Association Analysis Techniques for Discovering Functional Modules from Microarray Data

An application of great interest in microarray data analysis is the identification of a group of genes that show very similar patterns of expression in a data set, and are expected to represent groups of genes that perform common/similar functions, also known as functional modules. Although clustering offers a natural solution to this problem, it suffers from the limitation that it uses all the conditions to compare two genes, whereas only a subset of them may be relevant. Association analysis offers an alternative route for finding such groups of genes that may be co-expressed only over a subset of the experimental conditions used to prepare the data set. The techniques in this field attempt to find groups of data objects that contain coherent values across a set of attributes, in an exhaustive and efficient manner. In this paper, we illustrate how a generalization of the techniques in association analysis for real-valued data can be utilized to extract coherent functional modules from large microarray data sets

Requirement- and cost-driven product development process

This paper presents an approach, which enables a cost and requirement driven control of the design process. It is based on the concept of Property-Driven Development (PDD) [WeWD-03]. Integrated in the approach are well established tools like Target Costing and Value Analysis as well as methods of design for requirements. In the authors\u27 approach, the product development process is controlled by an ongoing target/actual (\u27Soll/Ist\u27) comparison between target properties and the state of properties currently achieved. For each property, depending on the fulfilment, quality ratings from the customer\u27s point of view are assigned. The aim of the product development process is the maximisation of the sum of these quality ratings. This aim can be realised based on the PDD approach, because it supports the engineer/designer by explicitly representing the interdependencies between the properties (that have to be optimized) and the characteristics that influence these properties

New ideas for knowledge management in product development projects

Due to their high complexity, product development processes should be expected to be a highly rewarding field for the application of knowledge management systems. Nonetheless, such systems are rarely used in practice. One important reason is that the extra effort of writing down knowledge is widely perceived as slowing down the actual design work without offering visible benefits. Also, writing documents is typically not a favourite pastime of technicians and engineers. There is a high inhibition threshold due to the expectation that the text within a document is well structured and formulated. Furthermore, documents as knowledge carriers require additional effort to retrieve the knowledge: Often the user has to screen several complete documents to find the piece of information he/she is looking for. Full text searches can help, but quite frequently lead to irrelevant information or miss out an important passages because different words were used. So, economically speaking, knowledge management does not yield sufficient return on investment. The authors claim that this can be changed when • knowledge capturing is not a separate activity, but is integrated in the daily design work, • knowledge is integrated into a comprehensive representation of the design and the ongoing design process, • knowledge capturing can be done with very low effort, • knowledge retrieval is very efficient, i.e. the user finds relevant information quickly and reliably, • the captured knowledge can be automatically processed in order to extract different views and to create reports for different purposes. It will be shown that a system that handles a large number of small notes is able to fulfil these requirements. Writing a small note on an adhesive paper is surely the easiest and most popular way of putting down knowledge. The rising popularity of Blogs and Wiki systems for documentation purposes, even in business, shows that people are willing to put down and to share knowledge if they can do it in small portions

Mining Novel Multivariate Relationships in Time Series Data Using Correlation Networks

In many domains, there is significant interest in capturing novel relationships between time series that represent activities recorded at different nodes of a highly complex system. In this paper, we introduce multipoles, a novel class of linear relationships between more than two time series. A multipole is a set of time series that have strong linear dependence among themselves, with the requirement that each time series makes a significant contribution to the linear dependence. We demonstrate that most interesting multipoles can be identified as cliques of negative correlations in a correlation network. Such cliques are typically rare in a real-world correlation network, which allows us to find almost all multipoles efficiently using a clique-enumeration approach. Using our proposed framework, we demonstrate the utility of multipoles in discovering new physical phenomena in two scientific domains: climate science and neuroscience. In particular, we discovered several multipole relationships that are reproducible in multiple other independent datasets and lead to novel domain insights.Comment: This is the accepted version of article submitted to IEEE Transactions on Knowledge and Data Engineering 201

The Jackknife, the Bootstrap, and Censored Data

1 online resource (PDF, 25 pages