Wolfram Menzel

The RIN: an RNA integrity number for assigning integrity values to RNA measurements

BackgroundThe integrity of RNA molecules is of paramount importance for experiments that try to reflect the snapshot of gene expression at the moment of RNA extraction. Until recently, there has been no reliable standard for estimating the integrity of RNA samples and the ratio of 28S:18S ribosomal RNA, the common measure for this purpose, has been shown to be inconsistent. The advent of microcapillary electrophoretic RNA separation provides the basis for an automated high-throughput approach, in order to estimate the integrity of RNA samples in an unambiguous way.MethodsA method is introduced that automatically selects features from signal measurements and constructs regression models based on a Bayesian learning technique. Feature spaces of different dimensionality are compared in the Bayesian framework, which allows selecting a final feature combination corresponding to models with high posterior probability.ResultsThis approach is applied to a large collection of electrophoretic RNA measurements recorded with an Agilent 2100 bioanalyzer to extract an algorithm that describes RNA integrity. The resulting algorithm is a user-independent, automated and reliable procedure for standardization of RNA quality control that allows the calculation of an RNA integrity number (RIN).ConclusionOur results show the importance of taking characteristics of several regions of the recorded electropherogram into account in order to get a robust and reliable prediction of RNA integrity, especially if compared to traditional methods.

The KeY tool

KeY is a tool that provides facilities for formal specification and verification of programs within a commercial platform for UML based software development. Using the KeY tool, formal methods and object-oriented development techniques are applied in an integrated manner. Formal specification is performed using the Object Constraint Language (OCL), which is part of the UML standard. KeY provides support for the authoring and formal analysis of OCL constraints. The target language of KeY based development is Java Card DL, a proper subset of Java for smart card applications and embedded systems. KeY uses a dynamic logic for Java Card DL to express proof obligations, and provides a state-of-the-art theorem prover for interactive and automated verification. Apart from its integration into UML based software development, a characteristic feature of KeY is that formal specification and verification can be introduced incrementally.

The KeY Approach: Integrating Object Oriented Design and Formal Verification

This paper reports on the ongoing KeY project aimed at bridging the gap between (a) object-oriented software engineering methods and tools and (b) deductive verification. A distinctive feature of our approach is the use of a commercial CASE tool enhanced with functionality for formal specification and deductive verification.

The KeY System: Integrating Object-Oriented Design and Formal Methods

This paper gives a brief description of the KeY system, a tool written as part of the ongoing KeY project, which is aimed at bridging the gap between (a) OO software engineering methods and tools and (b) deductive verification. The KeY system consists of a commercial CASE tool enhanced with functionality for formal specification and deductive verification.

Bayesian learning for sales rate prediction for thousands of retailers

Abstract Every newspaper publisher has to solve the problem of printing a large number of copies and distributing them to the retail traders trying to keep the return quote as low as possible. To solve this task he needs to estimate as accurately as possible the sales rates for each retail trader. In this paper, we want to show how a prediction system for many thousands of retail traders can be built based on the prediction of the individual sales rates. This prediction is based on a neural network approach. We use a Bayesian learning algorithm to regularize the networks automatically. Furthermore, a top down search based on mutual information is used to optimize the input structure of the networks. The neural network approach reduces the return quota significantly. We conclude with the observation that several data sets are hard to predict and give reasons for that behaviour.

Die 80er Jahre

PRO-ART (PROmetheus ARTificial intelligence) bezeichnet eines von sieben Teilprojekten des EUREKA-Projektes PROMETHEUS (PROgraM for a European Traffic with Highest Efficiency and Unprecedented Safety), das bereits vor mehr als zwanzig Jahren begonnen und vor dreizehn Jahren abgeschlossen worden ist – fur unsere schnelllebige Zeit fast vor einer Ewigkeit. Dennoch kommen erfahrene Forscher und Entwickler in der europaischen Automobil- sowie Zuliefererindustrie bei Gesprachen zum „Stand der Kunst“ uber kurz oder lang auf PROMETHEUS als Schlusselereignis zuruck.

Classes with Easily Learnable Subclasses

Let Exdenote the explanatory model of learning [3,5]. Various more restrictive models have been studied in the literature, an example is finite identification [5]. The topic of the present paper are the natural variants (a) and (b) below of the classical question whether a given learning criteria is more restrictive than Ex-learning. (a) Does every infinite Ex-identifiable class have an infinite subclass which can be identified according to a given restrictive criterion? (b) If an infinite Exidentifiable class S has an infinite finitely identifiable subclass, does it necessarily follow that some appropriate learner Ex-identifies S as well as finitely identifies an infinite subclass of S? These questions are also treated in the context of ordinal mind change bounds.

Problem Solving with Neural Networks

What it means to “solve problems in a scientific way” changes in history. It had taken a long time for the paradigm of the “rigid” — the “objective”, the “clare et distincte” — to become precise (and hence fixed, in the ambivalent sense of such progress): as being identical with “formalized” or “formalizable”, thus referring to a given deductive apparatus. This appears convincing. In order to be rigid in the sense that anybody else (sufficiently trained) might understand my words and symbols as they were meant, I have to fix the language and the rules of operating on symbols beforehand, and all that “meaning” means must be contained in that initial ruling. What other way could there be to definitely exclude subjective misunderstanding and failure of any kind?

Neuronale Netze zur Prognose von Finanzzeitreihen und Absatzzahlen

Results are reported on applying neural networks to tasks of predicting economic data. These are, on the one hand, stock prices or foreign exchange rates and, on the other hand, the daily numbers of sales of a German newspaper.

Classes with easily learnable subclasses

In this paper we study the question of whether identifiable classes have subclasses which are identifiable under a more restrictive criterion. The chosen framework is inductive inference, in particular the criterion of explanatory learning (Ex) of recursive functions as introduced by Gold [Inform. Comput. 10 (1967) 447]. Among the more restrictive criteria is finite learning where the learner outputs, on every function to be learned, exactly one hypothesis (which has to be correct). The topic of the present paper are the natural variants (a) and (b) below of the classical question whether a given learning criterion like finite learning is more restrictive than Ex-learning. (a) Does every infinite Ex-identifiable class have an infinite finitely identifiable subclass? (b) If an infinite Ex-identifiable class S has an infinite finitely identifiable subclass, does it necessarily follow that some appropriate learner Ex-identifies S as well as finitely identifies an infinite subclass of S? These questions are also treated in the context of ordinal mind change bounds.