26.06.16 - 01.07.16, Seminar 16261

Integration of Expert Knowledge for Interpretable Models in Biomedical Data Analysis

Diese Seminarbeschreibung wurde vor dem Seminar auf unseren Webseiten veröffentlicht und bei der Einladung zum Seminar verwendet.

Motivation

New technologies in various fields of biomedical research have led to a dramatic increase of the amount of electronic data that is available. Not only is the number of patients or amount of disease specific data increasing, but so is the structural complexity of the data, in terms of its dimensionality, multi-modality and inhomogeneity.

As an important example, the sequencing of the human genome ushered in the era of computational biology and translational medicine, with the promise of novel insights into cancer and other diseases, better therapy options, more effective drugs and improved clinical outcome. The development of novel drugs can be directly linked to insights gained as a result of this genomic revolution.

A significant problem, recognized by both the bio-medical and computational community, is the lack of coordination among researchers in these disparate communities, who do not often have opportunities to share ideas and insights to plan and develop effective collaborative research projects.

The aim of the seminar is to bring together researchers who develop, investigate, or apply methods of machine learning and statistics in biomedical data analysis with experts from knowledge representation and integration as well as bio-medical experts with a strong interest in computation and interpretable models.

In order to advance this important field of research, it is vital to facilitate interdisciplinary exchange of ideas and an intense dialogue. The focus of the seminar will be on the development and optimization of methods and processing pipelines, which offer efficient solutions for structured data analysis appropriate for a wide range of bio-medical application scenarios. In particular, the systematic incorporation of domain knowledge into the approaches will be at the center of interest.

The seminar will be centered about six main areas of interest:

  1. Structured, inhomogeneous and multi-modal data
  2. Feature selection and biomarker detection
  3. Diagnosis and classification problems
  4. Generative models of bio-medical processes
  5. Visual analytics and data mining
  6. Big data mining for clinical impact

Emphasis will be on discussion groups formed by the participants with few introductory talks about recent developments and challenges in bio-medical data analysis and knowledge integration. These groups will report their work in plenary sessions as to ensure that their conclusions are disseminated to all participants.