Machine learning approach for multi-scale genomics problems
Olga Mineeva (Ph.D. Student)
In the age of rapid growth of available biological sequencing data enabled by the recent advances in sequencing technologies, there is an opportunity to answer biological and health-related questions at a more detailed level. At the same time, the amount of data allows the use of sophisticated methods, such that deep neural networks. The main goal of this research is to design a deep machine learning framework that addresses the challenges in genomics and metagenomics sequencing data. The work focuses on two topics: splicing of eukaryotic RNAs and evaluating the quality of metagenomic assemblies. An expected outcome is that life science researchers and other communities will get access to the reliable methods and tools which will help them to analyse sequencing data.
|Primary Host:||Gunnar Rätsch (ETH Zürich)|
|Exchange Host:||Isabel Valera (Max Planck Institute for Intelligent Systems)|
|PhD Duration:||01 November 2018 - 31 October 2022|
|Exchange Duration:||01 January 2020 - 30 June 2020|