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Table 1 Classification outcomes (prediction and probabilities) for WGS data using the final RF model. The predicted class is determined based on a majority vote from the individual decision trees of the random forest classifier, while the presented probabilities depict the corresponding percentage of decision trees voting toward a functional class

From: A novel machine learning-based approach for the computational functional assessment of pharmacogenomic variants

Location (GRCh38)

Allele

Existing variation

SYMBOL

HGVSc

GnomAD AF (%)

Predicted class

Probability of attributed class

1:97515839-97515839

C

rs1801159, CM033371, COSV64593269

DPYD

ENST00000370192.8:c.1627A>G

18.49%

Normal

0.96

12:21176804-21176804

G

rs2306283, CM043776, COSV57012766

SLCO1B1

ENST00000256958.3:c.388A>G

53.33%

Normal

0.66

12:21178615-21178615

C

rs4149056, CM043777, COSV57010105

SLCO1B1

ENST00000256958.3:c.521T>C

11.95%

Decreased

0.88

19:38492540-38492540

T

rs35364374

RYR1

ENST00000359596.8:c.6178G>T

4.95%

Increased

0.38

19:38499641-38499641

A

rs762454967, CM140865

RYR1

ENST00000359596.8:c.7034G>A

0.00%

Increased

0.80

19:41006936-41006936

T

rs3745274, CM130453, CS080663, COSV57843253

CYP2B6

ENST00000324071.10:c.516G>T

28.44%

Decreased

0.72