Classification models using circulating neutrophil transcripts can detect unruptured intracranial aneurysm

Poppenberg, Kerry E.; Tutino, Vincent M.; Li, Lu; Waqas, Muhammad; June, Armond; Chaves, Lee; Jiang, Kaiyu; Jarvis, James N.; Sun, Yijun; Snyder, Kenneth V.; Levy, Elad I.; Siddiqui, Adnan H.; Kolega, John; Meng, Hui

doi:10.1186/s12967-020-02550-2

Journal of Translational Medicine

Table 2 The 37 transcripts selected for classification model training

From: Classification models using circulating neutrophil transcripts can detect unruptured intracranial aneurysm

Gene	Gene ID	Accession #	Training Cohort			Testing Cohort
Gene	Gene ID	Accession #	Acc	Sen	Spe	Acc	Sen	Spe
AC011380.1	–	AC011380	0.56	0.64	0.51	0.58	0.69	0.50
C1QL1^†	10882	NM_006688.5	0.80	0.56	0.96	0.93	0.88	0.96
CCDC42B	387885	NM_001144872.2	0.59	0.05	0.96	0.65	0.19	0.96
CEP295NL^†	100653515	NM_001243541.1	0.71	0.59	0.80	0.80	0.56	0.96
CERS4^†	79603	NM_024552.3	0.79	0.72	0.84	0.85	0.88	0.83
CLP1	10978	NM_006831.3	0.57	0.00	0.98	0.58	0.00	0.96
DCUN1D1	54165	NM_020640.3	0.57	0.00	0.98	0.60	0.00	1.00
EIF4EBP3	8637	NM_003732.3	0.47	0.26	0.62	0.68	0.69	0.67
FLT1	2321	NM_002019.4	0.52	0.00	0.89	0.50	0.00	0.83
GBGT1^†	26301	NM_021996.6	0.71	0.74	0.69	0.70	0.88	0.58
GPR15^†	2838	NM_005290	0.79	0.79	0.78	0.93	1.00	0.88
GPR157	80045	NM_024980.5	0.57	0.00	0.98	0.60	0.00	1.00
GTF2B	2959	NM_001514.6	0.55	0.03	0.93	0.55	0.00	0.92
HBB	3043	NM_000518.5	0.79	1.00	0.64	0.58	0.94	0.33
HIST1H4E	8367	NM_003545.3	0.57	0.00	0.98	0.60	0.00	1.00
HIST2H2AB	317,72	NM_175065.2	0.57	0.00	0.98	0.60	0.00	1.00
ISY1	57461	NM_001199469.1	0.60	0.05	0.98	0.65	0.13	1.00
KIAA1324	57535	NM_020775.5	0.59	0.05	0.96	0.55	0.06	0.88
KIAA1614	57710	NM_020950.2	0.63	0.15	0.96	0.63	0.06	1.00
LOC100129697	100129697	NM_001290330.2	0.65	0.26	0.93	0.58	0.06	0.92
LOC105377284	105377284	XR_938891.2	0.59	0.05	0.96	0.65	0.13	1.00
LRRN3^†	54674	NM_001099658.2	0.78	0.79	0.76	0.85	1.00	0.75
MFSD6L	162387	NM_152599.3	0.67	0.59	0.73	0.65	0.50	0.75
MORC3	23515	NM_015358.3	0.57	0.00	0.98	0.60	0.00	1.00
MTRNR2L1	100462977	NM_001190452.1	0.57	0.03	0.96	0.60	0.00	1.00
NECAB1	64168	NM_022351.5	0.62	0.15	0.95	0.58	0.06	0.92
NEIL3	55247	NM_018248.3	0.57	0.00	0.98	0.60	0.00	1.00
PDCD10	11235	NM_007217.4	0.57	0.00	0.98	0.60	0.00	1.00
PGM5	5239	NM_021965.4	0.55	0.00	0.95	0.50	0.00	0.83
RFFL	117584	NR_037713.1	0.57	0.00	0.98	0.58	0.00	0.96
SDCBP2	27111	NM_080489.5	0.71	0.33	0.98	0.65	0.31	0.88
SMIM8	57150	NM_001042493.3	0.57	0.03	0.96	0.50	0.00	0.83
SYP	6855	NM_003179.2	0.59	0.05	0.96	0.63	0.13	0.96
TGS1^†	96764	NM_024831.7	0.80	0.79	0.80	0.78	0.94	0.67
TMC4	147798	NM_001145303.2	0.64	0.49	0.75	0.63	0.44	0.75
USF1	7391	NM_007122.5	0.59	0.03	0.98	0.60	0.06	0.96
UTY	7404	NM_182660.1	0.57	0.00	0.98	0.60	0.00	1.00

Acc accuracy, Sen sensitivity, Spe specificity
We show the per-transcript performance in the training and testing dataset. Transcripts with high accuracy (> 0.70) in both training and testing cohorts are denoted by ^†

Back to article page

ISSN: 1479-5876

Contact us

Submission enquiries: Access here and click Contact Us
General enquiries: info@biomedcentral.com