition Pathway
[1]
1.17E-04
[2]
175
[3]
1
[14] array · 4 items
[0]
Neuroprotective Role of THOP1 in Alzheimer's Disease
[1]
3.38E-04
[2]
40
[3]
1
[15] array · 4 items
[0]
Role of Osteoblasts
[1]
5.18E-04
[2]
214
[3]
2
[16] array · 4 items
[0]
Acute Myeloid Leukemia Signaling
[1]
5.48E-04
[2]
76
[3]
3
[17] array · 4 items
[0]
ILK Signaling
[1]
7.71E-04
[2]
181
[3]
2
[18] array · 4 items
[0]
Macropinocytosis Signaling
[1]
8.53E-04
[2]
68
[3]
2
[19] array · 4 items
[0]
LPS/IL-1 Mediated Inhibition of RXR Function
[1]
9.14E-04
[2]
210
[3]
2
locator
section[10]/table-wrap[2]#t0010
[7] object · 16 fields
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asset:table:pmid:28856086:5
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jats-table
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section[11]/table-wrap[1]#t0015
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Top 10 pairs of pathways with AUC value.
structure object · 5 fields
pmcid
PMC5565744
section_index
11
section_title
Selection of best paired pathways
row_count
21
column_count
2
table object · 8 fields
label
Table 3
caption
Top 10 pairs of pathways with AUC value.
rows array · 21 items
[0] array · 2 items
[0]
Pairs of pathways
[1]
AUC
[1] array · 2 items
[0]
(1a) Inhibition of matrix metalloproteases
[1]
1.000
[2] array · 1 item
[0]
(1b) EIF2 signaling
[3] array · 2 items
[0]
(2a) IL-8 signaling
[1]
0.975
[4] array · 1 item
[0]
(2b) EIF2 signaling
[5] array · 2 items
[0]
(3a) IL-8 signaling
[1]
0.939
[6] array · 1 item
[0]
(3b) Regulation of eIF4 and p70S6K signaling
[7] array · 2 items
[0]
(4a) IL-8 signaling
[1]
0.927
[8] array · 1 item
[0]
(4b) FGF signaling
[9] array · 2 items
[0]
(5a) PI3K signaling in B lymphocytes
[1]
0.910
[10] array · 1 item
[0]
(5b) EIF2 signaling
[11] array · 2 items
[0]
(6a) PI3K signaling in B lymphocytes
[1]
0.900
[12] array · 1 item
[0]
(6b) FGF signaling
[13] array · 2 items
[0]
(7a) Colorectal cancer metastasis signaling
[1]
0.898
[14] array · 1 item
[0]
(7b) EIF2 signaling
[15] array · 2 items
[0]
(8a) Altered T cell and B cell signaling in rheumatoid arthritis
[1]
0.897
[16] array · 1 item
[0]
(8b) EIF2 signaling
[17] array · 2 items
[0]
(9a) Inhibition of matrix metalloproteases
[1]
0.892
[18] array · 1 item
[0]
(9b) Regulation of eIF4 and p70S6K signaling
[19] array · 2 items
[0]
(10a) EIF2 signaling
[1]
0.890
[20] array · 1 item
[0]
(10b) TREM1 signaling
locator
section[11]/table-wrap[1]#t0015
[8] object · 16 fields
id
asset:table:pmid:28856086:6
role
table
format
jats-table
media_type
application/vnd.jats.table+json
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null
sha256
sha256:ca814939a4b09e9cb00266be6a51d23fc0daaa6439bd54341c3aea216d47a3a3
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971
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read_table
reader_status
available
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section[11]/table-wrap[2]#t0020
version
null
note
Top 10 pairs of pathways based on occurrence number not less than 5.
structure object · 5 fields
pmcid
PMC5565744
section_index
11
section_title
Selection of best paired pathways
row_count
13
column_count
2
table object · 8 fields
label
Table 4
caption
Top 10 pairs of pathways based on occurrence number not less than 5.
rows array · 13 items
[0] array · 2 items
[0]
Pathway pairs
[1]
Total occurrence number
[1] array · 2 items
[0]
PI3K Signaling in B Lymphocytes; EIF2 Signaling
[1]
27
[2] array · 2 items
[0]
Inhibition of Matrix Metalloproteases; EIF2 Signaling
[1]
10
[3] array · 2 items
[0]
PI3K Signaling in B Lymphocytes; Regulation of eIF4 and p70S6K Signaling
[1]
10
[4] array · 2 items
[0]
Colorectal Cancer Metastasis Signaling; EIF2 Signaling
[1]
8
[5] array · 2 items
[0]
IL-8 Signaling; EIF2 Signaling
[1]
7
[6] array · 2 items
[0]
PI3K Signaling in B Lymphocytes; GABA Receptor Signaling
[1]
7
[7] array · 2 items
[0]
Role of NFAT in Regulation of the Immune Response; EIF2 Signaling
[1]
6
[8] array · 2 items
[0]
EIF2 Signaling; IL-17A Signaling in Fibroblasts
[1]
6
[9] array · 2 items
[0]
Granulocyte Adhesion and Diapedesis; EIF2 Signaling
[1]
6
[10] array · 2 items
[0]
Leukocyte Extravasation Signaling; EIF2 Signaling
[1]
5
[11] array · 2 items
[0]
EIF2 Signaling; IL-10 Signaling
[1]
5
[12] array · 2 items
[0]
MSP-RON Signaling Pathway; EIF2 Signaling
[1]
5
locator
section[11]/table-wrap[2]#t0020
[9] object · 16 fields
id
asset:table:pmid:28856086:7
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table
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jats-table
media_type
application/vnd.jats.table+json
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null
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sha256:73df46ae240d3dd80e3a1f2459c5e3d252d46b79e9384b0249df0790ee6a776c
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read_table
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identifier
section[12]/table-wrap[1]#t0025
version
null
note
Top 10 pairs of pathways with AUC value, validated using the other MM microarray data.
structure object · 5 fields
pmcid
PMC5565744
section_index
12
section_title
In silico validation with independent MM microarray data
row_count
21
column_count
2
table object · 8 fields
label
Table 5
caption
Top 10 pairs of pathways with AUC value, validated using the other MM microarray data.
rows array · 21 items
[0] array · 2 items
[0]
Pairs of pathways
[1]
AUC
[1] array · 2 items
[0]
(1a) Inhibition of matrix metalloproteases
[1]
0.971
[2] array · 1 item
[0]
(1b) EIF2 signaling
[3] array · 2 items
[0]
(2a) TREM1 signaling
[1]
0.968
[4] array · 2 items
[0]
(2b) EIF2 signaling
[1]
[5] array · 2 items
[0]
(3a) EIF2 signaling
[1]
0.961
[6] array · 2 items
[0]
(3b) IL-8 signaling
[1]
[7] array · 2 items
[0]
(4a) IL-10 signaling
[1]
0.956
[8] array · 1 item
[0]
(4b) EIF2 signaling
[9] array · 2 items
[0]
(5a) Antigen Presentation Pathway
[1]
0.955
[10] array · 1 item
[0]
(5b) LPS/IL-1 Mediated Inhibition of RXR Function
[11] array · 2 items
[0]
(6a) Caveolar-mediated Endocytosis Signaling
[1]
0.955
[12] array · 1 item
[0]
(6b) Agranulocyte Adhesion and Diapedesis
[13] array · 2 items
[0]
(7a) Regulation of eIF4 and p70S6K Signaling
[1]
0.953
[14] array · 1 item
[0]
(7b) Actin Nucleation by ARP-WASP Complex
[15] array · 2 items
[0]
(8a) Antigen Presentation Pathway
[1]
0.947
[16] array · 1 item
[0]
(8b) IL-6 Signaling
[17] array · 2 items
[0]
(9a) PI3K Signaling in B Lymphocytes
[1]
0.945
[18] array · 1 item
[0]
(9b) GABA Receptor Signaling
[19] array · 2 items
[0]
(10a) EIF2 Signaling
[1]
0.944
[20] array · 2 items
[0]
(10b) Actin Cytoskeleton Signaling
[1]
locator
section[12]/table-wrap[1]#t0025
[10] object · 16 fields
id
asset:table:pmid:28856086:8
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table
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jats-table
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application/vnd.jats.table+json
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sha256:7b3a8778e66a55d890f23a4cbd4cf97ffef1426445a8f754f3a5b684de8d6cc7
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section[12]/table-wrap[2]#t0030
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Top 10 pairs of pathways based on occurrence number not less than 5, selected from the validated microarray data.
structure object · 5 fields
pmcid
PMC5565744
section_index
12
section_title
In silico validation with independent MM microarray data
row_count
11
column_count
2
table object · 8 fields
label
Table 6
caption
Top 10 pairs of pathways based on occurrence number not less than 5, selected from the validated microarray data.
rows array · 11 items
[0] array · 2 items
[0]
Pathway pairs
[1]
Total occurrence number
[1] array · 2 items
[0]
Caveolar-mediated Endocytosis Signaling; Agranulocyte Adhesion and Diapedesis
[1]
45
[2] array · 2 items
[0]
EIF2 Signaling; Inhibition of Matrix Metalloproteases
[1]
43
[3] array · 2 items
[0]
Antigen Presentation Pathway; IL-6 Signaling
[1]
37
[4] array · 2 items
[0]
EIF2 Signaling; IL-8 Signaling
[1]
31
[5] array · 2 items
[0]
EIF2 Signaling; Regulation of eIF4 and p70S6K Signaling
[1]
31
[6] array · 2 items
[0]
Antigen Presentation Pathway; LPS/IL-1 Mediated Inhibition of RXR Function
[1]
21
[7] array · 2 items
[0]
EIF2 Signaling; IL-17A Signaling in Fibroblasts
[1]
11
[8] array · 2 items
[0]
EIF2 Signaling; IL-10 Signaling
[1]
9
[9] array · 2 items
[0]
Regulation of eIF4 and p70S6K Signaling; Actin Nucleation by ARP-WASP Complex
[1]
9
[10] array · 2 items
[0]
PI3K Signaling in B Lymphocytes; GABA Receptor Signaling
[1]
5
locator
section[12]/table-wrap[2]#t0030
gaps array · 4 items
[0]
asset:data:geo:GSE6477 not downloaded
[1]
asset:data:geo:GSE6477 licence not read
[2]
asset:data:geo:GSE85837 not downloaded
[3]
asset:data:geo:GSE85837 licence not read
analysis object · 4 fields
state
partial
methods array · 2 items
[0] object · 10 fields
id
method-1
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 · 1 field
auc
aired pathways based on area under the curve (AUC) and Monte Carlo c
data_shapes array · 1 item
[0]
labelled_feature_matrix
evidence object · 3 fields
score
0.7
band
medium
quote
random forest
[1] object · 10 fields
id
method-2
family
null
specific
null
label
Monte Carlo cross-validation
dsl_operations array · 0 items
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 0 items
evidence object · 3 fields
score
1
band
verbatim
quote
Thus, in this study, the Monte Carlo cross-validation method was employed to randomly select 60% of the original microarray data comprising of training data, and the remaining 40% was assigned to the testing data.
targets array · 0 items
gaps array · 2 items
[0]
method.reader_named_unmapped
[1]
method.machine_learning_random_forest
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
false
required_operations array · 0 items
missing_operations array · 1 item
[0]
machine_learning_random_forest
basis
The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: random_forest.
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 · 2 items
[0]
GSE6477
[1]
GSE85837
resolved_accessions array · 2 items
[0]
GSE6477
[1]
GSE85837
statements array · 1 item
[0]
The gene expression library of MM (accession number: GSE6477) [14] was retrieved from Gene Expression Omnibus (GEO) database, based on the GPL96 platform of [HG-U133A]Affymetrix Human Genome U133A Array.
basis
The paper names GSE6477, GSE85837 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 · 0 items
assets array · 10 items
[0]
source:pmid:28856086
[1]
data:geo:GSE6477
[2]
asset:table:pmid:28856086:1
[3]
asset:table:pmid:28856086:2
[4]
asset:table:pmid:28856086:3
[5]
asset:table:pmid:28856086:4
[6]
asset:table:pmid:28856086:5
[7]
asset:table:pmid:28856086:6
[8]
asset:table:pmid:28856086:7
[9]
asset:table:pmid:28856086:8
steps array · 0 items
must_build object · 5 fields
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[0] object · 3 fields
operation
machine_learning_random_forest
priority
null
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readers array · 0 items
capabilities array · 0 items
data_gaps array · 1 item
[0]
data:geo:GSE6477
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
null
independence
same_provider
evidence_check object · 2 fields
method
verbatim
availability
null
calls
0
usage object · 0 fields
stages array · 1 item
[0] object · 4 fields
id
extraction
state
complete
reader
MiniMax-M3.1-Flash
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minimax
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[0]
review.independent_reader
[1]
review.machine_critique
[2]
review.human_critique
execution object · 5 fields
state
absent
status
not_run
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null
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gaps array · 1 item
[0]
execution.not_run
provenance object · 7 fields
entities array · 3 items
[0] object · 3 fields
id
pmid:28856086
type
paper
label
pmid:28856086
[1] object · 3 fields
id
source:pmid:28856086
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manuscript
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PMID 28856086 full text
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PMID 28856086 full text
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sted with another independently published MM microarray data (GSE85837), using in silico validation.
[1]
e, it was validated consistently in separate microarray data sets (GSE85837). Paired pathways (inhib
[2]
sted with another independently published MM microarray data (GSE85837) using in silico validation.
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Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:28856086", "kind": "science-record", "revision": 8, "content_hash": "sha256:2452881e87eff2cc2d2f5c266c846e50d07eac2b5e6e5e205e8e39df548fc99f", "created_at": "2026-11-01T15:40:28Z", "updated_at": "2026-11-01T21:59:29Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of novel targets for multiple myeloma through integrative approach with Monte Carlo cross-validation analysis.", "doi": "10.1016/j.jbo.2017.08.001", "pmid": "28856086", "authors": ["Congjian Liu", "Xiang Gu", "Zhenxian Jiang"], "journal": "Journal of bone oncology", "year": 2017, "abstract": "More than one pathway is involved in disease development and progression, and two or more pathways may be interconnected to further affect the disease onset, as functional proteins participate in multiple pathways. Thus, identifying cross-talk among pathways is necessary to understand the molecular mechanisms of multiple myeloma (MM). Based on this, this paper looked at extracting potential pathway cross-talk in MM through an integrative approach using Monte Carlo cross-validation analysis. The gene expression library of MM (accession number: GSE6477) was downloaded from the Gene Expression Omnibus (GEO) database. The integrative approach was then used to identify potential pathway cross-talk, and included four steps: Firstly, differential expression analysis was conducted to identify differentially expressed genes (DEGs). Secondly, the DEGs obtained were mapped to the pathways downloaded from an ingenuity pathways analysis (IPA), to reveal the underlying relationship between the DEGs and pathways enriched by these DEGs. A subset of pathways enriched by the DEGs was then obtained. Thirdly, a discriminating score (DS) value for each paired pathway was computed. Lastly, random forest (RF) classification was used to identify the paired pathways based on area under the curve (AUC) and Monte Carlo cross-validation, which was repeated 50 times to explore the best paired pathways. These paired pathways were tested with another independently published MM microarray data (GSE85837), using in silico validation. Overall, 60 DEGs and 19 differential pathways enriched by DEGs were extracted. Each pathway was sorted based on their AUC values. The paired pathways, inhibition of matrix metalloproteases and EIF2 signaling pathway, indicated the best AUC value of 1.000. Paired pathways consisting of IL-8 and EIF2 signaling pathways with higher AUC of 0.975, were involved in 7 runs. Furthermore, it was validated consistently in separate microarray data sets (GSE85837). Paired pathways (inhibition of matrix metalloproteases and EIF2 signaling, IL-8 signaling and EIF2 signaling) exhibited the best AUC values and higher frequency of validation. Two paired pathways (inhibition of matrix metalloproteases and EIF2 signaling, IL-8 signaling and EIF2 signaling) were used to accurately classify MM and control samples. These paired pathways may be potential bio-signatures for diagnosis and management of MM.", "claims": [{"id": "claim-method", "statement": "Thus, in this study, the Monte Carlo cross-validation method was employed to randomly select 60% of the original microarray data comprising of training data, and the remaining 40% was assigned to the testing data.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:28856086", "locator": "sections[7] \"Extracting the best paired pathways\" paragraph 3", "quote": "Thus, in this study, the Monte Carlo cross-validation method was employed to randomly select 60% of the original microarray data comprising of training data, and the remaining 40% was assigned to the testing data."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The gene expression library of MM (accession number: GSE6477) [14] was retrieved from Gene Expression Omnibus (GEO) database, based on the GPL96 platform of [HG-U133A]Affymetrix Human Genome U133A Array.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:28856086", "locator": "sections[3] \"Acquisition of gene expression profile\" paragraph 2", "quote": "The gene expression library of MM (accession number: GSE6477) [14] was retrieved from Gene Expression Omnibus (GEO) database, based on the GPL96 platform of [HG-U133A]Affymetrix Human Genome U133A Array."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "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:28856086"], "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:GSE6477"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "blocked", "blocked_on": "method:machine_learning_random_forest", "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {"auc": "aired pathways based on area under the curve (AUC) and Monte Carlo c"}, "inputs": ["data:geo:GSE6477"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:run_analysis blocked on method:machine_learning_random_forest"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:28856086", 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"reader_status": "available", "identifier": "section[12]/table-wrap[1]#t0025", "version": null, "note": "Top 10 pairs of pathways with AUC value, validated using the other MM microarray data.", "structure": {"pmcid": "PMC5565744", "section_index": 12, "section_title": "In silico validation with independent MM microarray data", "row_count": 21, "column_count": 2}, "table": {"label": "Table 5", "caption": "Top 10 pairs of pathways with AUC value, validated using the other MM microarray data.", "rows": [["Pairs of pathways", "AUC"], ["(1a) Inhibition of matrix metalloproteases", "0.971"], ["(1b) EIF2 signaling"], ["(2a) TREM1 signaling", "0.968"], ["(2b) EIF2 signaling", ""], ["(3a) EIF2 signaling", "0.961"], ["(3b) IL-8 signaling", ""], ["(4a) IL-10 signaling", "0.956"], ["(4b) EIF2 signaling"], ["(5a) Antigen Presentation Pathway", "0.955"], ["(5b) LPS/IL-1 Mediated Inhibition of RXR Function"], ["(6a) Caveolar-mediated Endocytosis Signaling", "0.955"], ["(6b) Agranulocyte Adhesion and 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