Mini Workshop: Combinatorial Optimization for Personalized Medicine

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When?UNI-Logo_RGB_01
10:00am – 12:00am
October 1, 2014

Where?
Sky Lounge, 12th Floor
Faculty of Business, Economics, and Statistics
University of Vienna
Oskar-Morgenstern-Platz 1
1090 Vienna, Austria

SPEAKERS:
Regina Berretta, Deputy Head of School of Electrical Engineering and Computer Science,
Priority Research Centre for Bioinformatics, Biomarker Discovery and Information Based Medicine,
Information Based Medicine Program, Hunter Medical Research Institute,
The University of Newcastle, Australia
Pablo Moscato, School of Electrical Engineering and Computer Science,
Priority Research Centre for Bioinformatics, Biomarker Discovery and Information Based Medicine,
Information Based Medicine Program, Hunter Medical Research Institute,
The University of Newcastle, Australia

Workshop Program:

10:00 – 11:00 Regina Berretta: Combinatorial optimisation models for analysing biological data sets
Abstract: This talk will present combinatorial optimisation models and algorithmic techniques that have been developed to analyse large datasets. First, the presentation will focus on an approach, based on a combinatorial optimisation problem (called the (α,β)-k-Feature Set Problem) to deal with the problem of selecting groups of features, such as genes, that discriminate between different existing classes. We will illustrate the application of these models using different variations of the model in several datasets.
Next, the presentation will illustrate how a classical and well-known combinatorial optimisation problem; the Quadratic Assignment Problem (QAP), is employed as a mathematical model to produce a visualization of a data set, based on the relationships between the elements in the data set. The visualization method can also incorporate the results of a clustering algorithm to facilitate the process of data analysis.

11:00 – 12:00 Pablo Moscato: Personalized Information-based Medicine: Huge challenges, massive opportunities and some lessons learned
Abstract: A recent report from the McKinsey Global Institute highlights the top six disruptive technologies with highest economic impact: mobile internet, automation of knowledge work, Internet of Things, Cloud, Advanced robotics and Autonomous and near-autonomous vehicles. A close seventh is at the core of information-based medicine, next-generation genomics. These seven technologies account for an estimated value which is at least 28 trillion US dollar a year.
All of them share with Information-based Medicine the need of analyzing large datasets, with “Big Data” being the current buzzword. As such, the need of querying a large variety of digital data and the use of artificial-intelligence and optimization software to find novel insights is not considered a separate technology, but a omnipresent requirement across all technologies.
Personalized Medicine aims at putting the best interests of the patient/individual, at the centre of all medical decisions, institutional practices, and/or drugs and treatments that necessarily be “tailored” to the individual profile. Clearly next-generation genomics is pertinent here, but the automation of knowledge work will also prove vital.
These two perspectives for the future of Medicine should contribute to each other. The novel technologies generate an ocean of data, but without strategic approaches for knowledge reuse they do not deliver for the promise. The huge perceived challenges generally involve large optimization. However, the implicit challenge is the development of new mathematical models that contemporize the needs of personalized medicine, who aims at the best diagnostic and treatment, and Information-based Medicine, with the needs of institutions/governments that aim at delivering the best health policies while minimizing global intervention costs operating under budget constraints.

The workshop program can be downloaded here

Sponsored by:
Austrian Research Fund (FWF)
Faculty of Business, Economics, and Statistics, University of Vienna
Austrian Society of Operations Research

Organized by:
Markus Leitner, University of Vienna
Ivana Ljubic, University of Vienna