The Role of Genetic Factors in Characterizing Extra-Intestinal Manifestations in Crohn’s Disease Patients: Are Bayesian Machine Learning Methods Improving Outcome Predictions?
Abstract
:1. Introduction
2. Material and Methods
2.1. BMLTs
2.2. Statistical Analysis
3. Results
4. Discussion
4.1. Study Limitations
4.2. Final Remarks
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Variable (n*) | EIM = No, n (%) N = 77 | EIM = Yes, n (%) N = 75 | Combined, n (%) N = 152 | |
---|---|---|---|---|
Onset (147) | Medical | 70 (90.9) | 63 (84.0) | 133 (87.5) |
Surgical | 7 (9.1) | 7 (9.3) | 14 (9.2) | |
Behavior (108) | B1 | 25 (32.5) | 25 (33.3) | 50 (32.9) |
B2 | 15 (19.5) | 20 (26.7) | 35 (23.0) | |
B3 | 9 (11.7) | 14 (18.7) | 23 (15.1) | |
Location (109) | L1 | 14 (18.2) | 14 (18.7) | 28 (18.4) |
L2 | 11 (14.3) | 14 (18.7) | 25 (16.4) | |
L3 | 21 (27.3) | 27 (36.0) | 48 (31.6) | |
L4 | 4 (5.2) | 4 (5.3) | 8 (5.3) | |
Age (146) | A1 | 53 (68.8) | 51 (68.0) | 104 (68.4) |
A2 | 21 (27.3) | 21 (28.0) | 42 (27.6) | |
Gender (152) | M | 46 (59.7) | 34 (45.3) | 80 (52.6) |
F | 31 (40.3) | 41 (54.7) | 72 (47.4) | |
Smoker (146) | No | 42 (54.5) | 36 (48.0) | 78 (51.3) |
Yes | 19 (24.7) | 26 (34.7) | 45 (29.6) | |
Ex | 11 (14.3) | 12 (16.0) | 23 (15.1) | |
Family History (139) | No | 58 (75.3) | 57 (76.0) | 115 (75.7) |
Yes | 11 (14.3) | 13 (17.3) | 24 (15.8) | |
NOD2:R702W (152) | RR | 63 (81.8) | 64 (85.3) | 127 (83.6) |
RW | 11 (14.3) | 9 (12.0) | 20 (13.2) | |
WW | 3 (3.9) | 2 (2.7) | 5 (3.3) | |
G908R (152) | GG | 73 (94.8) | 67 (89.3) | 140(92.1) |
GR | 4 (5.2) | 8 (10.7) | 12 (7.9) | |
L1007fs (152) | LL | 71 (92.2) | 65 (86.7) | 136 (89.5) |
L/insC | 5 (6.5) | 8 (10.7) | 13 (8.6) | |
insC/insC | 1 (1.3) | 2 (2.7) | 3 (2.0) | |
CD14 (152) | CC | 20 (26.0) | 20 (26.7) | 40 (26.3) |
TC | 39 (50.6) | 36 (48.0) | 75 (49.3) | |
TT | 18 (23.4) | 19 (25.3) | 37 (24.3) | |
TNF-308 (72) | GG | 35 (45.5) | 18 (24.0) | 53 (34.9) |
GA | 9 (11.7) | 4 (5.3) | 13 (8.6) | |
AA | 5 (6.5) | 1 (1.3) | 6 (3.9) | |
TNF -238 (72) | GG | 49 (63.6) | 23 (30.7) | 72 (47.4) |
IL12B (72) | AA | 17 (22.1) | 11 (14.7) | 28 (18.4) |
AC | 24 (31.2) | 10 (13.3) | 34 (22.4) | |
CC | 8 (10.4) | 2 (2.7) | 10 (6.6) | |
IL1RN (72) | ILRN*1 | 29 (37.7) | 12 (16.0) | 41 (27.0) |
ILRN*1/ILRN* | 15 (19.5) | 7 (9.3) | 22 (14.5) | |
ILRN*2 | 3 (3.9) | 3 (4.0) | 6 (3.9) | |
ILRN*1/ILRN* | 1(1.3) | 1 (1.3) | 2 (1.3) | |
ILRN*2/ILRN* | 1 (1.3) | 0 | 1 (0.7) |
Learning algorithm | MCR |
---|---|
Model without genetic variables | |
Grow-Shrink (GS) | 0.57 |
Incremental Association Markov-Blanket (IAMB) | 0.61 |
Fast Incremental Association Markov-Blanket (Fast-IAMB) | 0.61 |
Interleaved Incremental Association Markov-Blanket (Inter-IAMB) | 0.59 |
Hill-Climbing (HC) | 0.57 |
Tabu-Search (TS) | 0.61 |
Max-Min Hill-Climbing (MMHC) | 0.53 |
Restricted Maximization (RSMAX2) | 0.60 |
Max-Min Parents and Children (MMPC) | 0.55 |
Hiton Parents and Children (SI-HITON-PC) | 0.51 |
Chow‒Liu (CL) | 0.56 |
ARACNE | 0.58 |
Model with genetic variables | |
Grow-Shrink (GS) | 0.57 |
Incremental Association Markov-Blanket (IAMB) | 0.62 |
Fast Incremental Association Markov-Blanket (Fast-IAMB) | 0.61 |
Interleaved Incremental Association Markov-Blanket (Inter-IAMB) | 0.59 |
Hill-Climbing (HC) | 0.34 |
Tabu-Search (TS) | 0.34 |
Max-Min Hill-Climbing (MMHC) | 0.53 |
Restricted Maximization (RSMAX2) | 0.60 |
Max-Min Parents and Children (MMPC) | 0.56 |
Hiton Parents and Children (SI-HITON-PC) | 0.51 |
Chow‒Liu (CL) | 0.57 |
ARACNE | 0.58 |
MCR | Sensitivity | Specificity | PPV | NPV | AUC | Somer’s D | |
---|---|---|---|---|---|---|---|
Model without genetic variables | |||||||
LR | 0.46 | _ | _ | 0.77 | 0.52 | 0.72 | 0.45 |
GAM | 0.44 | _ | _ | 0.81 | 0.53 | 0.72 | 0.45 |
PPR | 0.36 | _ | _ | 0.98 | 0.58 | 0.82 | 0.64 |
LDA | 0.49 | _ | _ | 0.98 | 0.52 | 0.70 | 0.40 |
QDA | 0.49 | _ | _ | 0.72 | 0.52 | 0.67 | 0.34 |
ANN | 0.38 | _ | _ | 0.94 | 0.57 | 0.79 | 0.58 |
NB | 0.34 | 0.45 | 0.81 | 0.68 | 0.65 | 0.71 | 0.42 |
BN | 0.50 | 1.00 | 0.00 | 0.51 | 0.49 | 0.50 | 0.00 |
BART | 0.32 | 0.64 | 0.68 | 0.67 | 0.69 | 0.76 | 0.51 |
Model with genetic variables | |||||||
LR | 0.39 | _ | _ | 0.89 | 0.56 | 0.77 | 0.53 |
GAM | 0.37 | _ | _ | 0.90 | 0.57 | 0.77 | 0.54 |
PPR | 0.30 | _ | _ | 0.99 | 0.62 | 0.94 | 0.87 |
LDA | 0.38 | _ | _ | 0.99 | 0.57 | 0.77 | 0.53 |
QDA | 0.22 | _ | _ | 0.74 | 0.52 | 0.88 | 0.75 |
ANN | 0.33 | _ | _ | 0.92 | 0.60 | 0.87 | 0.73 |
NB | 0.33 | 0.65 | 0.69 | 0.69 | 0.66 | 0.75 | 0.51 |
BN | 0.34 | 0.64 | 0.69 | 0.68 | 0.65 | 0.67 | 0.33 |
BART | 0.32 | 0.66 | 0.69 | 0.67 | 0.69 | 0.78 | 0.56 |
ID | EIMs | IL12B | TNFA-308 | TNFA-238 | IL1RN | NB | BN | BART |
---|---|---|---|---|---|---|---|---|
63 | NO | AC | GG | GG | ILRN*1 | 0.06 | 0.36 | 0.26 |
64 | NO | AA | GG | GG | ILRN1/ILRN3 | 0.13 | 0.36 | 0.29 |
65 | NO | AC | GG | GG | ILRN*1 | 0.15 | 0.36 | 0.48 |
66 | NO | AA | GG | GG | ILRN*1 | 0.22 | 0.36 | 0.48 |
67 | NO | AC | GG | GG | ILRN*1 | 0.02 | 0.36 | 0.35 |
68 | NO | AC | GG | GG | ILRN*1 | 0.03 | 0.36 | 0.31 |
69 | NO | AA | GA | GG | ILRN1/ILRN2 | 0.09 | 0.36 | 0.32 |
70 | NO | AA | AA | GG | ILRN*1 | 0.04 | 0.36 | 0.37 |
71 | YES | AA | GA | GG | ILRN*1 | 0.02 | 0.36 | 0.28 |
72 | YES | AA | GG | GG | ILRN*2 | 0.28 | 0.36 | 0.36 |
73 | YES | Missing | Missing | Missing | Missing | 0.91 | 0.65 | 0.65 |
74 | NO | Missing | Missing | Missing | Missing | 0.69 | 0.65 | 0.61 |
75 | YES | Missing | Missing | Missing | Missing | 0.98 | 0.65 | 0.70 |
76 | YES | Missing | Missing | Missing | Missing | 0.95 | 0.65 | 0.65 |
77 | YES | Missing | Missing | Missing | Missing | 0.97 | 0.65 | 0.64 |
78 | YES | Missing | Missing | Missing | Missing | 0.95 | 0.65 | 0.68 |
79 | YES | Missing | Missing | Missing | Missing | 0.98 | 0.65 | 0.70 |
80 | YES | Missing | Missing | Missing | Missing | 0.88 | 0.65 | 0.68 |
81 | YES | Missing | Missing | Missing | Missing | 0.93 | 0.65 | 0.68 |
82 | YES | Missing | Missing | Missing | Missing | 0.98 | 0.65 | 0.75 |
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Bottigliengo, D.; Berchialla, P.; Lanera, C.; Azzolina, D.; Lorenzoni, G.; Martinato, M.; Giachino, D.; Baldi, I.; Gregori, D. The Role of Genetic Factors in Characterizing Extra-Intestinal Manifestations in Crohn’s Disease Patients: Are Bayesian Machine Learning Methods Improving Outcome Predictions? J. Clin. Med. 2019, 8, 865. https://doi.org/10.3390/jcm8060865
Bottigliengo D, Berchialla P, Lanera C, Azzolina D, Lorenzoni G, Martinato M, Giachino D, Baldi I, Gregori D. The Role of Genetic Factors in Characterizing Extra-Intestinal Manifestations in Crohn’s Disease Patients: Are Bayesian Machine Learning Methods Improving Outcome Predictions? Journal of Clinical Medicine. 2019; 8(6):865. https://doi.org/10.3390/jcm8060865
Chicago/Turabian StyleBottigliengo, Daniele, Paola Berchialla, Corrado Lanera, Danila Azzolina, Giulia Lorenzoni, Matteo Martinato, Daniela Giachino, Ileana Baldi, and Dario Gregori. 2019. "The Role of Genetic Factors in Characterizing Extra-Intestinal Manifestations in Crohn’s Disease Patients: Are Bayesian Machine Learning Methods Improving Outcome Predictions?" Journal of Clinical Medicine 8, no. 6: 865. https://doi.org/10.3390/jcm8060865