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- DescriptionManuel 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 vel 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.
- Author BiographyAfter 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.
- Author(s)Manuel Kroiss
- PublisherSpringer Fachmedien Wiesbaden
- Date of Publication13/05/2016
- SubjectScience & Mathematics: Textbooks & Study Guides
- Series TitleBestMasters
- Place of PublicationWeisbaden
- Country of PublicationGermany
- ImprintSpringer Spektrum
- Content Note6 black & white tables, biography
- Weight126 g
- Width148 mm
- Height210 mm
- Spine5 mm
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