MOTIVATION: Large public repositories of gene expression measurements offer the opportunity to position a new experiment into the context of earlier studies. While previous methods rely on experimental annotation or global similarity of expression profiles across genes or gene sets, we compare experiments by measuring similarity based on an unsupervised, data-driven regulatory model around pre-specified genes of interest. Our experiment retrieval approach is novel in two conceptual respects: (i) targetable focus and interpretability: the analysis is targeted at regulatory relationships of genes that are relevant to the analyst or come from prior knowledge; (ii) regulatory model-based similarity measure: related experiments are retrieved based on the strength of inferred regulatory links between genes. RESULTS: We learn a model for the regulation of specific genes from a data repository and exploit it to construct a similarity metric for an information retrieval task. We use the Fisher kernel, a rigorous similarity measure that typically has been applied to use generative models in discriminative classifiers. Results on human and plant microarray collections indicate that our method is able to substantially improve the retrieval of related experiments against standard methods. Furthermore, it allows the user to interpret biological conditions in terms of changes in link activity patterns. Our study of the osmotic stress network for Arabidopsis thaliana shows that the method successfully identifies relevant relationships around given key genes. AVAILABILITY: The code (R) is available at http://research.ics.tkk.fi/mi/software.shtml. CONTACT: [email protected]; [email protected]; [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

We introduce an expectation maximizationtype (EM) algorithm for maximum likelihood optimization of conditional densities. It is applicable to hidden variable models where the distributions are from the exponential family. The algorithm can alternatively be viewed as automatic step size selection for gradient ascent, where the amount of computation is traded off to guarantees that each step increases the likelihood. The tradeoff makes the algorithm computationally more feasible than the earlier conditional EM. The method gives a theoretical basis for extended Baum Welch algorithms used in discriminative hidden Markov models in speech recognition, and compares favourably with the current best method in the experiments.

We study a new task, proactive information retrieval by combining implicit relevance feedback and collaborative filtering. We have constructed a controlled experimental setting, a prototype application, in which the users try to find interesting scientific articles by browsing their titles. Implicit feedback is inferred from eye movement signals, with discriminative hidden Markov models estimated from existing data in which explicit relevance feedback is available. Collaborative filtering is carried out using the User Rating Profile model, a state-of-the-art probabilistic latent variable model, computed using Markov Chain Monte Carlo techniques. For new document titles the prediction accuracy with eye movements, collaborative filtering, and their combination was significantly better than by chance. The best prediction accuracy still leaves room for improvement but shows that proactive information retrieval and combination of many sources of relevance feedback is feasible.

This article describes the implementation of a system that is able to organize vast document collections according to textual similarities. It is based on the self-organizing map (SOM) algorithm. As the feature vectors for the documents statistical representations of their vocabularies are used. The main goal in our work has been to scale up the SOM algorithm to be able to deal with large amounts of high-dimensional data. In a practical experiment we mapped 6,840,568 patent abstracts onto a 1,002,240-node SOM. As the feature vectors we used 500-dimensional vectors of stochastic figures obtained as random projections of weighted word histograms.