NSC Seminars on High Performance Computing
Please click here for information on forthcoming
seminars.
List of old seminars
Detailed Programme
Title: Atomic-Scale
Computational Materials Science
Speaker: Prof. Göran Wahnström, Chalmers University
of Technology and Göteborgs University
Time: Tuesday, Nov. 28, 2000,
at 13.15-14.00
Abstract
With the rapid development of advanced technologies materials science is
confronted with many challenging problems. Atomic-scale computational materials
science has become one important tool in improving our understanding and
in facilitating the design of new materials. Quantum mechanical calculations
provide a means to describe materials on a truly microscopic level. In
principle the laws governing the behaviour of the microscopic constituents
of a material, the electrons and atomic nuclei, are well known: it is sufficient
to solve the Schrödinger equation. However, besides in some trivial
cases this is a formidable numerical task.
I would like to present common approximations and numerical techniques
used in solving the Schrödinger equation in the field of real materials.
I will use vacancies in aluminium as an illustrative example. Aluminium
has often been used as a test case for developing the computational methodology
and, to some extent, it can be viewed as the "hydrogen atom" of computational
materials science.
Title: Mining
Microarray Data: Predicting Gene Function from Gene Expressions and Background
Knowledge -- State-of-the-art and Opportunities for High Performance Computing
Speaker: Prof. Jan Komorowski
Time: Tuesday, Dec. 12, 2000,
at 16.15-17.00
Abstract
The majority of states in health or disease is most likely controlled by
hundreds of genes. Until recently, molecular biomedicine could study
one or very few genes in parallel. With the advent of the so-called
high throughput micro-array technology it is now possible to observe the
levels of activity in literally thousands of genes. At the same time, genome
mapping projects such as, for instance, the Human Genome Initiative, which
is an international research program for the creation of detailed genetic
and physical maps of the human genome, generate enormous quantities of
data. For the human genome, there are approximately 100K genes out
of which ca 5K genes are known. The major goal of Functional Genomics
is an assignment of function to genes. State-of-the-art in Functional
Genomics is the use of unsupervised learning methods. Our multidisciplinary
team has developed a supervised learning method for inducing predictive
rule models for functional classification of gene expressions from microarray
hybridization experiments.
In this talk I show how we have resolved some of the thorny issues in
the functional classification of gene expressions. Then, I identify
potential research and application areas for High Performance Computing.
Nota bene. The talk is self-contained with respect to the knowledge
of molecular biology.
