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The expertise cluster termed here AI and Data Science is based on formal sciences and is mostly computer science-oriented. It focuses on research into and development of sophisticated methods for data analysis and bioinformatics, such as machine learning and other leading-edge techniques of artificial intelligence (AI). The cluster excludes the hardware basis for digital health information processing, which falls into our health and wellbeing devices expertise.

The subarea is based in Aalto on combining the world-class research activities in ICT, health data science, and bioinformatics by Aalto with the biomedical and further expertise of our region’s medical school at the University of Helsinki (UH), the UH’s biological faculties that complement our own, and the research of partners like the Helsinki and Uusimaa Hospital District (HUS). By this combination, we are further developing a leading European health data research cluster.  

The AI and Data Science expertise cluster comprises the following themes

In the biological data science theme, we develop advanced analytics algorithms for big data arising in molecular biology and medicine. We particularly focus in developing models for realistic cases where the data may consist, of multiple views instead of tabular format and consist of structured objects. 

In the bioinformatics and computational systems biology theme, we develop computational tools to process data sets generated in biomedical and molecular-biology research by state-of-the-art high-throughput measurement technologies such as next-generation sequencing technologies. These technologies enable quantitative analysis of both large-scale (e.g. genome-wide) and smaller-scale (targeted approaches) biological systems. We collaborate with biological and clinical research groups on a number of applications. 

In the data-driven healthcare theme, we help tackle the issue of rising healthcare costs, which in developed countries are on the rise due to trends such as aging population and sedentary lifestyles. Advanced analysis of data is perceived as one of the main avenues for curbing the growth of these costs. In this theme, we predominantly employ data sets acquired from the clinical setting of hospitals. We analyze genomic data, imaging data, laboratory data, online-monitoring data, and data from biomedical research on selected diseases.

Via these various approaches, Aalto has considerable success in developing methods for, for instance, biomolecular data, including genome-wide association studies, drug bioactivity and interactions, metabolite identification, and personalized medicine, and in dynamic modeling of biological systems, genome-wide NGS data processing, and molecular-level biomarker discovery.

Furthermore, Aalto develops signal processing and machine learning technologies to automatically detect diseases from humans’ speech. This methodology is a potential preventive healthcare technology to detect diseases such as Parkinson’s at an early stage and to track physiological changes caused by the condition. These technologies are able to assess state of health automatically from recordings conduced in real-life conditions outside hospitals.