World Intelligence Congress WIC 2014 International Conference on Brain Informatics and Health (WIC 2014)

World_Intelligence_Congress_2014
11-14 August 2014 / Warsaw, Poland
http://wic2014.mimuw.edu.pl/bih/homepage

Special Session on Advanced Methods of Interactive Data Mining for Personalized Medicine
Paper Submission via the Conference On-Line System LNCS/LNAI Style  Papers (and Proposals) due to April, 13, 2014 (Camera Ready due to May 11, 2014) – closed –

Special Session Organizers:

Andreas HOLZINGER <expertise>, Frank EMMERT-STREIB <expertise>, Matthias DEHMER <expertise>, Szymon WILK <expertise>

One of the grand challenges in the life sciences are the large, complex, multi-dimensional and weakly structured data sets (big data). These increasingly enormous amounts of data require new, efficient and user-friendly solutions for data mining and knowledge discovery. The trend towards personalized medicine has resulted in an explosive growth in the amount of generated (-omics) data from various sources. A synergistic combination of methodologies and approaches of two areas offer ideal conditions towards solving these challenges: Human-Computer Interaction (HCI) and Knowledge Discovery/Data Mining (KDD), with the goal of supporting human intelligence with machine intelligence – to discover new, previously unknown insights into the data. In this special session we will focus on three promising research topics:

  1. Interactive Graph-based data mining,
  2. Interactive Entropy-based data mining, and
  3. Interactive Topological data mining.

For example, applying topological techniques to data mining and knowledge discovery is a hot and promising future topic. Topological methods can be applied to data represented by point clouds, that is, finite subsets of the n-dimensional Euclidean space. We can think of the input as a sample of some unknown space which one wishes to reconstruct and understand. One must distinguish between the ambient (embedding) dimension n, and the intrinsic dimension of the data. Whilst n is usually high, the intrinsic dimension, being of primary interest, is typically small. Therefore, knowing the intrinsic dimensionality of data is the first step towards understanding its structure. In addition to the expected results gained from basic research, benefits to evidence based medicine, treatment and public health can be achieved. Possibly, a blend of such ideas might enable to move a step forward.

This special session particularly focuses on building a small but beautiful expert group interested in working on joint projects.

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Accepted papers will be published in the conference proceedings by Springer as a volume of the series of Lecture Notes in Computer Science (LNCS) – subseries LNAI – Lecture Notes in Artificial Intelligence. Paper length is limited to 12 pages. As this special session is intended as an input session for starting cooperations amongst scientists with complementary bacckground but sharing same interests, it is planned to extend the work for a special issue in a high-end journal.

International Scientific Committee: The [HCI-KDD network of excellence] will ensure the highest possible scientific quality, each paper will be reviewed by at least three reviewers (the acceptance rate of the last special session was 30%).