Modelling biological sequences using infinite hidden Markov models
In this talk I will briefly visit several topics in sequence analysis and highlight how infinite hidden Markov models could be used to solve some of the key problems.
Dr Timo Lassmann
Telethon Kids Institute
Timo Lassmann, B.Sc. Biotechnology (1999), MSc Bioinformatics (2001), PhD functional genomics (2006), Karolinska Institute Sweden, has a major interest in computational biology and genomics. During his PhD he published a series of innovative algorithms to analyze biological sequences. In parallel, he led the data analysis in several collaborative studies involving laboratory groups. Finally he contributed to the PFAM consortium, a self-updating database of protein domain families.
In 2006 he moved to RIKEN, Japan, where he transitioned into leadership roles in the international Functional ANnotation of The
Mammalian Genome – FANTOM4 (3x Nature Genetics, 2009) and FANTOM5 project (3x Nature, 2014 – 2017, Science 2015). Between 2009 – 2012 he was a lead analyst in the ENCyclopedia Of DNA Elements (ENCODE) project (2x Nature, 2012).
In 2014, Timo Lassmann was appointed as the head of computational biology at the Telethon Kids Institute (TKI), Australia. The focus of his research lab is to re-purpose big “omics” data in translational projects to improve the well-being of children suffering from cancers and rare diseases. In 2017 he was appointed the leader of the Genetics and Rare Disease program at TKI, initiated a precision medicine project and was awarded the Feilman Fellowship in genomics.