Predictive gene lists for breast cancer prognosis: a topographic visualisation study

Background: The controversy surrounding the non-uniqueness of predictive gene lists (PGL) of small selected subsets of genes from very large potential candidates as available in DNA microarray experiments is now widely acknowledged 1. Many of these studies have focused on constructing discriminative semi-parametric models and as such are also subject to the issue of random correlations of sparse model selection in high dimensional spaces. In this work we outline a different approach based around an unsupervised patient-specific nonlinear topographic projection in predictive gene lists. Methods: We construct nonlinear topographic projection maps based on inter-patient gene-list relative dissimilarities. The Neuroscale, the Stochastic Neighbor Embedding(SNE) and the Locally Linear Embedding(LLE) techniques have been used to construct two-dimensional projective visualisation plots of 70 dimensional PGLs per patient, classifiers are also constructed to identify the prognosis indicator of each patient using the resulting projections from those visualisation techniques and investigate whether a-posteriori two prognosis groups are separable on the evidence of the gene lists. A literature-proposed predictive gene list for breast cancer is benchmarked against a separate gene list using the above methods. Generalisation ability is investigated by using the mapping capability of Neuroscale to visualise the follow-up study, but based on the projections derived from the original dataset. Results: The results indicate that small subsets of patient-specific PGLs have insufficient prognostic dissimilarity to permit a distinction between two prognosis patients. Uncertainty and diversity across multiple gene expressions prevents unambiguous or even confident patient grouping. Comparative projections across different PGLs provide similar results. Conclusion: The random correlation effect to an arbitrary outcome induced by small subset selection from very high dimensional interrelated gene expression profiles leads to an outcome with associated uncertainty. This continuum and uncertainty precludes any attempts at constructing discriminative classifiers. However a patient's gene expression profile could possibly be used in treatment planning, based on knowledge of other patients' responses. We conclude that many of the patients involved in such medical studies are intrinsically unclassifiable on the basis of provided PGL evidence. This additional category of 'unclassifiable' should be accommodated within medical decision support systems if serious errors and unnecessary adjuvant therapy are to be avoided.

Guiding local regression using visualisation

Solving many scientific problems requires effective regression and/or classification models for large high-dimensional datasets. Experts from these problem domains (e.g. biologists, chemists, financial analysts) have insights into the domain which can be helpful in developing powerful models but they need a modelling framework that helps them to use these insights. Data visualisation is an effective technique for presenting data and requiring feedback from the experts. A single global regression model can rarely capture the full behavioural variability of a huge multi-dimensional dataset. Instead, local regression models, each focused on a separate area of input space, often work better since the behaviour of different areas may vary. Classical local models such as Mixture of Experts segment the input space automatically, which is not always effective and it also lacks involvement of the domain experts to guide a meaningful segmentation of the input space. In this paper we addresses this issue by allowing domain experts to interactively segment the input space using data visualisation. The segmentation output obtained is then further used to develop effective local regression models.

Risk assessment applied to comsumer products with reference to CE marking machines for use at work

New Approach’ Directives now govern the health and safety of most products whether destined for workplace or domestic use. These Directives have been enacted into UK law by various specific legislation principally relating to work equipment, machinery and consumer products. This research investigates whether the risk assessment approach used to ensure the safety of machinery may be applied to consumer products. Crucially, consumer products are subject to the Consumer Protection Act (CPA) 1987, where there is no direct reference to “assessing risk”. This contrasts with the law governing the safety of products used in the workplace, where risk assessment underpins the approach. New Approach Directives are supported by European harmonised standards, and in the case of machinery, further supported by the risk assessment standard, EN 1050. The system regulating consumer product safety is discussed, its key elements identified and a graphical model produced. This model incorporates such matters as conformity assessment, the system of regulation, near miss and accident reporting. A key finding of the research is that New Approach Directives have a common feature of specifying essential performance requirements that provide a hazard prompt-list that can form the basis for a risk assessment (the hazard identification stage). Drawing upon 272 prosecution cases, and with thirty examples examined in detail, this research provides evidence that despite the high degree of regulation, unsafe consumer products still find their way onto the market. The research presents a number of risk assessment tools to help Trading Standards Officers (TSOs) prioritise their work at the initial inspection stage when dealing with subsequent enforcement action.

Guiding local regression using visualisation

Solving many scientific problems requires effective regression and/or classification models for large high-dimensional datasets. Experts from these problem domains (e.g. biologists, chemists, financial analysts) have insights into the domain which can be helpful in developing powerful models but they need a modelling framework that helps them to use these insights. Data visualisation is an effective technique for presenting data and requiring feedback from the experts. A single global regression model can rarely capture the full behavioural variability of a huge multi-dimensional dataset. Instead, local regression models, each focused on a separate area of input space, often work better since the behaviour of different areas may vary. Classical local models such as Mixture of Experts segment the input space automatically, which is not always effective and it also lacks involvement of the domain experts to guide a meaningful segmentation of the input space. In this paper we addresses this issue by allowing domain experts to interactively segment the input space using data visualisation. The segmentation output obtained is then further used to develop effective local regression models.

Performance management and high performance work practices in emerging markets

In this chapter, we discuss performance management systems (PMSs) and high performance work systems (HPWSs) in emerging economies. We start by discussing PMSs, with specific emphasis on PMSs in global organizations. We follow this up with an introduction of HPWSs, and then discuss PMSs and HPWSs in emerging economies. While the list of emerging economies keeps changing, and is rather long, as one might expect, in this chapter we have concentrated on five key emerging economies – China, India, Mexico, South Korea, and Turkey. Performance management is the process through which organizations set goals, determine standards, assign and evaluate work, coach and give feedback, and distribute rewards (Fletcher, 2001). In this connection, organizations all over the world face the challenge of how best to manage performance, including finding ways to motivate employees to sustain high levels of performance. In other words, organizations must develop and implement PMSs that are appropriate for their environment in such a way that high levels of performance can be achieved and sustained over time (DeNisi, Varma and Budhwar, 2008). While all organizations need to address these issues, the way a firm decides to go about addressing these issues is dependent on its location and context. In other words, differences in local norms, culture, law, and technology, make it critical that organizations develop and/or adapt techniques, policies and practices that are appropriate to the setting (see for example, Hofstede, 1993).

Visualisation and browsing of image databases

In this chapter we provide a comprehensive overview of the emerging field of visualising and browsing image databases. We start with a brief introduction to content-based image retrieval and the traditional query-by-example search paradigm that many retrieval systems employ. We specify the problems associated with this type of interface, such as users not being able to formulate a query due to not having a target image or concept in mind. The idea of browsing systems is then introduced as a means to combat these issues, harnessing the cognitive power of the human mind in order to speed up image retrieval.We detail common methods in which the often high-dimensional feature data extracted from images can be used to visualise image databases in an intuitive way. Systems using dimensionality reduction techniques, such as multi-dimensional scaling, are reviewed along with those that cluster images using either divisive or agglomerative techniques as well as graph-based visualisations. While visualisation of an image collection is useful for providing an overview of the contained images, it forms only part of an image database navigation system. We therefore also present various methods provided by these systems to allow for interactive browsing of these datasets. A further area we explore are user studies of systems and visualisations where we look at the different evaluations undertaken in order to test usability and compare systems, and highlight the key findings from these studies. We conclude the chapter with several recommendations for future work in this area. © 2011 Springer-Verlag Berlin Heidelberg.