The Evolution of myExperiment

The myExperiment social website for sharing scientific workflows, designed according to Web 2.0 principles, has grown to be the largest public repository of its kind. It is distinctive for its focus on sharing methods, its researcher-centric design and its facility to aggregate content into sharable 'research objects'. This evolution of myExperiment has occurred hand in hand with its users. myExperiment now supports Linked Data as a step toward our vision of the future research environment, which we categorise here as '3rd generation e-Research'

Cancer Biomarker Discovery: The Entropic Hallmark

Background: It is a commonly accepted belief that cancer cells modify their transcriptional state during the progression of the disease. We propose that the progression of cancer cells towards malignant phenotypes can be efficiently tracked using high-throughput technologies that follow the gradual changes observed in the gene expression profiles by employing Shannon's mathematical theory of communication. Methods based on Information Theory can then quantify the divergence of cancer cells' transcriptional profiles from those of normally appearing cells of the originating tissues. The relevance of the proposed methods can be evaluated using microarray datasets available in the public domain but the method is in principle applicable to other high-throughput methods. Methodology/Principal Findings: Using melanoma and prostate cancer datasets we illustrate how it is possible to employ Shannon Entropy and the Jensen-Shannon divergence to trace the transcriptional changes progression of the disease. We establish how the variations of these two measures correlate with established biomarkers of cancer progression. The Information Theory measures allow us to identify novel biomarkers for both progressive and relatively more sudden transcriptional changes leading to malignant phenotypes. At the same time, the methodology was able to validate a large number of genes and processes that seem to be implicated in the progression of melanoma and prostate cancer. Conclusions/Significance: We thus present a quantitative guiding rule, a new unifying hallmark of cancer: the cancer cell's transcriptome changes lead to measurable observed transitions of Normalized Shannon Entropy values (as measured by high-throughput technologies). At the same time, tumor cells increment their divergence from the normal tissue profile increasing their disorder via creation of states that we might not directly measure. This unifying hallmark allows, via the the Jensen-Shannon divergence, to identify the arrow of time of the processes from the gene expression profiles, and helps to map the phenotypical and molecular hallmarks of specific cancer subtypes. The deep mathematical basis of the approach allows us to suggest that this principle is, hopefully, of general applicability for other diseases

Androgen receptor associated-protein 70 (ARA70) expression in breast cancer, possible relationship with her2/neu overexpression

grantor: University of TorontoAndrogens have been postulated to have a protective effect in breast cancer and expression of androgen receptors (AR) is considered a favorable prognostic indicator. AR associated protein-70 (ARA70), a putative coactivator with a predilection for the AR, can enhance the transcriptional action of the AR up to 10-fold. Overexpression of the 'her'2/' neu' proto-oncogene also amplifies AR action in transfected prostate cancer cells. To determine if ARA70, AR, and 'her'2/' neu' expression is associated in breast cancer, AR and ARA70 protein was examined in 41 cases of invasive ductal carcinoma. To execute this study, a polyclonal antibody to ARA70 was produced and validated. Immunohistochemistry indicated that ARA70 is expressed in normal breast epithelium and that this expression is diminished or lost in 46.3% of the tumors with a trend toward a greater loss in erbB-2 positive tumors. Western blot analyses also raise the possibility that ARA70 isoforms may be expressed in the cancer cells.M.Sc

Opcode Frequency Based Malware Detection Using Hybrid Classifiers

The world we live in is the world of technology. Almost every sector in business or organizations makes use of computers for storing information. Some information is private and sensitive to the users. It may include business ideas, government papers, bank account passwords etc. The malicious software is programmed by cyber-criminals to get a hold of this information. This can result in huge losses for the organization or an individual. There are traditional anti-viruses available in the market. However, the complexity and variety of malware are increasing day-by-day. They can bypass the traditional signature-based antimalware. Research has been focused on Machine learning to find a solution for this advanced malware. There are research conducted to detect a malware executable using opcodes. Opcodes are a part of machine level language that instructs the processor hardware what functions to perform. This thesis makes use of a machine learning-based hybrid algorithm to boost the accuracy of malicious file detection. The thesis developed makes use of opcodebased features