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.

Targeted retrieval of gene expression measurements using regulatory models

Abstract: 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: elisabeth.georgii@aalto.fi; jarkko.salojarvi@helsinki.fi; samuel.kaski@hiit.fi Supplementary Information: Supplementary data are available at Bioinformatics online.

SNV-PPILP: refined SNV calling for tumor data using perfect phylogenies and ILP

Abstract: Motivation Recent studies sequenced tumor samples from the same progenitor at different development stages and showed that by taking into account the phylogeny of this development, single-nucleotide variant (SNV) calling can be improved. Accurate SNV calls can better reveal early-stage tumors, identify mechanisms of cancer progression or help in drug targeting. Results We present SNV-PPILP, a fast and easy to use tool for refining GATK's Unified Genotyper SNV calls, for multiple samples assumed to form a phylogeny. We tested SNV-PPILP on simulated data, with a varying number of samples, SNVs, read coverage and violations of the perfect phylogeny assumption. We always match or improve the accuracy of GATK, with a significant improvement on low read coverage. Availability and implementation SNV-PPILP, available at cs.helsinki.fi/gsa/snv-ppilp/, is written in Python and requires the free ILP solver lp_solve. Supplementary information Supplementary data are available at Bioinformatics online.

Use of future-oriented information in user-centered product concept ideation

Abstract: User-centered product concept design aims at creating concepts of new products Its success is dependent on the design team’s ability to use present-day information to come up with concepts concerning future products This paper takes as its task to investigate and explore what underlies this use of future-oriented information and what challenges it poses at the creative stages of a design process The proposed solution is based on an analytic division of available information into (1) trends such as company strategies, trends in the society and working life that denote changing conditions, and (2) stable context features that describe issues that are unlikely to change in the timeframe concerned A small case study is presented that exemplifies how this analytic distinction can be put into use More broadly, the paper encourages designers to think reflectively about the nature of information on which design decisions are based

Range predecessor and lempel-ziv parsing

Abstract: The Lempel-Ziv parsing of a string (LZ77 for short) is one of the most important and widely-used algorithmic tools in data compression and string processing. We show that the Lempel-Ziv parsing of a string of length n on an alphabet of size σ can be computed in O(n log log σ) time (O(n) time if we allow randomization) using O(n log σ) bits of working space; that is, using space proportional to that of the input string in bits. The previous fastest algorithm using O(n log σ) space takes O(n(log σ + log log n)) time. We also consider the important rightmost variant of the problem, where the goal is to associate with each phrase of the parsing its most recent occurrence in the input string. We solve this problem in O(n(1 + (log σ/[EQUATION])) time, using the same working space as above. The previous best solution for rightmost parsing uses O(n(1 + log σ/log log n)) time and O(n log n) space. As a bonus, in our solution for rightmost parsing we provide a faster construction method for efficient 2D orthogonal range reporting, which is of independent interest.