Dr. Felix Heinrich

Research Fields and Interests

Machine Learning and Deep Learning
Information Theory
Application of these methods and theories in animal and plant breeding

Current Research Project

Within the framework of the joint project GlasSchwein (Förderkennzeichen: 28DI107B23; 11/2024 - 11/2027), funded by the Federal Office for Agriculture and Food (BLE), I analyze heterogeneous data collected along the pork value chain. My research focuses on the development of statistical models and machine learning algorithms to accurately predict carcass traits and meat quality of individual animals at the earliest possible stage of the value chain.

Current Teaching

Winter term

Applied Machine Learning in Agriculture with R (MSc)

Curriculum Vitae

Scientific qualification

Period	Degree and Institution
2018 - 2022	Ph.D. (Dr. rer. nat.) in Agricultural Sciences, University of Göttingen
2016 - 2018	Master of Science in Applied Computer Science, University of Göttingen
2013 - 2016	Bachelor of Science in Applied Computer Science, University of Göttingen

Professional Experience

Period	Position and Institution
Oct 2018 - Present	Research Associate, Breeding Informatics group, University of Göttingen
Apr 2017 - Sep 2018	Student Research Assistant, Breeding Informatics group, University of Göttingen
Summer term 2016 - Summer term 2018	Student Assistant, Institute of Computer Science, University of Göttingen
Jun 2015 - Jun 2018	Student Research Assistant, Institute of Bioinformatics, University Medical Center Göttingen

Selected Publications

Authors	Titel	Journal
Heinrich, F., Lange, T. M., Ramzan, F., Gültas, M., Schmitt, A.O.	Normalized Cumulative Gain as an Alternative Evaluation Measure for Genomic Selection Models	Genetics Selection Evolution
Lange, T. M., Gültas, M., Schmitt, A.O., Heinrich, F.	optRF: Optimising random forest stability by determining the optimal number of trees	BMC Bioinformatics
Heinrich, F., Simianer, H., Bölling, J., Röckelein, H., Roos, C., Reimer, C., Schmitt, A. O.	Genomic analysis of three medieval parchments from German monasteries	Scientific Reports
Heinrich, F., Lange, T. M., Kircher, M., Ramzan, F., Schmitt, A. O., Gültas, M.	Exploring the potential of incremental feature selection to improve genomic prediction accuracy	Genetics Selection Evolution
Wutke, M., Heinrich, F., Das, P.P., Lange, A., Gentz, M., Traulsen, I., Warns, F.K., Schmitt, A.O., Gültas, M.	Detecting Animal Contacts—A Deep Learning-Based Pig Detection and Tracking Approach for the Quantification of Social Contacts	Sensors
Heinrich, F., Ramzan, F., Rajavel, A., Schmitt, A.O., Gültas, M.	MIDESP: Mutual Information-Based Detection of Epistatic SNP Pairs for Qualitative and Quantitative Phenotypes	Biology
Klees, S., Heinrich, F., Schmitt, A.O., Gültas, M.	agReg-SNPdb: A Database of Regulatory SNPs for Agricultural Animal Species	Biology
Heinrich, F., Wutke, M., Das, P.P., Kamp, M., Gültas, M., Link, W., Schmitt, A.O.	Identification of regulatory SNPs associated with vicine and convicine content of Vicia faba based on genotyping by sequencing data using deep learning	Genes

For a full list of publication see ResearchGate or Google Scholar.