Helsinki Institute for Information Technology

About: Helsinki Institute for Information Technology is a facility organization based out in Espoo, Finland. It is known for research contribution in the topics: Population & Bayesian network. The organization has 630 authors who have published 1962 publications receiving 63426 citations.

Multi-faceted information retrieval system for large scale email archives

Abstract: We profile a system for search and analysis of large-scale email archives. The system builds around four facets: Content-based search engine, statistical topic model, automatically inferred social networks and time-series analysis. The facets correspond to the types of information available in email data. The presented system allows chaining or combining the facets flexibly. Results of one facet may be used as input to another, yielding remarkable combinatorial power. In information retrieval point of view, the system provides support for exploration, approximate textual searches and data visualization. We present some experimental results based on a large real-world email corpus.

Assessing Data Mining Results on Matrices with Randomization

Abstract: Randomization is a general technique for evaluating the significance of data analysis results. In randomization-based significance testing, a result is considered to be interesting if it is unlikely to obtain as good result on random data sharing some basic properties with the original data. Recently, the randomization approach has been applied to assess data mining results on binary matrices and limited types of real-valued matrices. In these works, the row and column value distributions are approximately preserved in randomization. However, the previous approaches suffer from various technical and practical shortcomings. In this paper, we give solutions to these problems and introduce a new practical algorithm for randomizing various types of matrices while preserving the row and column value distributions more accurately. We propose a new approach for randomizing matrices containing features measured in different scales. Compared to previous work, our approach can be applied to assess data mining results on different types of real-life matrices containing dissimilar features, nominal values, non-Gaussian value distributions, missing values and sparse structure. We provide an easily usable implementation that does not need problematic manual tuning as theoretically justified parameter values are given. We perform extensive experiments on various real-life datasets showing that our approach produces reasonable results on practically all types of matrices while being easy and fast to use.

Implicitly Adaptive Importance Sampling

Abstract: Adaptive importance sampling is a class of techniques for finding good proposal distributions for importance sampling. Often the proposal distributions are standard probability distributions whose parameters are adapted based on the mismatch between the current proposal and a target distribution. In this work, we present an implicit adaptive importance sampling method that applies to complicated distributions which are not available in closed form. The method iteratively matches the moments of a set of Monte Carlo draws to weighted moments based on importance weights. We apply the method to Bayesian leave-one-out cross-validation and show that it performs better than many existing parametric adaptive importance sampling methods while being computationally inexpensive.

Frequency-dependent selection can forecast evolution in Streptococcus pneumoniae

Abstract: Predicting how pathogen populations will change over time is challenging. Such has been the case with Streptococcus pneumoniae, an important human pathogen, and the pneumococcal conjugate vaccines (PCVs), which target only a fraction of the strains in the population. Here, we use the frequencies of accessory genes to predict changes in the pneumococcal population after vaccination, hypothesizing that these frequencies reflect negative frequency-dependent selection (NFDS) on the gene products. We find that the standardized predicted fitness of a strain, estimated by an NFDS-based model at the time the vaccine is introduced, enables us to predict whether the strain increases or decreases in prevalence following vaccination. Further, we are able to forecast the equilibrium post-vaccine population composition and assess the invasion capacity of emerging lineages. Overall, we provide a method for predicting the impact of an intervention on pneumococcal populations with potential application to other bacterial pathogens in which NFDS is a driving force.