Prof. Dr. Mehmet Gültas
RESEARCH FOCUS
1. Algorithmic Methods
- Machine Learning and Deep Learning
- Information theory
- Probabilistic data models
- Algorithmic methods of statistical learning
2. Data Management and Data Analysis
- Statistical analysis and interpretation of big data in plant/animal breeding, bioinformatics and livestock husbandry
- Big data analyses in agriculture in general
- Development of machine learning algorithms to identify and classify specific behavioural patterns in livestock
- Development of new data management systems with regard to collection, storage and administration of data relevant to agriculture
- Establishment of unsupervised machine learning algorithms (e.g. clustering methods) to analyse agricultural data
- Development of statistical data models and machine learning methods to establish digital technologies within the field of agriculture
- Understanding complex biological processes like epistatic interactions among genotypic markers by applying statistical methods
3. Bioinformatics and Breeding Informatics
- Analysis and interpretation of multi-omics data (Next-Generation Sequencing(NGS), RNA-seq, etc.)
- Understanding of complex biological processes and networks, e.g. the research on epistatic interactions among genotypic markers
- Analysis of transcription factors concerning their functions as well as interactions
- Pathway analyses (upstream, downstream and masterregulator analyses) for the understanding of biological activities of regulatory processes on many levels (RNA, proteins, metabolites, etc.)
- Information theory and its applications in bioinformatics and computational biology
- Machine Learning approaches and evolutionary algorithms in bioinformatics and computational biology
- Gene regulatory network analysis
- Clustering approaches in bioinformatics (Markov Cluster Algorithm)
Teaching
Winter term
- Applied Machine Learning in Agriculture with R (MSc)
- Data Analysis with R (MSc)
- Applied Bioinformatics with R (MSc)
- Scientific Project: scientific methods, procedures and practical skills in animal and plant breeding (MSc)
- Forschungspraktikum Biometrie mit R (BSc)
Summer term
- Data Analysis with R (MSc)
- Forschungspraktikum Biometrie mit R (BSc)
- Anwendungsgebiete der Data Science (BSc)
- Bioinformatik (BSc)
- Scientific Project: scientific methods, procedures and practical skills in animal and plant breeding (MSc)
On-going and past projects, student related works and theses
- Data Analysis with R (MSc)
- Forschungspraktikum Biometrie mit R (BSc)
- Anwendungsgebiete der Data Science (BSc)
- Bioinformatik (BSc)
- Scientific Project: scientific methods, procedures and practical skills in animal and plant breeding (MSc)
On-going and past projects, student related works and theses
| Term | Topic |
|---|---|
| ST 2020 | Supervision of master's thesis: Development of an Automatic Pig Detection and Tracking System Using Machine Learning |
| ST 2020 | Supervision of bachelor's thesis: Identifikation regulatorischer SNPs in Brassica napus durch Analyse von Multi-Omics-Daten |
| ST 2020 | Supervision of bachelor's thesis: Die Entschlüsselung der spezifischen transkriptionellen Genregulation in Bezug auf den Krankheitsverlauf der Hühnerpest bei Stockente und Huhn |
| WT 2019/2020 | Supervision of master's thesis: Genomic Prediction of Economically Relevant Traits in Livestock using Machine Learning |
| WT 2019/2020 | Supervision of bachelor's thesis: Vorhersage von regulatorischen SNPs und deren Einfluss auf die Bindeaffinität von TFs in Pflanzen |
| WT 2019/2020 | Supervision of bachelor's thesis: Analyse von Markerkandidaten und ihren assoziierten Genen in Vicia faba mittels bioinformatischer Methoden |
| ST 2019 | Supervision of master's thesis Automatic distinction of behaviour patterns in pigs using anomaly detection techniques with a predictive convolutional network |
| ST 2019 | Supervision of master's thesis: Development of a database of predicted regulatory SNPs and their impact on the binding propensity of transcription factors |
| ST 2019 | Supervision of master's thesis: Random forest feature selection for MI epistatic networks |
| ST 2019 | Supervision of research project: Comparison on genomic predictions using different statistical methods in a simulated cattle population |
| WT 2018/2019 | Supervision of research project: Prediction of eukaryotic promoters using next generation sequencing data |
| WT 2018/2019 | Supervision of research project: Prediction of epistatic interactions using information theory |
| ST 2018 | Supervision of master's thesis: Assoziationsstudie zum Vicingehalt bei Vicia faba basierend auf Genotyping by Sequencing-Daten |
| ST 2018 | Supervision of master's thesis: Genome wide association analysis for identification of markers associated with eggshell thickness using reverse regression based methods |
| ST 2018 | Supervision of master's thesis: Identification of bovine tissue- and population specific transcription factor cooperations using next generation sequencing data and genome-wide variant calling |
Publications
See ResearchGate or Google Scholar.