Machine Learning Methods for Gene Selection in Uveal Melanoma
Abstract
:1. Introduction
2. Results
2.1. Gene Prioritization Methods
2.1.1. Data Fusion
2.1.2. CNA Analysis Methods
2.1.3. Methylation Analysis Methods
2.2. Integration of Results
3. Discussion
4. Materials and Methods
4.1. jSVD Data Preparation and Analysis
4.2. CNAPE and IGC
4.3. MethylMix
4.4. Joint Singular Value Decomposition
4.5. Gene Signature Performance Evaluation
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Correction Statement
References
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Number of k | 2 | 3 | 4 |
---|---|---|---|
Connectivity score | 2.25 | 6.27 | 15.66 |
Silhouette score | 0.45 | 0.51 | 0.52 |
GENE | CNAPE | IGC Loss | Data Fusion | n Overlap | Cytoband | MGS Score |
---|---|---|---|---|---|---|
ROBO1 | 1 | 1 | 1 | 3 | 3p12.3 | −0.241 |
ROPN1 | 1 | 1 | 1 | 3 | 3q21.1 | 0.312 |
CADM1 | 1 | 0 | 1 | 2 | 11q23.3 | 0.233 |
ITPR2 | 1 | 0 | 1 | 2 | 12p12.1 | −0.323 |
ISM1 | 1 | 0 | 1 | 2 | 20p12.1 | 0.213 |
PDE4B | 1 | 0 | 1 | 2 | 1p31.3 | −0.291 |
ACSF2 | 1 | 0 | 1 | 2 | 17q21.33 | 0.302 |
BCHE | 0 | 1 | 1 | 2 | 3q26.1 | 0.274 |
CHL1 | 0 | 1 | 1 | 2 | 3p26.3 | −0.152 |
IL12RB2 | 0 | 0 | 1 | 1 | 1p31.3 | −0.225 |
MTUS1 | 0 | 0 | 1 | 1 | 8p22 | −0.276 |
CTF1 | 0 | 0 | 1 | 1 | 16p11.2 | −0.301 |
CPS1 | 0 | 0 | 1 | 1 | 2q34 | 0.177 |
HTR2B | 0 | 0 | 1 | 1 | 2q37.1 | 0.21 |
CARD11 | 0 | 0 | 1 | 1 | 7p22.2 | −0.254 |
TNFRSF19 | 0 | 0 | 1 | 1 | 13q12.12 | 0.125 |
PTGER4 | 0 | 0 | 1 | 1 | 5p13.1 | 0.12 |
Chr | CNAPE | IGC Gain | IGC Loss | Data Fusion | MethylMix |
---|---|---|---|---|---|
1p | 9 | 0 | 606 | 5 | 6 |
1q | 8 | 0 | 0 | 1 | 4 |
3p | 71 | 0 | 301 | 7 | 4 |
3q | 53 | 0 | 285 | 5 | 0 |
6p | 11 | 265 | 0 | 2 | 5 |
6q | 3 | 0 | 244 | 1 | 2 |
8p | 5 | 1 | 0 | 2 | 1 |
8q | 11 | 236 | 0 | 3 | 3 |
16p | 3 | 0 | 0 | 2 | 2 |
16q | 2 | 0 | 98 | 1 | 0 |
other | 123 | 0 | 0 | 48 | 63 |
total | 299 | 502 | 1534 | 77 | 90 |
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Reggiani, F.; El Rashed, Z.; Petito, M.; Pfeffer, M.; Morabito, A.; Tanda, E.T.; Spagnolo, F.; Croce, M.; Pfeffer, U.; Amaro, A. Machine Learning Methods for Gene Selection in Uveal Melanoma. Int. J. Mol. Sci. 2024, 25, 1796. https://doi.org/10.3390/ijms25031796
Reggiani F, El Rashed Z, Petito M, Pfeffer M, Morabito A, Tanda ET, Spagnolo F, Croce M, Pfeffer U, Amaro A. Machine Learning Methods for Gene Selection in Uveal Melanoma. International Journal of Molecular Sciences. 2024; 25(3):1796. https://doi.org/10.3390/ijms25031796
Chicago/Turabian StyleReggiani, Francesco, Zeinab El Rashed, Mariangela Petito, Max Pfeffer, Anna Morabito, Enrica Teresa Tanda, Francesco Spagnolo, Michela Croce, Ulrich Pfeffer, and Adriana Amaro. 2024. "Machine Learning Methods for Gene Selection in Uveal Melanoma" International Journal of Molecular Sciences 25, no. 3: 1796. https://doi.org/10.3390/ijms25031796