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.

The role of polygenic risk and susceptibility genes in breast cancer over the course of life.

Abstract: Polygenic risk scores (PRS) for breast cancer have potential to improve risk prediction, but there is limited information on their utility in various clinical situations. Here we show that among 122,978 women in the FinnGen study with 8401 breast cancer cases, the PRS modifies the breast cancer risk of two high-impact frameshift risk variants. Similarly, we show that after the breast cancer diagnosis, individuals with elevated PRS have an elevated risk of developing contralateral breast cancer, and that the PRS can considerably improve risk assessment among their female first-degree relatives. In more detail, women with the c.1592delT variant in PALB2 (242-fold enrichment in Finland, 336 carriers) and an average PRS (10-90th percentile) have a lifetime risk of breast cancer at 55% (95% CI 49-61%), which increases to 84% (71-97%) with a high PRS ( > 90th percentile), and decreases to 49% (30-68%) with a low PRS ( < 10th percentile). Similarly, for c.1100delC in CHEK2 (3.7-fold enrichment; 1648 carriers), the respective lifetime risks are 29% (27-32%), 59% (52-66%), and 9% (5-14%). The PRS also refines the risk assessment of women with first-degree relatives diagnosed with breast cancer, particularly among women with positive family history of early-onset breast cancer. Here we demonstrate the opportunities for a comprehensive way of assessing genetic risk in the general population, in breast cancer patients, and in unaffected family members.

Building Customer Relationship through Game Mechanics in Social Games

Abstract: This chapter examines mechanics of game design in social games that are used in building customer relationship. The developments in the game industry towards service orientation, and increased emphasis on social design, have resulted in overlap of game design and business design. This chapter examines the junction of these domains in contemporary social games, by studying how game design is used in pursuing business goals of the related business models. Several virtual worlds and social games are examined with the support of secondary data provided by experts in the field. The identified mechanics are then categorised and analysed in the context of business model literature on customer relationship building. The results provide several game mechanics that are located in the union of game design and business planning. Moreover, the results imply a new approach to game design in general by exemplifying how the traditional way of thinking about game design is no longer sufficient when the design of engaging mechanics needs to meet with business goals.

Geographic Variation and Bias in the Polygenic Scores of Complex Diseases and Traits in Finland

Abstract: Polygenic scores (PSs) are becoming a useful tool to identify individuals with high genetic risk for complex diseases, and several projects are currently testing their utility for translational applications. It is also tempting to use PSs to assess whether genetic variation can explain a part of the geographic distribution of a phenotype. However, it is not well known how the population genetic properties of the training and target samples affect the geographic distribution of PSs. Here, we evaluate geographic differences, and related biases, of PSs in Finland in a geographically well-defined sample of 2,376 individuals from the National FINRISK study. First, we detect geographic differences in PSs for coronary artery disease (CAD), rheumatoid arthritis, schizophrenia, waist-hip ratio (WHR), body-mass index (BMI), and height, but not for Crohn disease or ulcerative colitis. Second, we use height as a model trait to thoroughly assess the possible population genetic biases in PSs and apply similar approaches to the other phenotypes. Most importantly, we detect suspiciously large accumulations of geographic differences for CAD, WHR, BMI, and height, suggesting bias arising from the population's genetic structure rather than from a direct genotype-phenotype association. This work demonstrates how sensitive the geographic patterns of current PSs are for small biases even within relatively homogeneous populations and provides simple tools to identify such biases. A thorough understanding of the effects of population genetic structure on PSs is essential for translational applications of PSs.

Modeling Energy Consumption of Data Transmission over Wi-Fi

Abstract: Wireless data transmission consumes a significant part of the overall energy consumption of smartphones, due to the popularity of Internet applications. In this paper, we investigate the energy consumption characteristics of data transmission over Wi-Fi, focusing on the effect of Internet flow characteristics and network environment. We present deterministic models that describe the energy consumption of Wi-Fi data transmission with traffic burstiness, network performance metrics like throughput and retransmission rate, and parameters of the power saving mechanisms in use. Our models are practical because their inputs are easily available on mobile platforms without modifying low-level software or hardware components. We demonstrate the practice of model-based energy profiling on Maemo, Symbian, and Android phones, and evaluate the accuracy with physical power measurement of applications including file transfer, web browsing, video streaming, and instant messaging. Our experimental results show that our models are of adequate accuracy for energy profiling and are easy to apply.