``` r
# Combine all sub-tibbles into a single data frame with the parent list name as an ID
top_predictors_df <- imap_dfr(top_predictors, ~ {
  .x
})

# View the resulting data frame
print(top_predictors_df)
```

```
## # A tibble: 170 × 4
##    Predictor    MeanImportance Type   Feature   
##    <chr>                 <dbl> <chr>  <chr>     
##  1 lncRNA_19090         0.0942 lncRNA FUN_001160
##  2 lncRNA_19080         0.0495 lncRNA FUN_001160
##  3 lncRNA_19455         0.0412 lncRNA FUN_001160
##  4 lncRNA_8841          0.0388 lncRNA FUN_001160
##  5 lncRNA_20578         0.0371 lncRNA FUN_001160
##  6 lncRNA_18679         0.0706 lncRNA FUN_001326
##  7 lncRNA_19528         0.0573 lncRNA FUN_001326
##  8 lncRNA_2205          0.0422 lncRNA FUN_001326
##  9 lncRNA_18344         0.0360 lncRNA FUN_001326
## 10 lncRNA_13204         0.0280 lncRNA FUN_001326
## # ℹ 160 more rows
```

``` r
# Optional: save to CSV
write.csv(top_predictors_df, "../output/22.3-Apul-multiomic-machine-learning-byTP/top_predictors.csv", row.names = FALSE)
```