EMS Prediction for xQTL Modifier Scores

Contents

EMS Prediction for xQTL Modifier Scores#

This notebook demonstrates the prediction process for ensemble machine learning models used in xQTL modifier score prediction.

A SoS Jupyter notebook accepts the following types of cells:

  • Markdown cells that can be rendered to have title, table etc

  • Subkernel cells with statements in other languages with optional SoS magics at the beginning of the cells

  • SoS cells with SoS statements without any section header

  • Workflow cells with SoS statements and section header. These cells can only be executed by magic %run, %sosrun, or command sos run from command line

%preview -n rn[1:3]
rn <- rnorm(5000)
%preview rn[1:3]
> rn[1:3]:
  1. 1.73156050362224
  2. 0.578415051972805
  3. -1.55262655684374
%get rn --from R
sum(rn)/len(rn)

0.012104626083526911

# Placeholder for imports and setup
# This will be expanded with actual training code

import pandas as pd
import numpy as np
# Additional imports will be added here

Overview#

This notebook will include:

  • Loading pre-trained models

  • Data preprocessing for prediction

  • Feature engineering for new data

  • Model prediction and scoring

  • Results interpretation and visualization

Contents