Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
612-822-4611

  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Predicting the Lineage Choice of Hematopoietic Stem Cells: A Novel Approach Using Deep Neural Networks

Predicting the Lineage Choice of Hematopoietic Stem Cells: A Novel Approach Using Deep Neural Networks

Kroiss, Manuel

Paperback

Series: Bestmasters

Chemistry

ISBN10: 365812878X
ISBN13: 9783658128784
Publisher: Springer Spektrum
Published: May 20 2016
Pages: 68
Weight: 0.27
Height: 0.21 Width: 5.83 Depth: 8.27
Language: English
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.

Also in

Chemistry

View All