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PrECISE





  • PrECISE

    Personalized Engine for Cancer Integrative Study and Evaluation




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Welcome to PrECISE


The PrECISE project is a pilot project that combines hypothesis-driven strategies with data-driven analysis in a novel mathematical and computational methodology for the integration of genomic, epigenetic, transcriptomic, proteomic, and clinical data with the goal of risk-stratifying patients and suggesting personalized therapeutic interventions. We have the following specific objectives:

  • Development of a comprehensive computational methodology:
  • To integrate publicly available multi-omics datasets, well-characterized multiple-biopsies cohorts, and literature-driven knowledge powered by the Watson cognitive computer, developed at IBM.


  • Characterization of intra-tumour heterogeneity:
  • We will apply PrECISE to prostate cancer molecular cohorts where multiple biopsies have been generated from each patient.


  • Suggestion of chemotherapy drugs and targeted therapies for each patient:
  • We will investigate molecular mechanisms, identify suitable intervention points for therapy and suggest personalized therapies based on patient’s clonal signatures, and we will validate our predictions in a panel of prostatic cell lines.


  • Development of PrECISE into deployable, easy to use software tool:
  • We will integrate the developed computational modules with the Watson cognitive technology developed at IBM in a user-friendly interface and make PrECISE accessible to the clinical research community.


Welcome to PrECISE


The PrECISE consortium is constantly publishing scientific articles. For all these publications open access is ensured via the open access repository Zenodo. We also created a dedicated community focusing on PrECISE related content. Click on the logo on the left to get directly forwarded to the PrECISE community.

Moreover, we provide open access to all officially accepted public deliverables of the PrECISE project.





2017

Community assessment of cancer drug combination screens identifies strategies for synergy prediction
Michael P Menden, Dennis Wang, Yuanfang Guan, Michael Mason, Bence Szalai, Krishna C Bulusu, Thomas Yu, Jaewoo Kang, Minji Jeon, Russ Wolfinger, Tin Nguyen, Mikhail Zaslavskiy, DREAM Consortium, In Sock Jang, Zara Ghazoui, Mehmet Eren Ahsen, Robert Vogel, Elias Chaibub Neto, Thea Norman, Eric KY Tang, Mathew J Garnett, Giovanni Di Veroli, Steve Fawell, Gustavo Stolovitzky, Justin Guinney, Jonathan R. Dry, Julio Saez-Rodriguez
DREAM challenges, October, 2017.

Application of network diffusion approaches to drug screenings: A perspective on multi-layered networks derived from cell lines and drugs (Poster)
Vigneshwari Subramanian, Bence Szalai, Luis Tobalina, Julio Saez-Rodriguez
17th Workshop on Network Tools and Applications for Biology: Methods, tools and platforms for Personalized Medicine in the Big Data Era (NETTAB) , October, 2017.

Logic modeling in quantitative systems pharmacology (Journal Paper)
Pauline Traynard, Luis Tobalina, Federica Eduati, Laurence Calzone, Julio Saez-Rodriguez
CPT: Pharmacometrics and Systems Pharmacology, Volume 6, Issue 8, August 2017.

MaBoSS 2.0: an environment for stochastic Boolean modeling (Journal Paper)
Gautier Stoll, Barthelemy Caron, Eric Viara, Aurelian Dugourd, Andrei Zinovyev, Aurelien Naldi, Guido Kroemer, Emmanue Barillot, Laurence Calzone
Bioinformatics, Volume 33, Issue 14, July 2017.

Selection of stable biomarker signature for prediction of metabolic phenotypes (Poster)
Jelena Čuklina, Yibo Wu, Evan G. Williams, María Rodríguez Martínez, Ruedi Aebersold
25th Conference on Intelligent Systems for Molecular Biology / 16th European Conference on Computational Biology (ISMB/ECCB), July 2017.

DeepGRN: Deciphering gene deregulation in cancer development using deep learning (Poster)
Roland Mathis, Matteo Manica, Maria Rodriguez Martinez
25th Conference on Intelligent Systems for Molecular Biology / 16th European Conference on Computational Biology (ISMB/ECCB), July 2017.

Inferring network statistics from high-dimensional undersampled time-course data (Poster)
Dominik Linzner, Heinz Koeppl
25th Conference on Intelligent Systems for Molecular Biology / 16th European Conference on Computational Biology (ISMB/ECCB), July 2017.

Fast biological network reconstruction from high-dimensional time-course perturbation data using sparse multivariate Gaussian processes (Poster)
Sara Al-Sayed, Heinz Koeppl
25th Conference on Intelligent Systems for Molecular Biology / 16th European Conference on Computational Biology (ISMB/ECCB), July 2017.

Logic modeling in quantitative systems pharmacology (Poster)
Pauline Traynard, Luis Tobalina, Federica Eduati, Laurence Calzone, Julio Saez-Rodriguez
25th Conference on Intelligent Systems for Molecular Biology / 16th European Conference on Computational Biology (ISMB/ECCB), July 2017.

Incorporating patient-specific molecular data into a logic model of prostate cancer (Poster)
Pauline Traynard, Jonas Beal, Luis Tobalina, Emmanuel Barillot, Julio Saez-Rodriguez, Laurence Calzone
25th Conference on Intelligent Systems for Molecular Biology / 16th European Conference on Computational Biology (ISMB/ECCB), July 2017.

Inferring clonal composition from multiple tumor biopsies (Technical Note)
Matteo Manica, Philippe Chouvarine, Roland Mathis, Ulrich Wagner, Kathrin Oehl, Karim Saba, Laura De Vargas Roditi, Arati N Pati, Maria Rodriguez-Martinez, Peter J Wild, Pavel Sumazin
25th Conference on Intelligent Systems for Molecular Biology / 16th European Conference on Computational Biology (ISMB/ECCB), July 2017.

2016

Integration of Multi-omics Data for Prediction of Metabolic Traits (Poster)
Jelena Čuklina, Yibo Wu, Evan. G. Williams, Maria Rodríguez-Martínez; Ruedi Aebersold
LATSIS Symposium on Personalized Medicine (LATSIS), 2016.

Pypath and Omnipath: integrate, analyze and extract signaling networks from literature curated resources (Poster)
Dénes Türei, Luis Tobalina, David Henriques, Pauline Traynard, Laurence Calzone, Tamás Korcsmáros, Julio Saez-Rodriguez
17th International Conference on Systems Biology (ICSB), 2016.

Building a Boolean model of signaling pathways altered in prostate cancer (Poster)
Pauline Traynard, Luis Tobalina, David Henriques, Emmanuel Barillot, Julio Saez-Rodriguez, Laurence Calzone
17th International Conference on Systems Biology (ICSB), 2016.

Stratification of prostate cancer patients based on molecular interaction profiles (Poster)
Roland Mathis, Matteo Manica, María Rodríguez Martínez
All SystemsX.ch Day, 2016.

CoDON: a learning framework for linking genomics and transcriptomics data to protein expression (Poster)
Matteo Manica, Roland Mathis, María Rodríguez Martínez
All SystemsX.ch Day, 2016.

Proteome heterogeneity in benign and malignant prostate tissue (Poster)
Tiannan Guo, Li Li, Qing Zhong, Niels J. Rupp, Konstantina Charmpi, Christine E. Wong, Ulrich Wagner, Jan H. Rueschoff, Wolfram Jochum, Christian Fankhauser, Karim Saba, Cedric Poyet, Peter J. Wild, Ruedi Aebersold, Andreas Beyer
All SystemsX.ch Day, 2016.

An integrative Systems Biology approach to advance in the understanding and treatment of prostate cancer (Poster)
Luis Tobalina, David Henriques, Julio Saez-Rodriguez
Bioinformatics for Young inTernational researchers (byteMAL), 2016.

tec MErCuRIC is a multicentre phase Ib/II clinical trial which will assess a novel therapeutic strategy (combined treatment of a MEK inhibitor MEK-162 with a MET inhibitor PF-02341066) to combat metastasis, improve survival and change current clinical practice for CRC patients with KRAS mutant (MT) and KRAS wild type (WT) (with aberrant c-MET) tumours. The consortium will go beyond the current state-of-the- art by (i) employing a novel treatment strategy targeting the biology of the disease and by (ii) using next generation sequencing (NGS) and ‘xenopatients’ to identify CRC patient subgroups who will maximally benefit from this novel treatment strategy.



May 2016

A postdoctoral position is opened in the group of Julio Saez-Rodriguez at the Joint Research Center for Computational Biomedicine (JRC-COMBINE), an initiative of RWTH Aachen, Aachen University Hospital and Bayer Technology Services.

Applicants interested broadly in their research are welcome, but they are particular looking for a postdoc to work within the EU-H2020 PrECISE project, to combine hypothesis-driven with data-driven analysis in a novel mathematical and computational methodology for the integration of genomic, epigenetic, transcriptomic, proteomic, and clinical data, applied to prostate cancer.

All the information is available on the JRC-COMBINE News web page.




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