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Background: The biological interpretation of large-scale gene expression data is one of the paramount challenges in current bioinformatics. In particular, placing the results in the context of other available functional genomics data, such as existing bio-ontologies, has already provided substantial improvement for detecting and categorizing genes of interest. One common approach is to look for functional annotations that are significantly enriched within a group or cluster of genes, as compared to a reference group. Results: In this work, we suggest the information-theoretic concept of mutual information to investigate the relationship between groups of genes, as given by data-driven clustering, and their respective functional categories. Drawing upon related approaches (Gibbons and Roth, Genome Research 12: 1574-1581, 2002), we seek to quantify to what extent individual attributes are sufficient to characterize a given group or cluster of genes. Conclusion: We show that the mutual information provides a systematic framework to assess the relationship between groups or clusters of genes and their functional annotations in a quantitative way. Within this framework, the mutual information allows us to address and incorporate several important issues, such as the interdependence of functional annotations and combinatorial combinations of attributes. It thus supplements and extends the conventional search for overrepresented attributes within a group or cluster of genes. In particular taking combinations of attributes into account, the mutual information opens the way to uncover specific functional descriptions of a group of genes or clustering result. All datasets and functional annotations used in this study are publicly available. All scripts used in the analysis are provided as additional files.
Non-stationarities are ubiquitous in EEG signals. They are especially apparent in the use of EEG-based brain- computer interfaces (BCIs): (a) in the differences between the initial calibration measurement and the online operation of a BCI, or (b) caused by changes in the subject's brain processes during an experiment (e.g. due to fatigue, change of task involvement, etc). In this paper, we quantify for the first time such systematic evidence of statistical differences in data recorded during offline and online sessions. Furthermore, we propose novel techniques of investigating and visualizing data distributions, which are particularly useful for the analysis of (non-) stationarities. Our study shows that the brain signals used for control can change substantially from the offline calibration sessions to online control, and also within a single session. In addition to this general characterization of the signals, we propose several adaptive classification schemes and study their performance on data recorded during online experiments. An encouraging result of our study is that surprisingly simple adaptive methods in combination with an offline feature selection scheme can significantly increase BCI performance
Advances in biotechnologies rapidly increase the number of molecules of a cell which can be observed simultaneously. This includes expression levels of thousands or ten-thousands of genes as well as concentration levels of metabolites or proteins. Such Profile data, observed at different times or at different experimental conditions (e.g., heat or dry stress), show how the biological experiment is reflected on the molecular level. This information is helpful to understand the molecular behaviour and to identify molecules or combination of molecules that characterise specific biological condition (e.g., disease). This work shows the potentials of component extraction algorithms to identify the major factors which influenced the observed data. This can be the expected experimental factors such as the time or temperature as well as unexpected factors such as technical artefacts or even unknown biological behaviour. Extracting components means to reduce the very high-dimensional data to a small set of new variables termed components. Each component is a combination of all original variables. The classical approach for that purpose is the principal component analysis (PCA). It is shown that, in contrast to PCA which maximises the variance only, modern approaches such as independent component analysis (ICA) are more suitable for analysing molecular data. The condition of independence between components of ICA fits more naturally our assumption of individual (independent) factors which influence the data. This higher potential of ICA is demonstrated by a crossing experiment of the model plant Arabidopsis thaliana (Thale Cress). The experimental factors could be well identified and, in addition, ICA could even detect a technical artefact. However, in continuously observations such as in time experiments, the data show, in general, a nonlinear distribution. To analyse such nonlinear data, a nonlinear extension of PCA is used. This nonlinear PCA (NLPCA) is based on a neural network algorithm. The algorithm is adapted to be applicable to incomplete molecular data sets. Thus, it provides also the ability to estimate the missing data. The potential of nonlinear PCA to identify nonlinear factors is demonstrated by a cold stress experiment of Arabidopsis thaliana. The results of component analysis can be used to build a molecular network model. Since it includes functional dependencies it is termed functional network. Applied to the cold stress data, it is shown that functional networks are appropriate to visualise biological processes and thereby reveals molecular dynamics.
Most information systems log events (e.g., transaction logs, audit traits) to audit and monitor the processes they support. At the same time, many of these processes have been explicitly modeled. For example, SAP R/3 logs events in transaction logs and there are EPCs (Event-driven Process Chains) describing the so-called reference models. These reference models describe how the system should be used. The coexistence of event logs and process models raises an interesting question: "Does the event log conform to the process model and vice versa?". This paper demonstrates that there is not a simple answer to this question. To tackle the problem, we distinguish two dimensions of conformance: fitness (the event log may be the result of the process modeled) and appropriateness (the model is a likely candidate from a structural and behavioral point of view). Different metrics have been defined and a Conformance Checker has been implemented within the ProM Framework
Business process management
(2006)
In this paper a self-checking carry select adder is proposed. The duplicated adder blocks which are inherent to a carry select adder without error detection are checked modulo 3. Compared to a carry select adder without error detection the delay of the MSB of the sum of the proposed adder does not increase. Compared to a self-checking duplicated carry select adder the area is reduced by 20%. No restrictions are imposed on the design of the adder blocks
We give a new view on building content clusters from page pair models. We measure the heuristic importance within every two pages by computing the distance of their accessed positions in usage sessions. We also compare our page pair models with the classical pair models used in information theories and natural language processing, and give different evaluation methods to build the reasonable content communities. And we finally interpret the advantages and disadvantages of our models from detailed experiment results
Denken in Services ist der Schlüssel zu einer gemeinsamen Sicht für IT Experten, Business Experten und Manager auf bestehende und entstehende Anwendungen und damit zu einer engeren Zusammenarbeit, die heutige Arbeitsabläufe revolutionieren kann: Auf der Service-Ebene ist es erstmals möglich, Fachexperten kontinuierlich während des gesamten Lebens-zyklus einer Anwendung einzubinden. Ihren Ursprung hat die service- orientierte Denkweise in der Telekommunikation, wo sie die Grundlage der modernsten Anwendungen für mobile und feste Plattformen war, insbesondere der so genannten Mehrwertdienste, wie Televoting, Freephone (die 0800 Nummern), Virtual Private Network. Nur durch die für die service-orientierung charakteristische konsequente Virtualisierung der Infrastrukturen und die lose Kopplung der Funktionalitäten war es möglich, die hochgradig heterogene Landschaft der Telefonie zu beherrschen. Dieselben Prinzipien sind aber viel allgemeiner anwendbar, zum Beispiel auch für Dienste in weniger technischen Geschäftsbereichen wie e-commerce, Logistik, Gesundheitswesen oder Verwaltung. Ihre konsequente Umsetzung als neues Paradigma für die Konzeption, den Entwurf und das Management komplexer Anwendungen hat das Potential, der Gesellschaft eine neue Generation personalisierter, sicherer, hochverfügbarer und effizienter (Internet-) Dienstleistungen zu bescheren. Damit werden viele Geschäftsbereiche revolutioniert, ähnlich wie bereits die Email in vielen Bereichen die klassische Kommunikation per Post revolutioniert hat.
Three quantum cryptographic protocols of multiuser quantum networks with embedded authentication, allowing quantum key distribution or quantum direct communication, are discussed in this work. The security of the protocols against different types of attacks is analysed with a focus on various impersonation attacks and the man-in-the-middle attack. On the basis of the security analyses several improvements are suggested and implemented in order to adjust the investigated vulnerabilities. Furthermore, the impact of the eavesdropping test procedure on impersonation attacks is outlined. The framework of a general eavesdropping test is proposed to provide additional protection against security risks in impersonation attacks.
When decomposing single trial electroencephalography it is a challenge to incorporate prior physiological knowledge. Here, we develop a method that uses prior information about the phase-locking property of event-related potentials in a regularization framework to bias a blind source separation algorithm toward an improved separation of single-trial phase-locked responses in terms of an increased signal-to-noise ratio. In particular, we suggest a transformation of the data, using weighted average of the single trial and trial-averaged response, that redirects the focus of source separation methods onto the subspace of event-related potentials. The practical benefit with respect to an improved separation of such components from ongoing background activity and extraneous noise is first illustrated on artificial data and finally verified in a real-world application of extracting single-trial somatosensory evoked potentials from multichannel EEG-recordings
Two common data representations are mostly used in intelligent data analysis, namely the vectorial and the pairwise representation. Pairwise data which satisfy the restrictive conditions of Euclidean spaces can be faithfully translated into a Euclidean vectorial representation by embedding. Non-metric pairwise data with violations of symmetry, reflexivity or triangle inequality pose a substantial conceptual problem for pattern recognition since the amount of predictive structural information beyond what can be measured by embeddings is unclear. We show by systematic modeling of non-Euclidean pairwise data that there exists metric violations which can carry valuable problem specific information. Furthermore, Euclidean and non-metric data can be unified on the level of structural information contained in the data. Stable component analysis selects linear subspaces which are particularly insensitive to data fluctuations. Experimental results from different domains support our pattern recognition strategy.
Incremental Support Vector Machines (SVM) are instrumental in practical applications of online learning. This work focuses on the design and analysis of efficient incremental SVM learning, with the aim of providing a fast, numerically stable and robust implementation. A detailed analysis of convergence and of algorithmic complexity of incremental SVM learning is carried out. Based on this analysis, a new design of storage and numerical operations is proposed, which speeds up the training of an incremental SVM by a factor of 5 to 20. The performance of the new algorithm is demonstrated in two scenarios: learning with limited resources and active learning. Various applications of the algorithm, such as in drug discovery, online monitoring of industrial devices and and surveillance of network traffic, can be foreseen.