Cancer is a heterogeneous class of pathologies that can affect nearly all tissues in the human body. These diseases are generally characterized by a common set of features, known as the Hallmarks of cancer (Hanahan and Weinberg 2000 and 2011). The hallmarks constitute properties, cancer cells often acquire during their development. These are often caused by an interplay of miscellaneous molecular and (epi-)genetic aberrations.
Using modern high-throuput technologies, we are now able to measure different properties of cancer cells at relatively low cost. This development facilitated the creation of huge international projects, such as The Cancer Genome Atlas (TCGA), that try to catalog the genomic landscape of thousands of cancers across many disease types. The tremendous amount of the generated datasets and their high dimensionality make a manual evaluation impossible. Therefore, the development of automated and robust bioinformatics methods for the analysis of these datasets has become a necessity.
In this seminar you will learn how bioinformatics approaches can be used to study the molecular mechanisms of cancer and thereby improve our knowledge of biological processes that are responsible for tumor initiation and progression. Each participant will present a research paper that covers an interesting computational method to categorize tumors, to identify pathogenic mechanisms, or to find novel biomarkers that improve the diagnostic of the disease.
The seminar will be held as a block seminar the week before the lectures start.
In order to participate in the seminar you are required to attend the first meeting.
There the topics are distributed. No prior registration or reservation is possible.
Please confirm your participation in the seminar until 2019/07/12 via mail to teaching@bioinf.uni-sb.de.
Also, you have to register in HISPOS until 2019/07/27.
# | Paper | Autoren | |
---|---|---|---|
1 | Designing string-of-beads vaccines with optimal spacers. | Schubert et al. | |
2 | Predicting drug response of tumors from integrated genomic profiles by deep neural networks. | Chiu et al. | |
3 | Identifying drug effects via pathway alterations using an integer linear programming optimization formulation on phosphoproteomic data. | Mitsos et al. | |
4 | Metagenes and molecular pattern discovery using matrix factorization. | Brunet et al. | |
5 | Improved anticancer drug response prediction in cell lines using matrix factorization with similarity regularization | Wang et al. | |
6 | Optimal structural inference of signaling pathways from unordered and overlapping gene sets | Acharya et al. | |
7 | A Copula Based Approach for Design of Multivariate Random Forests for Drug Sensitivity Prediction | Haider et al. |