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

Additional Information

Pau Aceituno at the MPI Mathematics offer an introductory course on graph theory that could be of interest:
Tomorrow! 
Date: Feb. 2nd
Time: 13:30 - 15:00
Place:
A3 01 (3rd Stock, Section A)
Max Planck Institut for Mathematics in the Sciences
Inselstrasse 22

   Topics:
   Programming on graphs (30-45 min)
   Basics of Computer Science
   - Von Neumann architecture
   - Basic data structures: List vs array
   Data structures for graphs
   - Adjacency lists vs Adjacency matrices
   Simple algorithms on graphs
   - Count paths between nodes: Proof of correctness
   - Count all paths of a given length (Dynamic programming version)
   - Equivalence of path counting and matrix multiplication
   Spectral methods (45-60 min)
   Power method for computing eigenvalues/eigenvectors
   - Derivation of the method for the largest eigenvalue
   - Convergence rate
   - Extension to other eigenpairs
   Spectral Partitioning
   - Motivation and Problem set up.
   - Method description
   Page-rank
   - Motivation and problem set-up
   - Method description

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

    Zeit und Ort

    Praktikum vom: 05.02.2018 -- 23.02.2018. Woche 12.02.2018 -- 16.02.2018 Eigenarbeit ohne Betreuung.
    Raum: R109
    Ganztaetig: 10:00 -- 17:00


    Spezielle Termine:
    VLDatumUhrzeit
    Defensio Al'Arab06.02.18 15:00 Keine Betreuung ab 14:30 Uhr; alle Teilnehmer sind willkommen der Defensio beizuwohnen (Paulinum, P501)

    Background

    introduction to the problem

    Research Questions

    Tasks