Visualisation of bioinformatics datasets

Analysing the molecular polymorphism and interactions of DNA, RNA and proteins is of fundamental importance in biology. Predicting functions of polymorphic molecules is important in order to design more effective medicines. Analysing major histocompatibility complex (MHC) polymorphism is important for mate choice, epitope-based vaccine design and transplantation rejection etc. Most of the existing exploratory approaches cannot analyse these datasets because of the large number of molecules with a high number of descriptors per molecule. This thesis develops novel methods for data projection in order to explore high dimensional biological dataset by visualising them in a low-dimensional space. With increasing dimensionality, some existing data visualisation methods such as generative topographic mapping (GTM) become computationally intractable. We propose variants of these methods, where we use log-transformations at certain steps of expectation maximisation (EM) based parameter learning process, to make them tractable for high-dimensional datasets. We demonstrate these proposed variants both for synthetic and electrostatic potential dataset of MHC class-I. We also propose to extend a latent trait model (LTM), suitable for visualising high dimensional discrete data, to simultaneously estimate feature saliency as an integrated part of the parameter learning process of a visualisation model. This LTM variant not only gives better visualisation by modifying the project map based on feature relevance, but also helps users to assess the significance of each feature. Another problem which is not addressed much in the literature is the visualisation of mixed-type data. We propose to combine GTM and LTM in a principled way where appropriate noise models are used for each type of data in order to visualise mixed-type data in a single plot. We call this model a generalised GTM (GGTM). We also propose to extend GGTM model to estimate feature saliencies while training a visualisation model and this is called GGTM with feature saliency (GGTM-FS). We demonstrate effectiveness of these proposed models both for synthetic and real datasets. We evaluate visualisation quality using quality metrics such as distance distortion measure and rank based measures: trustworthiness, continuity, mean relative rank errors with respect to data space and latent space. In cases where the labels are known we also use quality metrics of KL divergence and nearest neighbour classifications error in order to determine the separation between classes. We demonstrate the efficacy of these proposed models both for synthetic and real biological datasets with a main focus on the MHC class-I dataset.

Ozone and climate:scientific consensus and leadership

This article compares the cases of ozone layer protection and climate change. In both cases, scientific expertise has played a comparatively important role in the policy process. The author argues that against conventional assumptions, scientific consensus is not necessary to achieve ambitious political goals. However, the architects of the Intergovernmental Panel on Climate Change operated under such assumptions. The author argues that this is problematic both from a theoretical viewpoint and from empirical evidence. Contrary to conventional assumptions, ambitious political regulations in the ozone case were agreed under scientific uncertainty, whereas the negotiations on climate change were much more modest albeit based on a large scientific consensus. On the basis of a media analysis, the author shows that the creation of a climate of expectation plus pressure from leader countries is crucial for success. © 2006 Sage Publication.

Using an experiential business game to stimulate sustainable thinking in marketing education

Recent years have seen a significant increase in the importance of environmental protection and sustainability to consumers, policy makers, and society in general. Reflecting this, most organizations are at least aware of this new agenda and wish to be seen as taking steps to improve behaviors in this regard. However, there appears to be a gap between this evolving agenda and the comparatively low level of knowledge that marketing managers actually have of the environmental impact of their own functional decisions. We suggest that this low knowledge level may be due, in part, to the marketplace focus of foundational marketing educational programs, and we attempt to show how broadening the horizons of marketing courses can help students (i.e., future managers) more deeply understand the environmental consequences of their actions. We demonstrate the use of a novel business game, based on the Life Cycle Assessment method, as the foundational cornerstone for the development of a broad understanding of the environmental impact of marketing decisions and actions for the entire life cycle of a product—from raw material extraction to ultimate disposal. The results of an empirical study show that this approach increases students’ appreciation for, and understanding of, these fundamental environmental sustainability concepts.

Short-horizon excess returns and exchange rate and interest rate effects

stocks. We examine the effects of foreign exchange (FX) and interest rate changes on the excess returns of U.S. stocks, for short-horizons of 1-40 days. Our new evidence shows a tendency for the volatility of both excess returns and FX rate changes to be negatively related with FX rate and interest rate effects. Both the number of firms with significant FX rate and interest rate effects and the magnitude of their exposures increase with the length of the return horizon. Our finding seems inconsistent with the view that firms hedge effectively at short-return horizons.

Inflation forecasting, relative price variability and skewness

This article presents out-of-sample inflation forecasting results based on relative price variability and skewness. It is demonstrated that forecasts on long horizons of 1.5-2 years are significantly improved if the forecast equation is augmented with skewness. © 2010 Taylor & Francis.

New horizons in mouse immunoinformatics:reliable in silico prediction of mouse class I histocompatibility major complex peptide binding affinity

Quantitative structure–activity relationship (QSAR) analysis is a main cornerstone of modern informatic disciplines. Predictive computational models, based on QSAR technology, of peptide-major histocompatibility complex (MHC) binding affinity have now become a vital component of modern day computational immunovaccinology. Historically, such approaches have been built around semi-qualitative, classification methods, but these are now giving way to quantitative regression methods. The additive method, an established immunoinformatics technique for the quantitative prediction of peptide–protein affinity, was used here to identify the sequence dependence of peptide binding specificity for three mouse class I MHC alleles: H2–Db, H2–Kb and H2–Kk. As we show, in terms of reliability the resulting models represent a significant advance on existing methods. They can be used for the accurate prediction of T-cell epitopes and are freely available online (http://www.jenner.ac.uk/MHCPred).