Bioinformatics Seminar: Deciphering the Splicing Code via Neural Networks
The workshop is hosted by Maria Poptsova, associate professor at the big data and information retrieval department, head of the bioinformatics lab, Higher School of Economics.
Machine learning techniques are already widely used in genetics and genomics. They turned out to be most useful for interpreting large sets of genomic data and annotating a large number of genome elements. Machine learning techniques have been successfully used to detect transcription start, splice, alternative splicing, promoters, enhancers, and nucleosome positioning sites. Following a revolution in sequencing technologies, the accumulation of experimental data occurs faster than the construction of models explaining the functioning of the genome. It is necessary to develop new approaches, methods and algorithms, as well as to master the existing technologies for working with big data systems in genomics. At the seminar, we will learn about latest research on the application of machine learning techniques to the analysis of genomic data in Harvard, MIT, Cambridge, Sorbonne, EMBL-EBI and other leading universities.