1. Instructor

- Shuigeng Zhou (周水庚), sgzhou@fudan.edu.cn
- Office: Room 502, Yifu Building
- Tel: 55664967
- Homepage: admis.fudan.edu.cn/~sgzhou

2. Lectures

- Time: 8:55 – 11:35, Tuesday (1st -16th week)
- Place: HY605
- Office Hours: By appointment. The best way to reach me is by e-mail.

3. Course Overview and Goals

In the past decades, vast amounts of biological network data have been generated, which allow increasing numbers of system level studies of biological structures and processes. And many mathematical and computational tools are being developed to analyze these data with the goal of better understanding of biological processes, disease, and contributing to the time and cost effectiveness of biological experimentation. This course will give an overview of the existing types of biological network data, highlight the sources of errors and biases in the data, and introduce in detail the major methods and techniques for analyzing and mining these data.4. Prerequisites

You should have exposure to at least some of the following:- Mathematics: Basic probability and/or discrete math. Linear algebra: definition of a matrix.
- Computer science: Experience with computer programming; Knowledge of basic data structures/algorithms
- Biology: Basic definitions of molecular biology (e.g. DNA, RNA, proteins, etc.).

5. Recommended Texts

- Luonan Chen, Rui-Sheng Wang, and Xiang-Sun Zhang, Biomolecular Networks: Methods and Applications in Systems Biology. Wiley, 2009
- Xiaoli Li and See-Kiong Ng (eds). Biological Data Mining in Protein Interaction Networks, IGI Global , 2009
- F. Kepes (ed.), Biological Networks, World Scientific, 2007.
- Jurisica and Wigle (Editors), Knowledge Discovery in Proteomics, CRC Press, 2005.
- Bornholdt and Schuster (Editors), Handbook of Graphs and Networks: From the Genome to the Internet, Wiley, 2003.
- Kurt Mehlhorn, Stefan Näher, LEDA: A Platform for Combinatorial and Geometric Computing, Cambridge University Press, 1999.
- Related articles from Nature, Science, PNAS, NAR, Bioinformatics, BMC Bioinformatics / Systems Biology, etc.

6. Grading Scheme

- 10% class participation
- 30% paper presentation
- 60% algorithm implementation and written report

7. Topics Outline

The course will cover the following topics:- Concepts and types of biological networks (1 week)
- Topological properties and structure of biological networks (2 weeks)
- Alignment of biological networks (2 weeks)
- Protein-protein interaction networks (2 weeks)
- Network-based prediction of protein function (2 weeks)
- Network-based prediction of complexes (2 weeks)
- Network-based drug target detection (1 week)
- Matrix factorization methods for biological network computing (1 week)
- Random walk methods for biological network computing (1 week)
- Graph embedding methods for biological network computing (1 week)
- Deep learning for biological network computing (1 week)
- Gene regulatory networks (2 weeks)
- Transcriptional regulatory networks (1 week)
- Network visualization (1 week)

8. Lecture Notes

Lec# | Content | Lecture noted download |
---|---|---|

1 |
Intro to the Course
Intro to Biological Networks |
Lecture 0
Lecture 1 |

2 | Intro to Molecular Biology | Lecture 2 |

3 | Topological properties | Lecture 3 |

4 | Network Models, Motifs and Clustering | Lecture 4 |

5 | Protein to Protein Interactions | Lecture 5 |

6 | Detecting essential proteins | Lecture 6 |

7 | Protein Function Prediction | |

8 | Random Walk and Its Applications | |

9 | Graph Embedding and Its Applications |

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