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.

Haplotype Sharing Provides Insights into Fine-Scale Population History and Disease in Finland

Abstract: Finland provides unique opportunities to investigate population and medical genomics because of its adoption of unified national electronic health records, detailed historical and birth records, and serial population bottlenecks. We assembled a comprehensive view of recent population history (≤100 generations), the timespan during which most rare-disease-causing alleles arose, by comparing pairwise haplotype sharing from 43,254 Finns to that of 16,060 Swedes, Estonians, Russians, and Hungarians from geographically and linguistically adjacent countries with different population histories. We find much more extensive sharing in Finns, with at least one ≥ 5 cM tract on average between pairs of unrelated individuals. By coupling haplotype sharing with fine-scale birth records from more than 25,000 individuals, we find that although haplotype sharing broadly decays with geographical distance, there are pockets of excess haplotype sharing; individuals from northeast Finland typically share several-fold more of their genome in identity-by-descent segments than individuals from southwest regions. We estimate recent effective population-size changes through time across regions of Finland, and we find that there was more continuous gene flow as Finns migrated from southwest to northeast between the early- and late-settlement regions than was dichotomously described previously. Lastly, we show that haplotype sharing is locally enriched by an order of magnitude among pairs of individuals sharing rare alleles and especially among pairs sharing rare disease-causing variants. Our work provides a general framework for using haplotype sharing to reconstruct an integrative view of recent population history and gain insight into the evolutionary origins of rare variants contributing to disease.

Bayesian Optimization for Likelihood-Free Inference of Simulator-Based Statistical Models

Abstract: Our paper deals with inferring simulator-based statistical models given some observed data. A simulator-based model is a parametrized mechanism which specifies how data are generated. It is thus also referred to as generative model. We assume that only a finite number of parameters are of interest and allow the generative process to be very general; it may be a noisy nonlinear dynamical system with an unrestricted number of hidden variables. This weak assumption is useful for devising realistic models but it renders statistical inference very difficult. The main challenge is the intractability of the likelihood function. Several likelihood-free inference methods have been proposed which share the basic idea of identifying the parameters by finding values for which the discrepancy between simulated and observed data is small. A major obstacle to using these methods is their computational cost. The cost is largely due to the need to repeatedly simulate data sets and the lack of knowledge about how the parameters affect the discrepancy. We propose a strategy which combines probabilistic modeling of the discrepancy with optimization to facilitate likelihood-free inference. The strategy is implemented using Bayesian optimization and is shown to accelerate the inference through a reduction in the number of required simulations by several orders of magnitude.

Defining the price of hospitality: networked hospitality exchange via Airbnb

Abstract: This study examines how money mediates and structures social exchange in a hospitality exchange service, and how social and economic factors guiding exchange get intertwined in this context. We present a qualitative study on the experiences of people who offer to rent out their homes, or parts of them, via the online peer-to-peer renting service Airbnb. Our study suggests that the frame monetary transactions set to exchange relationships contributes to the hosts' sense of control and ease in the exchange. We identified two behavioral patterns that highlight the importance of reputation and trust: (1) hosts divert their accumulated reputational capital into the rental price and (2) they may price their property below "the market price", so that they can choose their exchange partners form a wider pool of candidates.

A Generic Large Scale Simulator for Ubiquitous Computing

Abstract: The complexity associated to gathering and processing contextual data makes testing mobile context-aware applications and services difficult. Furthermore, the lack of standard data sets and simulation tools makes the evaluation of machine learning algorithms in context-aware settings an even harder task. To ease the situation, we introduce a generic simulator that has been designed with the above mentioned purposes in mind. The simulator has also proven to be a good demonstration tool for mobile services and applications that are aimed at groups. The simulator is highly customizable and it can output context information of individual entities both through an interactive GUI and as data streams consisting of comma separated values. To support a wide range of tasks and scenarios, we have separated the three main information sources: behavior of agents, the scenario being simulated and the used context variable. The simulator has been implemented using Java, and the data streams have been made available through a web service interface.

A generative approach for image-based modeling of tumor growth

Abstract: Extensive imaging is routinely used in brain tumor patients to monitor the state of the disease and to evaluate therapeutic options. A large number of multi-modal and multi-temporal image volumes is acquired in standard clinical cases, requiring new approaches for comprehensive integration of information from different image sources and different time points. In this work we propose a joint generative model of tumor growth and of image observation that naturally handles multimodal and longitudinal data. We use the model for analyzing imaging data in patients with glioma. The tumor growth model is based on a reaction-diffusion framework. Model personalization relies only on a forward model for the growth process and on image likelihood. We take advantage of an adaptive sparse grid approximation for efficient inference via Markov Chain Monte Carlo sampling. The approach can be used for integrating information from different multi-modal imaging protocols and can easily be adapted to other tumor growth models.