Seminar Wissensentdeckung in der Bioinformatik II (S2)

 Termine und Räume
Vorbesprechung: Mittwoch, 28.04. 12.00 Uhr in Raum Z613.
 
 Dozenten:
Thomas Gabriel
Michael Berthold
 
 Themengebiet:
Angewandte Informatik
 
 Adressaten:
Studierende des Information Engineering im Bachelor-Vertiefungsstudium und Masterstudium.
 
 Inhalt:
Das Seminar beschäftigt sich mit ausgewählten Themen der Bioinformatik. Dazu erhält jeder Teilnehmer ein individuelles Thema und hält zu definierten Zeitpunkten Rücksprache mit seinem Betreuer. In der ersten Veranstaltung werden entsprechende wissenschaftliche Aufsätze ausgegeben. Zu den übrigen Terminen finden die Vorträge der Teilnehmer statt, an die sich jeweils eine Diskussion über Form und Inhalt anschließt.
Die Themengebiete werden noch bekannt gegeben.
 
 Lernziele:
Selbständiges wissenschaftliches Arbeiten am Beispiel eines Themas der Bioinformatik. Die Studierenden sollen in die Lage versetzt werden, das Thema zu erarbeiten, verständlich zu präsentieren und angemessen niederzuschreiben. Dazu gehören insbesondere der sorgfältige Umgang mit Literatur, Vortragstechniken, Verwendung von Präsentationsmedien und wissenschaftliches Schreiben.
 
 Literatur:

Einführung und Movitation

Systematic variation in gene expression patterns in human cancer cell lines
Douglas T. Ross et al., 2000.

Abstract. We used cDNA microarrays to explore the variation in expression of approximately 8,000 unique genes among the 60 cell lines used in the National Cancer Institute’s screen for anti-cancer drugs. Classification of the cell lines based solely on the observed patterns of gene expression revealed a correspondence to the ostensible origins of the tumours from which the cell lines were derived. The consistent relationship between the gene expression patterns and the tissue of origin allowed us to recognize outliers whose previous classification appeared incorrect. Specific features of the gene expression patterns appeared to be related to physiological properties of the cell lines, such as their doubling time in culture, drug metabolism or the interferon response. Comparison of gene expression patterns in the cell lines to those observed in normal breast tissue or in breast tumour specimens revealed features of the expression patterns in the tumours that had recognizable counterparts in specific cell lines, reflecting the tumour, stromal and inflammatory components of the tumour tissue. These results provided a novel molecular characterization of this important group of human cell lines and their relationships to tumours in vivo. pdf.

Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks
Javed Khan et al., 2001.

Abstract. The purpose of this study was to develop a method of classifying cancers to specific diagnostic categories based on their gene expression signatures using artificial neural networks (ANNs). We trained the ANNs using the small, round blue-cell tumors (SRBCTs) as a model. These cancers belong to four distinct diagnostic categories and often present diagnostic dilemmas in clinical practice. The ANNs correctly classified all samples and identified the genes most relevant to the classification. Expression of several of these genes has been reported in SRBCTs, but most have not been associated with these cancers. To test the ability of the trained ANN models to recognize SRBCTs, we analyzed additional blinded samples that were not previously used for the training procedure, and correctly classified them in all cases. This study demonstrates the potential applications of these methods for tumor diagnosis and the identification of candidate targets for therapy. pdf.

Computational Analysis of Microarray Data
John Quackenbush, 2001.

Abstract. Microarray experiments are providing unprecedented quantities of genome-wide data on gene-expression patterns. Although this technique has been enthusiastically developed and applied in many biological contexts, the management and analysis of the millions of data points that result from these experiments has received less attention. Sophisticated computational tools are available, but the methods that are used to analyse the data can have a profound influence on the interpretation of the results. A basic understanding of these computational tools is therefore required for optimal experimental design and meaningful data analysis. pdf.

Machine Learning Methods Applied to DNA Microarray Data Can Improve the Diagnosis of Cancer
Eric Bair et al., 2003.

Abstract. The morbidity rate of cancer victims varies greatly for similar patiets who receive similar treatments. It is hypothesized that this variation can be explained by the genetic heterogeneity of the disease. DNA Microarrays, which can simultaneously measure the expression level of thousands of different genes, have been successfully used to identify such genetic differences. However, microarray data typically has large number of feature and relatively few observations, meaning that conventional machine learning tools can fail when applied to such data. We describe a novel procedure called "nearest shrunken centroids" that has been successfully detected clinically relevant genetic differences in cancer patients. This procedure has the potential to become a powerful tool for diagnosing and treating cancer. pdf.
 
 Leistungsnachweise:
Mündlicher Vortrag von ca. 30min und schriftliche Ausarbeitung von ca. 10-20 Seiten (wahlweise auf deutsch oder englisch) zum jeweiligen Thema; Anwesenheit und aktive Teilnahme an den Vortragsdiskussionen.
 
 Leistungspunkte:
Bei Bestehen des Leistungsnachweises können 3 Punkte erworben werden.
 
 Pool:
Studierende, die das Seminar besuchen, tragen sich bitte in die Gruppe s_bioinf_S04 ein. Eine Anleitung dazu findet man hier.
 
 Angebot im Lehrexport:
Mathematik, Nebenfach oder Schwerpunkt Informatik
Physik, Wahlpflichtfach Informatik
Nebenfach Informatik in einem Magisterstudiengang