Jan T. Kim

An Information-Theoretic Approach for the Quantification of Relevance

We propose a concept for a Shannon-type quantification of information relevant to a decision unit or agent. The proposed measure is operational, can - at least in principle - be calculated for a given system and has an immediate interpretation as an information quantity. Its use as a natural framework for the study of sensor evolution is discussed.

Binding Matrix: A Novel Approach for Binding Site Recognition

Recognition of protein-DNA binding sites in genomic sequences is a crucial step for discovering biological functions of genomic sequences. Explosive growth in availability of sequence information has resulted in a demand for binding site detection methods with high specificity. The motivation of the work presented here is to address this demand by a systematic approach based on Maximum Likelihood Estimation. A general framework is developed in which a large class of binding site detection methods can be described in a uniform and consistent way. Protein-DNA binding is determined by binding energy, which is an approximately linear function within the space of sequence words. All matrix based binding word detectors can be regarded as different linear classifiers which attempt to estimate the linear separation implied by the binding energy function. The standard approaches of consensus sequences and profile matrices are described using this framework. A maximum likelihood approach for determining this linear separation leads to a novel matrix type, called the binding matrix. The binding matrix is the most specific matrix based classifier which is consistent with the input set of known binding words. It achieves significant improvements in specificity compared to other matrices. This is demonstrated using 95 sets of experimentally determined binding words provided by the TRANSFAC database.

Developing and Testing Methods for Microarray Data Analysis Using an Artificial Life Framework

Microarray technology has resulted in large sets of gene expression data. Using these data to derive knowledge about the underlying mechanisms that control gene expression dynamics has become an important challenge. Adequate models of the fundamental principles of gene regulation, such as Artificial Life models of regulatory networks, are pivotal for progress in this area. In this contribution, we present a framework for simulating microarray gene expression experiments. Within this framework, artificial regulatory networks with a simple regulon structure are generated. Simulated expression profiles are obtained from these networks under a series of different environmental conditions. The expression profiles show a complex diversity. Consequently, success in using hierarchical clustering to detect groups of genes which form a regulon proves to depend strongly on the method which is used to quantify similarity between expression profiles. When measurements are noisy, even clusters of identically regulated genes are surprisingly difficult to detect. Finally, we suggest cluster support, a method based on overlaying multiple clustering trees, to find out which clusters in a tree are biologically significant.

One-Class Classification with Subgaussians

If a simple and fast solution for one-class classification is required, the most common approach is to assume a Gaussian distribution for the patterns of the single class. Bayesian classification then leads to a simple template matching. In this paper we show for two very different applications that the classification performance can be improved significantly if a more uniform subgaussian instead of a Gaussian class distribution is assumed. One application is face detection, the other is the detection of transcription factor binding sites on a genome. As for the Gaussian, the distance from a template, i.e., the distribution center, determines a pattern’s class assignment. However, depending on the distribution assumed, maximum likelihood learning leads to different templates from the training data. These new templates lead to significant improvements of the classification performance.

Statistical learning for detecting protein-DNA-binding sites

Detecting the sites on genomic DNA at which DNA binding proteins bind is a highly relevant task in bioinformatics. For example, the binding sites of transcription factors are key elements of regulatory networks and determine the location of genes on a genome. Usually, for a given DNA binding protein, only a few DNA-subsequences at which the protein binds are known experimentally. The task then is to deduce the global binding characteristics of the protein based on these few positive examples. A widespread approach is the so-called profile-matrix (PM). The PM-approach can be interpreted as a linear classifier (binding word class/non-binding word class) within the space of sequence words, with the profile of the experimentally verified binding sites determining its parameters. In this paper a novel approach called binding-matrix (BM) is introduced. Like the PM, the BM realizes a linear classification, but in contrast to the profile-matrix approach the parameters (matrix) of the classifier is now determined by maximum likelihood estimation. Tested on data from the TRANSFAC database, the maximum likelihood estimation leads to an increase in classification performance by about an order of magnitude.

Exploring Empowerment as a Basis for Quantifying Sustainability

Empowerment quantifies the choice available to an agent as the actuation channel capacity. However, not all such choices are sustainable: After some choices, the agent may not be able to return to its original state, or returning there may be costly. In this paper we explore whether empowerment can be adapted to obtain a measure of sustainability. As a straightforward modification, the agents options is restricted to actions that are reversible within a given time horizon. We furthermore investigate the lengths of return paths and discuss their potential to indicate sustainability.

On the Evolution of Information in the Constituents of Regulatory Gene Networks

Transcription factors and their binding sites have become a focal point of bioinformaties research since it became clear that regulatory networks are a centerpiece of genetic information processing. Understanding how genetic information controls and organizes complex biological processes, such as metabolic dynamics, development and morphogenesis [1-3], is a major challenge which is addressed by current bioinformatic modelling. The process of realizing phenotypic properties encoded in DNA sequences necessarily involves sequence specific contacts between DNA subsequences and molecules with a biological activity.