Predicting the Lineage Choice of Hematopoietic Stem Cells

1. Front Matter

   Pages I-XV

   PDF

2. Introduction

   • Manuel Kroiss

   Pages 1-7

3. Introduction to deep neural networks

   • Manuel Kroiss

   Pages 9-29

4. Using RNNs to predict the lineage choice of stem cells

   • Manuel Kroiss

   Pages 31-53

5. Discussion

   • Manuel Kroiss

   Pages 55-60

6. Back Matter

   Pages 61-68

   PDF

Manuel Kroiss examines the differentiation of hematopoietic stem cells using machine learning methods. This work is based on experiments focusing on the lineage choice of CMPs, the progenitors of HSCs, which either become MEP or GMP cells. The author presents a novel approach to distinguish MEP from GMP cells using machine learning on morphology features extracted from bright field images. He tests the performance of different models and focuses on Recurrent Neural Networks with the latest advances from the field of deep learning. Two different improvements to recurrent networks were tested: Long Short Term Memory (LSTM) cells that are able to remember information over long periods of time, and dropout regularization to prevent overfitting. With his method, Manuel Kroiss considerably outperforms standard machine learning methods without time information like Random Forests and Support Vector Machines.

• hematopoietic stem cells
• machine learning
• deep neural networks
• recurrent Neural Networks
• predict lineage choice

• Neuherberg, Germany

  Manuel Kroiss

After finishing his MSc in Bioinformatics, Manuel Kroiss moved to London to work for a computer science company. In his work, the author is focusing on algorithmic problem solving while still remaining interested in applied machine learning.

Policies and ethics