Mining co-regulated gene profiles for the detection of functional associations in gene expression data

Motivation: Association pattern discovery (APD) methods have been successfully applied to gene expression data. They find groups of co-regulated genes in which the genes are either up- or down-regulated throughout the identified conditions. These methods, however, fail to identify similarly expressed genes whose expressions change between up- and down-regulation from one condition to another. In order to discover these hidden patterns, we propose the concept of mining co-regulated gene profiles. Co-regulated gene profiles contain two gene sets such that genes within the same set behave identically (up or down) while genes from different sets display contrary behavior. To reduce and group the large number of similar resulting patterns, we propose a new similarity measure that can be applied together with hierarchical clustering methods. Results: We tested our proposed method on two well-known yeast microarray data sets. Our implementation mined the data effectively and discovered patterns of co-regulated genes that are hidden to traditional APD methods. The high content of biologically relevant information in these patterns is demonstrated by the significant enrichment of co-regulated genes with similar functions. Our experimental results show that the Mining Attribute Profile (MAP) method is an efficient tool for the analysis of gene expression data and competitive with bi-clustering techniques. Contact: [email protected] Supplementary information: Supplementary data and an executable demo program of the MAP implementation are freely available at http://www.fgcz.ch/publications/ma

Dynamic Vision System: Modeling the prey recognition of common toads Bufo bufo

Stolte E, Littmann E, Ritter H. Dynamic Vision System: Modeling the prey recognition of common toads Bufo bufo. In: Marinaro M, ed. ICANN ’94. Proceedings of the International Conference on Artificial Neural Networks Sorrento, Italy, 26–29 May 1994 Volume 1, Parts 1 and 2. Vol 1. London: Springer; 1994: 34-37.The tectal structures underlying the prey-catching of toads are one of the best-known mechanisms in neurobiology. In our paper we present a simplified model of the prey recognition of the common toad Bufo bufo. We show that the recognition performance can be explained by coupling two neuron layers that exhibit locally excitatory interaction while there is a topographic inhibition between the layers. The neurons are modeled as nodes with gaussian lateral excitation and time-dependent exponential decrease in activity. We derive a method to learn the adjustable parameters by supervised training

Scientific Data Repositories - Designing for a Moving Target

Managing scientific data warehouses requires constant adaptations to cope with changes in processing algorithms, computing environments, database schemas, and usage patterns. We have faced this challenge in the RHESSI Experimental Data Center (HEDC), a datacenter for the RHESSI NASA spacecraft. In this paper we describe our experience in developing HEDC and discuss in detail the design choices made. To successfully accommodate typical adaptations encountered in scientific data management systems, HEDC (i) clearly separates generic from domain specific code in all tiers, (ii) uses a file system for the actual data in combination with a DBMS to manage the corresponding meta data, and (iii) revolves around a middle tier designed to scale if more browsing or processing power is required. These design choices are valuable contributions as they address common concerns in a wide range of scientific data management systems

Mining co-regulated gene profiles for the detection of functional associations in gene expression data

ISSN:1367-4803ISSN:1460-205