Dominik Heider

Research Area

The main focus of the research in the Heider Lab is set on the development of computational solutions from the field of Data Science for solving biomedical problems, e.g., machine learning algorithms for predicting drug resistance of pathogens or for modeling of diseases. In another main part of our research, we aim to develop new methods and algorithms for analyzing (meta-)genomic and (meta-)transcriptomic data of microorganisms, as well as genome assembly and functional annotation. Since NGS technologies have great potential in biomedical research but data processing is still limited by computational power, we further investigate techniques based on high-performance computing.

Expertise

machine learning
bioinformatics
clinical informatics
data science
predictive modeling
biomarker discovery
next-generation sequencing
pathogen resistance
omics
modeling

Recent Publications, Last 3 Years

1. Schwarz J, Heider D (2019) GUESS: Projecting Machine Learning Scores to Well-Calibrated Probability Estimates for Clinical Decision Making. Bioinformatics 35(14): 2458–2465.

2. Spänig S, Heider D (2019) Encodings and models for antimicrobial peptide classification for multi-resistant pathogens. BioData Mining 12:7.

3. Löchel HF, Eger D, Sperlea T, Heider D (2020) Deep Learning on Chaos Game Representation for Proteins. Bioinformatics 36(1): 272–279.

http://www.heiderlab.de