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Technical and General Assembly meeting in Prague

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On Friday, 21st July 2017, the PrECISE technical and General Assembly (GA) meeting took place in Prague, Czech Republic.


The GA meeting was mainly dedicated to the ongoing periodic reporting session. The technical meeting focused on the ongoing research work of the single work packages, the upcoming challenges and the ongoing discussions between interacting partners. The discussion in WP1 and WP2 mainly focused on methods for predicting clonal compositions and evaluated them on synthetic data and on HCC profiles. The focus of the WP3 presentations was on prostatic cell line perturbation experiment time-course data processing and network reconstruction algorithms. WP4 is currently dealing with automatic reconstruction of molecular networks from publications, databases and datasets. In WP5 the focus lies on the integration of patient molecular data into the logical models - drug target prediction is one of the upcoming challenges. WP6 is currently selecting and preparing patient samples for molecular profiling. WP7 is working on the visualization of pathways and the data input interface. One of the upcoming challenges in general is taking into account a large number of factors and inputs from a variety of disciplines and tasks and integrate them in respect of the overall PrECISE goals. The meeting was great success and a lot of upcoming action points were defined.



The meeting was co-located with the 25th Conference on Intelligent Systems for Molecular Biology and the 16th European Conference on Computational Biology (ISMB/ECCB 2017). The PrECISE consortium was heavily involved in the poster sessions:

  • Session A-234: DeepGRN: Deciphering gene deregulation in cancer development using deep learning (IBM)
  • Session A-319: Inferring network statistics from high-dimensional time-course data (TUDA)
  • Session A-323: Fast biological network reconstruction from high-dimensional time-course perturbation data using sparse multivariate Gaussian processes (TUDA)
  • Session A-357: Conceptual and computational framework for logical modelling of biological networks deregulated in diseases (CI)
  • Session A-364: Pan-cancer classification of gene signatures for their information value and functional redundancy (CI)
  • Session B-276: Selection of stable biomarker signature for prediction of metabolic phenotypes (ETH, IBM)
  • Session B-330: A computational framework for systems pathology of prostate cancer (UZH, ETH, IBM)
  • Session B-384: Inferring clonal composition from multiple tumor biopsies (BCM, ETH, IBM, UZH)

The consortium is currently planning a technical meeting involving also the ADVISORY BOARD members. This meeting is planned to be held in autumn 2017.

Sandra Lisa Lattacher studied Medical Information Technology at the Carinthia University of Applied Sciences and received her master’s degree in Health Care IT in February 2014. Her master thesis deals with methods for indoor position estimation of seismic sources in smart homes by means of neural networks. She has been working for Technikon since April 2014, where her main focus lies on data analysis, pattern recognition and statistical evaluation on PUF data.


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