A Critical Perspective On Microarray Breast Cancer Gene Expression Profiling

Microarrays offer biologists an exciting tool that allows the simultaneous assessment of gene expression levels for thousands of genes at once. At the time of their inception, microarrays were hailed as the new dawn in cancer biology and oncology practice with the hope that within a decade diseases like breast cancer would be solved. Various...

Hypergeometric analysis of tiling-array and sequence data: Detection and interpretation of peaks

Probing protein-deoxyribonucleic acid (DNA) is gaining popularity as it sheds light on molecular mechanisms that regulate the expression of genes. Currently, tiling-arrays and next-generation sequencing technology can be used to measure these interactions. Both methods generate a signal over the genome in which contiguous regions of peaks on the...

RCytoscape: Tools for exploratory network analysis

Background: Biomolecular pathways and networks are dynamic and complex, and the perturbations to them which cause disease are often multiple, heterogeneous and contingent. Pathway and network visualizations, rendered on a computer or published on paper, however, tend to be static, lacking in detail, and ill-equipped to explore the variety and...

Multicore Architectures for Bioinformatics Applications

In this dissertation, we address the challenges of performance scaling for bioinformatics applications on multicore architectures. In particular, we focus on sequence alignment, one of the fundamental tasks in bioinformatics. Due to the exponential growth of biological databases and the computational complexity of the algorithms used, high...

Finding cancer genes in copy number data and insertional mutagenesis data

Cancer is a genetic disease. Step-wise alteration of genes that have a normal function in the cell can lead to the transformation of a healthy cell into a malignant cancer cell. Cancer genes provide several traits to the cell that allow it to become malignant. These traits have been researched for many years, and currently one knows quite well...

Computational approaches for dissecting cancer pathways from insertional mutagenesis data

Advances in the field of molecular biology have resulted in a decent understanding of the causes for and mechanisms through which healthy cells can develop into cancer cells. It is, for instance, well established that cancer is caused by mutation of so-called cancer genes. That said, current knowledge on exactly which genes can function as a...

Exploiting noisy and incomplete biological data for prediction and knowledge discovery

In modern molecular biology, the vast amount of experimental data enables us to obtain more comprehensive understanding of cellular activities, from transcription to metabolism. However, due to the inherent complexity of the cell and the various limitations of the measuring techniques, these data are often noisy and incomplete. Therefore,...

Vertical integration of high-throughput measurements to derive functional and regulatory interactions in S. cerevisiae

Bioinformatics concerns itself with developing data-driven tools for extracting biological information from high-throughput datasets (e.g. gene expression data, protein interaction data). Until recently, most of these tools focus on analyzing a single source of data. However, all high-throughput measurements are noisy and incomplete. Therefore,...

An Efficient and High Performance Linear Recursive Variable Expansion Implementation of the Smith-Waterman Algorithm

In this paper, we present an efficient and high performance linear recursive variable expansion (RVE) implementation of the Smith-Waterman (S-W) algorithm and compare it with a traditional linear systolic array implementation. The results demonstrate that the linear RVE implementation performs up to 2.33 times better than the traditional linear...

Inferring the influence of cultivation parameters on transcriptional regulation

Exactly how an organism adapts its transcriptional program in response to intra- and extracellular signals remains elusive. Development of computational approaches that use the large amounts of diverse intracellular data to unravel the cell's transcriptional program is one of today's main challenges in bioinformatics research. This thesis...