Theoretische Informatik und Algorithmische Methoden

Projects


Studenten die Interesse an einer Bachelor-/ Masterarbeit oder einem Praktikum in unserer Gruppe haben, können aus einer Vielzahl von Forschungsthemen aus folgenden Projekten wählen:

  • Development of novel information theory based methods for the identification of interacting transcription factors (TFs)
  • Identification of enhancer-promoter interactions using information theoretic approaches
  • Detection of differentially different TFBS-distributions in promoters
  • Clustering of co-expressed genes
  • Prediction of DNA-binding residues in proteins using mutual information
  • Visualisation TF <-> gene<-> miRNA signal transduction pathways



Dr. Mehmet Gültas
Dr. Mehmet Gültas
Margarethe von Wrangell-Weg 7
37075 Göttingen
Tel. +49 551 39 21893


FORSCHUNGSSCHWERPUNKT


  • Bioinformatics
    • Information Theory and its Applications in Bioinformatics and Computational Biology
    • Machine Learning Approaches in Bioinformatics and Computational Biology
    • Gene regulatory network analysis
    • Clustering Approaches in Bioinformatics (Markov Cluster Algorithm)

  • Theoretical Computer Science and Algorithmic Methods
    • Algorithmic Methods of Statistical Learning



NEWS


  • Protein-DNA binding sites with Jensen-Shannon divergence: Our study, A Novel Sequence-Based Feature for the Identification of DNA-Binding Sites in Proteins Using Jensen?Shannon Divergence, has been recently published in a special issue of the MDPI-Entropy on the research topic "Entropy on Biosignals and Intelligent Systems". (Published on 24 October 2016)

    • Metastasizing cancer: Our study, Computational Identification of Key Regulators in Two Different Colorectal Cancer Cell Lines, has been recently published in a special issue of the Frontiers in Genetics on the research topic "Systems Biology of Transcription Regulation". (Published on 05 April 2016)

    • Heart development: Our study, Computational Detection of Stage-Specific Transcription Factor Clusters during Heart Development, has been recently published in a special issue of the Frontiers in Genetics on the research topic "Systems Biology of Transcription Regulation". (Published on 23 March 2016)

    • PC-TraFF is published: Our novel information theory based method for the identification of potentially collaborating transcription factors has been recently published in BMC Bioinformatics.
      The main idea: we consider the genome as a document, genes as sentences, individual sequence elements as words, and apply mathematical concepts proven in linguistics. PC-TraFF stands for "Potentially Collaborating Transcription Factor Finder". It is able to identify, in a given set of genes, important pairs of transcription factor binding sites. The corresponding transcription factors can be reasonably hypothesized to functionally interact in the regulation of these genes. For details please see "PC-TraFF: identification of potentially collaborating transcription factors using pointwise mutual information". (Published on 1 December 2015)
      You can also use its web server for your analysis: "PC-TraFF Server".

    • Our new study, "CRF-based models of protein surfaces improve protein-protein interaction site predictions" , has been published in BMC Bioinformatics. For details please see "CRF-based models". (Published on 13 August 2014)

    • QCMF is published: Our novel quantum information theory based method for the identification of functionally or structurally important sites in proteins has been published in BMC Bioinformatics.
      In this study, we present a new method, the Quantum Coupled Mutation Finder (QCMF) that incorporates significant dis/similar amino acid pair signals in the prediction of functionally or structurally important sites. Our results suggest that the QCMF reaches an improved performance in identifying essential sites from MSAs of both proteins with a significantly higher Matthews correlation coefficient (MCC) value in comparison to previous methods.
      For details please see "Quantum coupled mutation finder: predicting functionally or structurally important sites in proteins using quantum Jensen-Shannon divergence and CUDA programming". (Published on 3 April 2014)

    • CMF is published: Our novel information theory based method for the identification of compensatory mutations between important sites in proteins has been published in BMC Bioinformatics.
      In this study, we present a new method, the Coupled Mutation Finder (CMF) that quantifies the phylogenetic noise for the detection of compensatory mutations.
      For details please see "Coupled mutation finder: A new entropy-based method quantifying phylogenetic noise for the detection of compensatory mutations". (Published on 11 September 2012)