“iPC” stands for “Individualized Paediatric Cure” and the project’s goal is to collect, standardize and harmonize existing clinical knowledge and medical data and, with the help of artificial intelligence, create treatment models for patients. Scientists will test them on virtual patients to evaluate treatment efficacy and toxicity, thus improving both patient survival and their quality of life.

In order to accomplish the project’s goals, an interdisciplinary team consisting of basic, translational, and clinical researchers - all amongst the leaders in their respective fields – has been formed. In addition, strong relationships with European Centres of Excellence, patient organizations, and clinical trials which focus on personalised medicine for our proposed case studies, have been established.

To summarize, iPC will address the critical need for personalized medicine for children with cancer, contribute to the digitalization of clinical workflows and support the European Digital Single Market Strategy. Find out more about iPC on the following website: https://ipc-project.eu/  and follow the project on Twitter: https://twitter.com/iPC_H2020

To sum up the project, a new podcast edition as well as a brand-new video have been released on the project website. Follow this link to check them out: https://www.precise-project.eu/news/press-news

If you are interested in all scientific publications that emerged from the PrECISE project, then have a look here: https://www.precise-project.eu/publications-deliverables/publications

The Advisory Board members joined us on day 2: 

• Markus Christen, UZH Digital Society Initiative & Institute of Biomedical Ethics and History of Medicine
• Diego Di Bernardo, University of Naples and TIGEM
• Silke Gillessen, Kantonsspital St. Gallen - Klinik für Med. Onkologie und Hämatologie
• Hans Lehrach, Max Planck Institute for Molecular Genetics
• Mark Rubin, University of Bern Biomedical Research Department

The discussions were fruitful for the future of the project. In particular, the Q&A session was significant in terms of new perspectives. Overall, the methods developed were positively assessed by the Advisory Board and evaluated as “cutting edge” in cancer research. The PrECISE Consortium has demonstrated to work well and the collaboration between the partners is efficient, and the integration between inputs and factors from different disciplines and tasks goes on.

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.

From 8^th – 10^th December the 14^th Annual Rocky Mountain Bioinformatics Conference took place in Aspen. At this conference members of the International Society for Computational Biology (ISCB) meet and exchange their know-how and expertise. IBM gave a talk concerning the “Stratification of prostate cancer patients based on molecular interaction profiles” at the Rocky Conference.

Furthermore an interview with IBM on artificial intelligence was published on 2^nd December 2016 in the Swiss French Newspaper L’Hebdo. For our French speaking followers, the transcript can be found here.

Members of the PrECISE consortium are currently working on submissions for the ISMB/ECCB Conference taking place from 21^st until 25^th July in Prague, Czech Republic. The team will also meet internally on the first day to discuss PrECISE related questions and challenges and the way forward.

It's Movember again.

The moustache and November join up together each year to raise awareness of cancer. The Movember Foundation challenge men to change their appearance - and the face of men’s health - by growing a moustache.

Rules? Start on the 1st November clean-shaven and then grow your mo during the entire month turning it into the ribbon for men’s health.

Women? The Mo challenge is whatever you make it. Run a race. Learn to rock climb. Work out in costume. Try something new and raise funds for men's health.

Others? This Movember, get together to raise funds, make some noise and stop men dying too young.

JOIN THE MOVEMENT.

For more information visit:

mvmbr.co/2fcJCKc 

Partners of the PrECISE consortium already teamed up: COMBember

IBM's Watson is a cognitive technology that processes information more like a human than a computer. Watson can understand questions posed in natural language, searches for the appropriate terms in extensive text files, analyzes and evaluates by statistical means and finally gives the most probable answer.

It is also about the right answer when it comes to the treatment of cancer. The related questions are: Is the tumour benign or malignant? How does cancer progress? What is the effect of a certain drug?

Computer scientists from IBM are convinced that the answers can be found in the growing amount of medical diagnostic data. Cancer is of such complexity, that the recognition of typical patterns is possible only with the help of artifical intelligence.

Watch the video for more information. The clip (timeframe: 06:15 - 10:20) features the PrECISE partners Peter Wild from University of Zurich and Maria Rodriguez Martinez from IBM.

A sequence was dedicated to IBM with several interviews including partner Dr. Peter Wild from the Hospital of Zurich, talking about the benefits of integrating Watson in making life-saving diagnosis: "Watson can analyse all of these prostate cancer samples and compare this with existing published data to determine the right course of treatment for the individual patient. It's impossible for me to do all of this as quickly."

Lately, IBM’s cognitive computer Watson successfully diagnosed a woman with a rare form of leukemia which doctors had not detected - see the article here. In the PrECISE project, the Watson cognitiv copmputer is used to mine literature and to analyze incoming data using the developed models and methods in the work packages 4 to 6. 

Moreover, Maria Rodriguez-Martinez of IBM and Peter Wild of University of Zurich bring in their expertise about cancer research and about the fact how big data brings medicine to new dimensions in THIS clip.

On 11^th - 12^th July 2016, TECHNIKON hosted the PrECISE technical meeting in Villach, Austria. We had a lot of technical discussions ranging from the inference of clonal populations, deep learning techniques and modelling to the production of genomic and proteomic data. The main goal of this meeting was to keep the consortium updated about the ongoing activities of each partner and to foster the collaboration within different research teams. Moreover, we focused on our way forward and planned the upcoming tasks properly. The upcoming months are characterized by loads of exiting research work that will result in promising publications and impact several deliverables.

The H2020 PrECISE project has successfully been kicked-off.

Representatives of 9 different partners from 6 different countries participated in the kick-off meeting which took place from 21^st - 22^nd January at IBM in Zurich. Lots of discussions regarding the future work were going on and plans for the next steps have been made.

For further project information, please have a look at the announcement letter.

On 1^st January 2016, the Pan-European and American cooperative H2020research project – PrECISE – has successfully started. The kick-off meeting will be hosted by the technical leader IBM in Zurich/Switzerland from 21^st – 22^nd January 2016. PrECISE aims to improve the detection and treatment process of prostate cancer by developing new computational approaches to exploit next generation molecular data. The proposed software framework will be accessible through a graphical interface that will facilitate its dissemination and use by researchers, clinicians and biomedical industries.