Prof. Frank Emmert-Streib
Tampere University
The Predictive Society and Data Analytics Lab pursues
innovative research in data science. That means we are creatively
developing and applying high-dimensional network-based methods in
machine learning, statistics and artificial intelligence that can be
used for knowledge extraction from data.
Examples for data we are
analyzing are high-throughput data from genomic experiments. For
instance, the data can come from gene expression, methylation or RNA-seq
experiments, e.g., generated from next-generation sequencing (NGS)
technologies. In this context, we are mainly interested in biomedical
and health related questions of complex disorders and their relation to
basic biological mechanisms. We have experience working with data from
many cancer types, including, breast cancer, colon cancer, bladder
cancer, prostate cancer, kidney cancer and lymphoma but we are also
involved in studies of cystic fibrosis, asthma and diabetes. In
addition, we are working on text mining problems, e.g., from electronic
health records (EHR) of patients, for automizing clinical prediction
tasks. All of these studies are conducted in close collaboration with
biologists and clinicians.
Other types of data we are analyzing
come from the stock market, social media, social sciences, marketing,
online gaming or business and industrial processes. We are particularly
interested in studying ways to forecast or predict the system behavior,
like gene functioning, consumer behavior or human personality, and
utilize for this network-based approaches in combination with a
statistical framework. Examples for indistrial partners are Nokia (Finland), Cargotec (Finland), Elisa (Finland), and Hughes insurance (UK).
For a very brief overview and an introductory video of our research see here.
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