Machine Learning & Knowledge Extraction for Bioinformatics
Session organized by Tim CONRAD, Joel ARRAIS, Ulrich STELZL, Ziv BAR-JOSEPH
The session aims to bring together biologists with machine learning experts.
Computational biology, bioinformatics and machine learning are of increasing importance for developing algorithms to model and solve biological problems. A typical example includes the analysis and knowledge extraction from gene-expression data, ultimately allowing for new clinical diagnostics tools. Also very promising is proteomics, a relatively young field dealing with complicated network protein-interactions. For example, changes in the concentration of some protein species can often be directly linked to diseases, e.g. cancer.
In addition to application papers, manuscripts dealing with fundamental questions and theoretical aspects in machine learning, as needed for successful biological applications are encouraged.
Research topics – with application to bioinformatics – covered by this special session include but are not limited to the following topics:
- Graphical models
- Prediction of drug-target interactions
- ML for cancer research
- Time Series Gene expression data
- Regulatory networks
Accepted Papers will be published in the Springer CD-MAKE Volume of Lecture Notes in Artificial Intelligence (LNAI). We are planning to invite outstanding contributions for extension in journals (Springer MACH, BMC MIDM, tba.)
For submission details please proceed to the CD-MAKE authors area