Informative Bayesian Neural Network Priors for Weak Signals

Funding Information: ∗This work was supported by the Academy of Finland (Flagship programme: Finnish Center for Artificial Intelligence, FCAI, grants 319264, 292334, 286607, 294015, 336033, 315896, 341763), and EU Horizon 2020 (INTERVENE, grant no. 101016775). We also acknowledge the computational resources provided by the Aalto Science-IT Project from Computer Science IT. †Helsinki Institute for Information Technology HIIT, Department of Computer Science, Aalto University, Finland, [email protected] ‡Finnish Institute for Health and Welfare (THL), Finland §Institute for Molecular Medicine Finland, FIMM-HiLIFE, Helsinki, Finland ¶Department of Computer Science, University of Manchester, UK ‖Equal contribution. Funding Information: This work was supported by the Academy of Finland (Flagship programme: Finnish Center for Artificial Intelligence, FCAI, grants 319264, 292334, 286607, 294015, 336033, 315896, 341763), and EU Horizon 2020 (INTERVENE, grant no. 101016775). We also acknowledge the computational resources provided by the Aalto Science-IT Project from Computer Science IT. Publisher Copyright: © 2022 International Society for Bayesian AnalysisEncoding domain knowledge into the prior over the high-dimensional weight space of a neural network is challenging but essential in applications with limited data and weak signals. Two types of domain knowledge are commonly available in scientific applications: 1. feature sparsity (fraction of features deemed relevant); 2. signal-to-noise ratio, quantified, for instance, as the proportion of variance explained. We show how to encode both types of domain knowledge into the widely used Gaussian scale mixture priors with Automatic Relevance Determination. Specifically, we propose a new joint prior over the local (i.e., feature-specific) scale parameters that encodes knowledge about feature sparsity, and a Stein gradient optimization to tune the hyperparameters in such a way that the distribution induced on the model’s proportion of variance explained matches the prior distribution. We show empirically that the new prior improves prediction accuracy compared to existing neural network priors on publicly available datasets and in a genetics application where signals are weak and sparse, often outperforming even computationally intensive cross-validation for hyperparameter tuning.Peer reviewe

Bayesian spatial and temporal epidemiology of non-communicable diseases and mortality

Spatial epidemiology combines spatial statistical modelling and disease epidemiology for studying geographic variation in mortality and morbidity. The effects of putative risk factors may be examined using ecological regression models. On the other hand, age-period-cohort models can be used to study the variation of mortality and morbidity through time. Bayesian hierarchical statistical models offer a flexible framework for these studies and enable the estimation of uncertainties in the results. The models are usually estimated using computer-intensive Markov chain Monte Carlo simulations. In this dissertation the first four publications present practical epidemiological studies on geographic variation in non-communicable diseases in Finland. In the last publication we study the long-time variation in all-cause mortality in several European countries. New statistical models are developed for these studies. This work provides new epidemiological information on the geographic variation of acute myocardial infarctions (AMI), ischaemic stroke and parkinsonism in Finland. An extended model for studying shared and disease specific geographic variation is presented using data on AMI and ischaemic stroke incidence. Existing results on the inverse association of water hardness and AMI are refined. New models for interpolation of geochemical data with non-detected values are presented with case studies using real data. Finally, the Bayesian age-period-cohort model is extended with versatile interactions and better prediction ability. The model is then used to study long-term variation in mortality in Europe

Kansallinen turvallisuus vs. perusoikeudet : Lainsäädäntötutkimus tiedustelulakihankkeesta

Tutkimuksen aiheena on turvallisuusviranomaisten harjoittaman tiedustelutoiminnan ja erityisesti tietoliikennetiedustelun saattaminen osaksi kansallista lainsäädäntöämme. Tiedonhankintalakityöryhmä muodostettiin 13.12.2013, ja sen tehtäväksi annettiin lainsäädännön kehittäminen turvallisuusviranomaisten tiedonhankintakyvyn parantamiseksi. Mietinnössä arvioitiin myös tiedustelua koskevan lainsäädännön kehittämistarpeita. Työryhmä ehdotti, että hallitus käynnistäisi tarvittavat toimenpiteet tiedustelua koskevan lain tai lakien säätämiseksi. Tiedustelulla olisi tarkoitus hankkia kansallisen turvallisuuden kannalta välttämätöntä tietoa muun muassa vakavista kansainvälisistä uhista. Tutkimuksen tarkoituksena on selvittää minkälaisilla uhkakuvilla tai muilla perusteilla tiedustelulakihanketta Suomeen vaaditaan sekä minkälaisia argumentteja lainvalmistelussa mukana olevat osapuolet esittävät niistä toimivaltuuksista ja keinoista, joilla tiedustelua olisi tarkoitus harjoittaa. Lisäksi pyrkimyksenä on saada selville mitkä perus- ja ihmisoikeudet ja miten niitä vaaditaan huomioonotettavaksi säädösprosessin valmisteluvaiheessa. Tutkimuksessa käytetään pääasiassa aineistoa, joka oli tiedustelulakihankkeesta saatavilla toukokuussa 2015 ja kohteena on lainsäädäntöprosessin ensimmäisen lausuntokierroksen valmisteluvaihe. Virallislähteinä tuossa vaiheessa olivat tiedonhankintalakityöryhmän mietintö lausuntopyyntöineen, liikenne- ja viestintäministeriön edustajan eriävä mielipide em. mietintöön ja mietinnöstä annetut 74 erillistä lausuntoa (252 A4 –sivua). Tutkimuksen pääasiallisena tutkimusmenetelmänä on kvalitatiivinen eli laadullinen, jonka avulla on sisällönanalyysin keinoin löytää ja eritellä aineistosta esiin nousevia teemoja ja argumentteja hankkeen puolesta ja vastaan. Lakihanketta puoltavien lausuntojen merkittävimmäksi teemaksi nousi tiedustelutiedon välttämättömyys turvallisuuden takaamiseksi. Vastustavien lausuntojen merkittävin teema oli, että esitetty tietoliikennetiedustelu on tehotonta. Esitettyjen tiedustelukeinojen vaarantamat keskeisimmät perusoikeudet ovat yksityiselämän suoja ja luottamuksellisen viestin salaisuuden suoja. Lisäksi voitiin todeta, ettei kansallinen turvallisuus sovellu luottamuksellisen viestin salaisuuden rajoitusperusteeksi

Modelling spatial patterns in host-associated microbial communities

Microbial communities exhibit spatial structure at different scales, due to constant interactions with their environment and dispersal limitation. While this spatial structure is often considered in studies focusing on free-living environmental communities, it has received less attention in the context of host-associated microbial communities or microbiota. The wider adoption of methods accounting for spatial variation in these communities will help to address open questions in basic microbial ecology as well as realize the full potential of microbiome-aided medicine. Here, we first overview known factors affecting the composition of microbiota across diverse host types and at different scales, with a focus on the human gut as one of the most actively studied microbiota. We outline a number of topical open questions in the field related to spatial variation and patterns. We then review the existing methodology for the spatial modelling of microbiota. We suggest that methodology from related fields, such as systems biology and macro-organismal ecology, could be adapted to obtain more accurate models of spatial structure. We further posit that methodological developments in the spatial modelling and analysis of microbiota could in turn broadly benefit theoretical and applied ecology and contribute to the development of novel industrial and clinical applications.Peer reviewe

Gene-gene interaction detection with deep learning

The extent to which genetic interactions affect observed phenotypes is generally unknown because current interaction detection approaches only consider simple interactions between top SNPs of genes. We introduce an open-source framework for increasing the power of interaction detection by considering all SNPs within a selected set of genes and complex interactions between them, beyond only the currently considered multiplicative relationships. In brief, the relation between SNPs and a phenotype is captured by a neural network, and the interactions are quantified by Shapley scores between hidden nodes, which are gene representations that optimally combine information from the corresponding SNPs. Additionally, we design a permutation procedure tailored for neural networks to assess the significance of interactions, which outperformed existing alternatives on simulated datasets with complex interactions, and in a cholesterol study on the UK Biobank it detected nine interactions which replicated on an independent FINRISK dataset.An open-source framework combines deep learning and permutations of gene interaction neural networks to detect complex gene-gene interactions and their significance in contributions to phenotypes.Peer reviewe

Polygenic Risk Scores Predict Hypertension Onset and Cardiovascular Risk

Although genetic risk scores have been used to predict hypertension, their utility in the clinical setting remains uncertain. Our study comprised N=218 792 FinnGen participants (mean age 58 years, 56% women) and N=22 624 well-phenotyped FINRISK participants (mean age 50 years, 53% women). We used public genome-wide association data to compute polygenic risk scores (PRSs) for systolic and diastolic blood pressure (BP). Using time-to-event analysis, we then assessed (1) the association of BP PRSs with hypertension and cardiovascular disease (CVD) in FinnGen and (2) the improvement in model discrimination when combining BP PRSs with the validated 4- and 10-year clinical risk scores for hypertension and CVD in FINRISK. In FinnGen, compared with having a 20 to 80 percentile range PRS, a PRS in the highest 2.5% conferred 2.3-fold (95% CI, 2.2-2.4) risk of hypertension and 10.6 years (95% CI, 9.9-11.4) earlier hypertension onset. In subgroup analyses, this risk was only 1.6-fold (95% CI, 1.5-1.7) for late-onset hypertension (age >= 55 years) but 2.8-fold (95% CI, 2.6-2.9) for early-onset hypertension (agePeer reviewe

Soluble HLA-DR serum levels are associated with smoking but not with acute coronary syndrome

Peer reviewe

Associations between gut microbiota and incident fractures in the FINRISK cohort.

The gut microbiota (GM) can regulate bone mass, but its association with incident fractures is unknown. We used Cox regression models to determine whether the GM composition is associated with incident fractures in the large FINRISK 2002 cohort (n = 7043, 1092 incident fracture cases, median follow-up time 18 years) with information on GM composition and functionality from shotgun metagenome sequencing. Higher alpha diversity was associated with decreased fracture risk (hazard ratio [HR] 0.92 per standard deviation increase in Shannon index, 95% confidence interval 0.87-0.96). For beta diversity, the first principal component was associated with fracture risk (Aitchison distance, HR 0.90, 0.85-0.96). In predefined phyla analyses, we observed that the relative abundance of Proteobacteria was associated with increased fracture risk (HR 1.14, 1.07-1.20), while the relative abundance of Tenericutes was associated with decreased fracture risk (HR 0.90, 0.85-0.96). Explorative sub-analyses within the Proteobacteria phylum showed that higher relative abundance of Gammaproteobacteria was associated with increased fracture risk. Functionality analyses showed that pathways related to amino acid metabolism and lipopolysaccharide biosynthesis associated with fracture risk. The relative abundance of Proteobacteria correlated with pathways for amino acid metabolism, while the relative abundance of Tenericutes correlated with pathways for butyrate synthesis. In conclusion, the overall GM composition was associated with incident fractures. The relative abundance of Proteobacteria, especially Gammaproteobacteria, was associated with increased fracture risk, while the relative abundance of Tenericutes was associated with decreased fracture risk. Functionality analyses demonstrated that pathways known to regulate bone health may underlie these associations