Name
Title | ||
|---|---|---|
Improving the prediction of cardiovascular risk with machine-learning and DNA methylation data |
Abstract | ||
|---|---|---|
Classically, the cardiovascular risk of individual is evaluated using phenomenological variables (PV)such as blood pressure, body mass, smoker status, gender, age etc. Here we show that, on prospective study (after 10-15 years)these PV display a poor agreement with case-control samples. We were able to obtain more accurate predictions using both DNA methylation data and PV as input features of a Random Forest model, achieving a ROC-AUC of 0.74. Furthermore, the Random Forest output correlates with the reliability of the predictions producing a ROC-AUC of 0.90 when only the most reliable predictions are taken into consideration. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/CIBCB.2019.8791483 | 2019 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) |
Keywords | DocType | ISBN |
Epigenetic biomarkers,DNA methylation,Genomics,Computational statistics | Conference | 978-1-7281-1463-7 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
10 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Giovanni Cugliari | 1 | 0 | 0.34 |
| Silvia Benevenuta | 2 | 0 | 1.01 |
| Simonetta Guarrera | 3 | 0 | 0.34 |
| Carlotta Sacerdote | 4 | 0 | 0.34 |
| Salvatore Panico | 5 | 0 | 0.34 |
| Vittorio Krogh | 6 | 2 | 0.89 |
| Rosario Tumino | 7 | 0 | 0.34 |
| Paolo Vineis | 8 | 18 | 1.41 |
| Piero Fariselli | 9 | 851 | 96.03 |
| Giuseppe Matullo | 10 | 0 | 0.34 |