Robust Likelihood-Based Survival Modeling with Microarray Data

Gene expression data can be associated with various clinical outcomes. In particular, these data can be of importance in discovering survival-associated genes for medical applications. As alternatives to traditional statistical methods, sophisticated methods and software programs have been developed to overcome the high-dimensional difficulty of microarray data. Nevertheless, new algorithms and software programs are needed to include practical functions such as the discovery of multiple sets of survival-associated genes and the incorporation of risk factors, and to use in the R environment which many statisticians are familiar with. For survival modeling with microarray data, we have developed a software program (called rbsurv) which can be used conveniently and interactively in the R environment. This program selects survival-associated genes based on the partial likelihood of the Cox model and separates training and validation sets of samples for robustness. It can discover multiple sets of genes by iterative forward selection rather than one large set of genes. It can also allow adjustment for risk factors in microarray survival modeling. This software package, the rbsurv package, can be used to discover survival-associated genes with microarray data conveniently.

The O’Neill Institute for National and Global Health Law: Discovering Innovative Solutions for the Most Pressing Health Problems Facing the Nation and the World

The connection between health and an individual’s ability to function in society, as well as the importance of health to a society’s economic, political, and social wellbeing necessitates finding innovative solutions to the world’s most pressing health problems. The O’Neill Institute for National and Global Health Law at Georgetown University seeks to demonstrate the role that academia can play in addressing complex national and global health problems in a comprehensive, evidence-based, intellectually-rigorous, and nonpartisan manner. The O’Neill Institute currently has three research programs: global health law, national health law, and the center for disease prevention and outcomes. Projects within these programs examine a broad range of health law and policy issues, such as global health governance, global tobacco control, health worker migration, emergency preparedness, national and Chinese health reform, HIV and AIDS issues, food safety, and personalized medicine. These projects merge the scholarly capacity within the institute with the resources of its partners, which include the World Health Organization, World Bank, the Bill & Melinda Gates Foundation, the U.S. Centers for Disease Control and Prevention, and the Campaign for Tobacco Free Kids. Additionally, the faculty and fellows of the O’Neill Institute regularly produce high-level scholarship and engage in teaching offering multi-disciplinary course offerings and innovative graduate degree programs. URL: http://www.law.georgetown.edu/oneillinstitute/documents/2010-03-09_oneill-solutions.pdf; http://mjlst.umn.edu/uploads/Pf/V1/PfV1QhiCT6lUOsv1AqDTCA/111_gostin.pdf

Veni Vidi Vici, A Three-Phase Scenario For Parameter Space Analysis in Image Analysis and Visualization

Automatic analysis of the enormous sets of images is a critical task in life sciences. This faces many challenges such as: algorithms are highly parameterized, significant human input is intertwined, and lacking a standard meta-visualization approach. This paper proposes an alternative iterative approach for optimizing input parameters, saving time by minimizing the user involvement, and allowing for understanding the workflow of algorithms and discovering new ones. The main focus is on developing an interactive visualization technique that enables users to analyze the relationships between sampled input parameters and corresponding output. This technique is implemented as a prototype called Veni Vidi Vici, or "I came, I saw, I conquered." This strategy is inspired by the mathematical formulas of numbering computable functions and is developed atop ImageJ, a scientific image processing program. A case study is presented to investigate the proposed framework. Finally, the paper explores some potential future issues in the application of the proposed approach in parameter space analysis in visualization

Redemption in the Dean’s Office

Academic deans, like all leaders trying to create principled-based positions, are subject to the hubris of power and the often divergent demands of many stakeholders. Dean Clements discusses how a leader’s failures, as well as successes, are all necessary to properly cultivate this role

MEMOFinder: combining _de_ _novo_ motif prediction methods with a database of known motifs

*Background:* Methods for finding overrepresented sequence motifs are useful in several key areas of computational biology. They aim at detecting very weak signals responsible for biological processes requiring robust sequence identification like transcription-factor binding to DNA or docking sites in proteins. Currently, general performance of the model-based motif-finding methods is unsatisfactory; however, different methods are successful in different cases. This leads to the practical problem of combining results of different motif-finding tools, taking into account current knowledge collected in motif databases.
*Results:* We propose a new complete service allowing researchers to submit their sequences for analysis by four different motif-finding methods for clustering and comparison with a reference motif database. It is tailored for regulatory motif detection, however it allows for substantial amount of configuration regarding sequence background, motif database and parameters for motif-finding methods.
*Availability:* The method is available online as a webserver at: http://bioputer.mimuw.edu.pl/software/mmf/. In addition, the source code is released on a GNU General Public License

Next Generation Evaluation: Embracing Complexity, Connectivity, and Change

This Learning Brief draws from literature and research, as well as more than a dozen interviews with foundation leaders, evaluation practitioners, and social sector thought leaders, with the intention of starting the conversation in the field around Next Generation Evaluation characteristics and approaches