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ebarnell edited this page Mar 27, 2018 · 21 revisions

Deep Learning to Refine Somatic Variants in Cancer

Welcome to the Machine Learning Approach for Somatic Variant Refinement! Use this page to navigate your way through our development and analysis of machine learning models that automate the variant refinement process. Each page has a link at the bottom to bring you back to this table of contents.

Table of Contents

  1. Chapter 1 - Background Information
    1. Authors
    2. Citation
    3. About
    4. Installation
  2. Chapter 2 - Identification of Somatic Variants in Sequencing Data
    1. Automated Somatic Variant Calling
    2. Somatic Variant Refinement (SVR)
  3. Chapter 3 - Methods and Analysis for Machine Learning Models
    1. Data Assembly
    2. Logistic Regression Model
    3. Random Forest Model
    4. Deep Learning Model
    5. Model Evaluation
    6. Inter-reviewer Variability
    7. Orthogonal Validation
    8. Manual Review Validation
    9. Clinical Analysis
    10. Re-review Analysis
  4. Chapter 4 - Using DeepSVR (Deep Learning Model)
    1. Create the Classifier
    2. Prepare Your Data
    3. Classify Your Data

Table of Contents

Chapter 1 - Background Information:
Authors | Citation | About | Repository Installation

Chapter 2 - Identification of Somatic Variants in Sequencing Data:
Automated Somatic Variant Calling | Somatic Variant Refinement (SVR)

Chapter 3 - Methods and Analysis for Machine Learning Models:
Data Assembly | Logistic Regression Model | Random Forest Model | Deep Learning Model | Model Evaluation | Inter-reviewer Variability | Orthogonal Validation | Manual Review Validation | Re-review Analysis

Chapter 4 - DeepSVR Tutorial:
Tutorial Preface | DeepSVR Installation | Create the Classifier | Prepare Data | Classify Data | Re-Train Model

Chapter 5 - Usage Documents:
DeepSVR Installation | Usage Documents

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