Here we collect information for all four parts (lecture, special lecture, seminar, and practical course) of the module Graphen und Biologische Netze.

Main Lecture

The lecture is a blackboard-style lecture. There is no TeX script!

Special Lecture

VLDatumRaumUhrzeitThema
VL 0111.12.17 R 109 14:00
VL 0215.12.17 R 109 14:00
VL 0318.12.17 R 109 14:00

Seminar

SeminarDatumRaumUhrzeitThema
S 0116.01.18 R 018 10:30 -- 20:00
S 0217.01.18 R 018 12:30 -- 20:00
S 0319.01.18 R 018 12:30 -- 20:00

IMPORTANT:
STICK TO 15 min presentations!
Note that the seminar takes place in R018!
The order below is *not* the order of presentations. The order of presentations is not fixed yet


Session 1: Biomedical Application 16.01.18   10:30 - 12:30

  • 1 [Anne-Sophie Kieslinger] (getauscht mit Kraft)
    Kocevar G, Stamile C, Hannoun S, et al. Graph Theory-Based Brain
    Connectivity for Automatic Classification of Multiple Sclerosis Clinical Courses.
    Frontiers in Neuroscience. 2016;10:478. doi:10.3389/fnins.2016.00478.
    link
  • 2 [Erik Fortenbacher]
    EpiGeNet: A Graph Database of Interdependencies Between Genetic and Epigenetic Events in Colorectal Cancer
    Irina Balaur, Charles Auffray
    Journal of Computational Biology 24: 969-980 (2017) DOI: 10.1089/cmb.2016.0095
    link
  • 3 [Jan Hake]
    Improving protein complex prediction by reconstructing a high-confidence protein-protein interaction network of Escherichia coli from different physical interaction data sources
    Shirin Taghipour, Peyman Zarrineh, Mohammad Ganjtabesh, Abbas Nowzari-Dalini
    BMC Bioinformatics 18: 10 (2017)
    link
  • PAUSE
  • 4 [Bastian Walthier]
    Eric Lewitus, Helene Morlon
    Characterizing and Comparing Phylogenies from their Laplacian Spectrum,
    Systematic Biology, Volume 65, Issue 3, 1 May 2016, Pages 495–507.“
    link
  • 5 [Camill Kaipf]
    Rasha Elhesha and Kahveci
    Identification of large disjoint motifs in biological networks
    BMC Bioinformatics (2016) 17:408
    link
  • Session 2: Biomedical Application 16.01.18   14:00 - 18:30

  • 1 [Benjamin Schindler]
    EpiTracer - an algorithm for identifying epicenters in condition-specific biological networks
    Narmada Sambaturu, Madhulika Mishra and Nagasuma Chandra
    BMC Genomics 2016
    link
  • 2 [Johanna Dobrinner]
    A. Costa et al.:
    On the calculation of betweenness centrality in marine connectivity studies using transfer probabilities
    PLoS ONE https://doi.org/10.1371/journal.pone.0189021
    link
  • 3 [Daniel Mayer]
    DiffSLC: A graph centrality method to detect essential proteins of a protein-protein interaction network
    Divya Mistry, Wise RP, Dickerson JA.
    PLoS ONE. 2017;12(11):e0187091. doi:10.1371/journal.pone.0187091.
    link
  • PAUSE
  • 4 [Theresa Kraft] (getauscht mit Kieslinger)
    McCusker JP, Dumontier M, Yan R, He S, Dordick JS, McGuinness DL.
    Finding melanoma drugs through a probabilistic knowledge graph.
    PeerJ Computer Science 3:e106 (2017)
    link
  • 5 [Simon Bordewisch]
    Minkin, Ilia, Son Pham, and Paul Medvedev.
    TwoPaCo: An efficient algorithm to build the compacted de Bruijn graph from many complete genomes
    Bioinformatics, 33(24), 2017, 4024-4032
    link
  • 6 [Elias Saalmann]
    Sadri, Amin, et al. "Shrink: Distance Preserving Graph Compression." Information Systems (2017).
    link
  • PAUSE
  • 7 [Falco Kirchner]
    A Novel Algorithm for Pattern Matching Based on Modified Push-Down Automata
    Bilal Lounnas, Brahim Bouderah, Abdelouahab Moussaoui
    J Information Sci Eng 32: 403-424 (2016)
    link
  • 8 [Dustyn Eggers]
    Fenix Huang, Christian Reidys, and Reza Rezazadegan
    Fatgraph models of RNA structure
    Molecular Based Mathematical Biology 5(1): 1-20 (2017) DOI 10.1515/mlbmb-2017-0001
  • Session 3: Mathematics and Computer Science 17.01.18   13:00 - 18:00

  • 1 [Christoph Kramer]
    "A New Graph Theoretical Method for Analyzing DNA Sequences Based on Genetic Codes"
    Nafiseh Jafarzadeh, Ali Iranmanesh
    MATCH 75:731-742 (2016)
    link
  • 2 [Alexander Heese]
    Analogies between the crossing number and the tangle crossing number
    Robin Anderson, Shuliang Bai, Fidel Barrera-Cruz, Éva Czabarka, Giordano Da Lozzo, Natalie L. F. Hobson, Jephian C.-H. Lin, Austin Mohr, Heather C. Smith, László A. Székely, Hays Whitlatch (2017)
    link
  • 3 [Eric Witt]
    Beguerisse-Díaz et al. (2017). Flux-dependent graphs for metabolic networks. ARXIV preprint
    link
  • PAUSE
  • 4 [Ye Chen]
    Whole Genome Phylogenetic Tree Reconstruction Using Colored de Bruijn Graphs
    Cole A. Lyman,M. Stanley Fujimoto,Anton Suvorov,Paul M. Bodily,Quinn Snell,Keith A. Crandall,Seth M. Bybee,Mark J. Clement
    ARXIV preprint
    link
  • 5 [Jichang Li]
    A greedy alignment-free distance estimator for phylogenetic inference
    Sharma V. Thankachan, Sriram P. Chockalingam, Yongchao Liu, Ambujam Krishnan and Srinivas Aluru
    BMC Bioinformatics (2017) 18(Suppl 8):238
  • 6 [Sandra Waske]
    Graph analysis and modularity of brain functional connectivity networks: searching for the optimal threshold
    Cécile Bordier, Carlo Nicolini, Angelo Bifone
    Front. Neurosci., 03 August 2017
    link
  • PAUSE
  • 7 [Fei Pu]
    A proximity-based graph clustering method for the identification and application of transcription factor clusters
    Maxwell Spadafore
    BMC bioinformatics 2017
    link
  • 8 [Albert Gass]
    Chunxiao Sun, Shide Song, Xiaoli Xie.
    A Graph-Based Algorithm for Weak Motif Discovery in DNA Sequences.
    Boletín Técnico 55(9):606-613 (2017)
    link
  • 9 [Marie Miotke]
    Estimating gene regulatory networks with pandaR
    Daniel Schlauch, Joseph N. Paulson, Albert Young, Kimberly Glass, John Quackenbush
    Bioinformatics 33(14): 2232-2234 (2017)
  • Practical Course

    Background

    introduction to the problem

    Research Questions

    Tasks