BioMediTech Research Groups

Predictive Medicine and Data Analytics

Group Leader: Associate Professor Frank Emmert-Streib

frank.emmert-streib(at)tut.fi

About Us

The general research interests of the Predictive Medicine and Data Analytics (PMDA) Lab, are in the development and application of machine learning and statistics methods that can be used for the interrogation of high-dimensional data. Our underlying philosophy is data-driven assuming that the constituting components of a system are not independent from each other but interact. That means we are combining approaches from data science and network science in order to learn about the functioning of a system. Application areas we are working on are from biology, medicine, finance and social science.

For more information please visit www.bio-complexity.com

Research interests and expertise

Data science, computational biology, network science and digital society

Achievements

The research of the PMDA lab focuses around the development and the application of computational and statistical models for analyzing complex and large datasets. Guided by a deep understanding of statistical methods in combination with integrating network-based approaches, the group made a number of major contributions.

First, a key achievement of the group is the development of causal inference methods for gene regulatory networks and financial networks. Second, we developed new statistical hypothesis tests for comparing multivariate data, and applied these to gene expression profiles from biomedical and biological data. Third, our group designed computational approaches for the quantitative analysis including the comparison of networks. Fourth, we analyzed data from the social science and finance to gain a better understanding of digital society. Fifth, we developed software tools for the visualization of large-scale networks and the experimental design of RNA-seq experiments.

Infrastructure

Current group members:

  • Frank Emmert-Streib, PI
  • Shailesh Tripathi, postdoctoral research associate
  • Aliyu Musa, PhD student
  • Soumya Das, MSc student
  • Nader Hariri, MSc student
  • Han Feng, MSc student
  • Nannan Zou, MSc student
  • Facihul Azam, Research student
  • Avishek Barua, Research student

Collaboration offer and requests

We are interested to collaborate with groups from medicine, management, psychology and social science.

Major Publications

  1. Altay G, Emmert-Streib F. Inferring the conservative causal core of gene regulatory networks. BMC Systems Biology 4 (1), 132
  2. Emmert-Streib F, Dehmer M. Networks for systems biology: conceptual connection of data and function. IET systems biology 5 (3), 185-207
  3. Emmert-Streib F, Glazko GV, Altay G, de Matos Simoes R. Statistical inference and reverse engineering of gene regulatory networks from observational expression data. Frontiers in genetics 3
  4. de Matos Simoes R, Emmert-Streib F. Bagging statistical network inference from large-scale gene expression data. PLoS One 7 (3), e33624
  5. Tripathi S, Dehmer M, Emmert-Streib F. NetBioV: an R package for visualizing large network data in biology and medicine. Bioinformatics 30 (19), 2834-2836
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