Viktors Berstis

Virtual Microscopy and Grid-Enabled Decision Support for Large-Scale Analysis of Imaged Pathology Specimens

Breast cancer accounts for about 30% of all cancers and 15% of cancer deaths in women. Advances in computer-assisted analysis hold promise for classifying subtypes of disease and improving prognostic accuracy. We introduce a grid-enabled decision support system for performing automatic analysis of imaged breast tissue microarrays. To date, we have processed more than 1 00 000 digitized specimens (1200 times 1200 pixels each) on IBMs World Community Grid (WCG). As a part of the Help Defeat Cancer (HDC) project, we have analyzed that the data returned from WCG along with retrospective patient clinical profiles for a subset of 3744 breast tissue samples, and have reported the results in this paper. Texture-based features were extracted from the digitized specimens, and isometric feature mapping was applied to achieve nonlinear dimension reduction. Iterative prototyping and testing were performed to classify several major subtypes of breast cancer. Overall, the most reliable approach was gentle AdaBoost using an eight-node classification and regression tree as the weak learner. Using the proposed algorithm, a binary classification accuracy of 89% and the multiclass accuracy of 80% were achieved. Throughout the course of the experiments, only 30% of the dataset was used for training.

The Proteome Folding Project: Proteome-scale prediction of structure and function

The incompleteness of proteome structure and function annotation is a critical problem for biologists and, in particular, severely limits interpretation of high-throughput and next-generation experiments. We have developed a proteome annotation pipeline based on structure prediction, where function and structure annotations are generated using an integration of sequence comparison, fold recognition, and grid-computing-enabled de novo structure prediction. We predict protein domain boundaries and three-dimensional (3D) structures for protein domains from 94 genomes (including human, Arabidopsis, rice, mouse, fly, yeast, Escherichia coli, and worm). De novo structure predictions were distributed on a grid of more than 1.5 million CPUs worldwide (World Community Grid). We generated significant numbers of new confident fold annotations (9% of domains that are otherwise unannotated in these genomes). We demonstrate that predicted structures can be combined with annotations from the Gene Ontology database to predict new and more specific molecular functions.

Security and protection of data in the IBM System/38

The IBM System/38 machine architecture provides data security through addressability control and protection domains while minimizing overhead for this service. This paper describes the addressing mechanism, basic data organization and process structure of the System/38. These constructs are fundamental to the operation of the security mechanism. A description is given of security in the architecture and how it can be extended.

The V compiler: automatic hardware design

The V language describes VLSI systems concisely through the use of sequential algorithmic descriptions. Because V includes high-level constructs such as queues, asynchronous calls, and cycle-blocks, designs are more readily described and optimized into efficient hardware implementations. The implementations can then be tuned for space, time, or other objectives using annotations. From the input description, the V compiler generates both a register-transfer-level specification and a software simulator. Thus, a single description is suitable for both functional simulation and input to logic synthesis. The author describes parsing. scheduling, and resource sharing using the V compiler. He discusses synthesis and simulation, annotations, and high-level constructs.<<ETX>>