A Bayesian method for identification of stock mixtures from molecular marker data

Molecular markers have been demonstrated to be useful for the estimation of stock mixture proportions where the origin of individuals is determined from baseline samples. Bayesian statistical methods are widely recognized as providing a preferable strategy for such analyses. In general, Bayesian estimation is based on standard latent class models using data augmentation through Markov chain Monte Carlo techniques. In this study, we introduce a novel approach based on recent developments in the estimation of genetic population structure. Our strategy combines analytical integration with stochastic optimization to identify stock mixtures. An important enhancement over previous methods is the possibility of appropriately handling data where only partial baseline sample information is available. We address the potential use of nonmolecular, auxiliary biological information in our Bayesian model

SITReg: Multi-resolution architecture for symmetric, inverse consistent, and topology preserving image registration

Deep learning has emerged as a strong alternative for classical iterative methods for deformable medical image registration, where the goal is to find a mapping between the coordinate systems of two images. Popular classical image registration methods enforce the useful inductive biases of symmetricity, inverse consistency, and topology preservation by construct. However, while many deep learning registration methods encourage these properties via loss functions, none of the methods enforces all of them by construct. Here, we propose a novel registration architecture based on extracting multi-resolution feature representations which is by construct symmetric, inverse consistent, and topology preserving. We also develop an implicit layer for memory efficient inversion of the deformation fields. Our method achieves state-of-the-art registration accuracy on two datasets

Patient Outcome and Zero-shot Diagnosis Prediction with Hypernetwork-guided Multitask Learning

Multitask deep learning has been applied to patient outcome prediction from text, taking clinical notes as input and training deep neural networks with a joint loss function of multiple tasks. However, the joint training scheme of multitask learning suffers from inter-task interference, and diagnosis prediction among the multiple tasks has the generalizability issue due to rare diseases or unseen diagnoses. To solve these challenges, we propose a hypernetwork-based approach that generates task-conditioned parameters and coefficients of multitask prediction heads to learn task-specific prediction and balance the multitask learning. We also incorporate semantic task information to improves the generalizability of our task-conditioned multitask model. Experiments on early and discharge notes extracted from the real-world MIMIC database show our method can achieve better performance on multitask patient outcome prediction than strong baselines in most cases. Besides, our method can effectively handle the scenario with limited information and improve zero-shot prediction on unseen diagnosis categories.Comment: EACL 202

Mixture of Coupled HMMs for Robust Modeling of Multivariate Healthcare Time Series

Analysis of multivariate healthcare time series data is inherently challenging: irregular sampling, noisy and missing values, and heterogeneous patient groups with different dynamics violating exchangeability. In addition, interpretability and quantification of uncertainty are critically important. Here, we propose a novel class of models, a mixture of coupled hidden Markov models (M-CHMM), and demonstrate how it elegantly overcomes these challenges. To make the model learning feasible, we derive two algorithms to sample the sequences of the latent variables in the CHMM: samplers based on (i) particle filtering and (ii) factorized approximation. Compared to existing inference methods, our algorithms are computationally tractable, improve mixing, and allow for likelihood estimation, which is necessary to learn the mixture model. Experiments on challenging real-world epidemiological and semi-synthetic data demonstrate the advantages of the M-CHMM: improved data fit, capacity to efficiently handle missing and noisy measurements, improved prediction accuracy, and ability to identify interpretable subsets in the data.Comment: 9 pages, 7 figures, Proceedings of Machine Learning Research, Machine Learning for Health (ML4H) 202

Molekyylitason biologisten aineistojen rakenteen selvittäminen bayesläistä ositusmallia hyödyntäen

Advancements in the analysis techniques have led to a rapid accumulation of biological data in databases. Such data often are in the form of sequences of observations, examples including DNA sequences and amino acid sequences of proteins. The scale and quality of the data give promises of answering various biologically relevant questions in more detail than what has been possible before. For example, one may wish to identify areas in an amino acid sequence, which are important for the function of the corresponding protein, or investigate how characteristics on the level of DNA sequence affect the adaptation of a bacterial species to its environment. Many of the interesting questions are intimately associated with the understanding of the evolutionary relationships among the items under consideration. The aim of this work is to develop novel statistical models and computational techniques to meet with the challenge of deriving meaning from the increasing amounts of data. Our main concern is on modeling the evolutionary relationships based on the observed molecular data. We operate within a Bayesian statistical framework, which allows a probabilistic quantification of the uncertainties related to a particular solution. As the basis of our modeling approach we utilize a partition model, which is used to describe the structure of data by appropriately dividing the data items into clusters of related items. Generalizations and modifications of the partition model are developed and applied to various problems. Large-scale data sets provide also a computational challenge. The models used to describe the data must be realistic enough to capture the essential features of the current modeling task but, at the same time, simple enough to make it possible to carry out the inference in practice. The partition model fulfills these two requirements. The problem-specific features can be taken into account by modifying the prior probability distributions of the model parameters. The computational efficiency stems from the ability to integrate out the parameters of the partition model analytically, which enables the use of efficient stochastic search algorithms.Jatkuvasti kehittyvien laboratoriomenetelmien ansiosta molekyylitason biologista aineistoa on tarjolla tutkijoille enemmän kuin koskaan aiemmin. Ihmisen koko genomi on onnistuttu selvittämään ja useiden eri lajien DNA:sta on tarjolla yhä tarkempia tietoja. Geneettinen aineisto on yksi esimerkki sekvenssimuotoisesta aineistosta, jossa kutakin havaittua yksilöä kohden on tarjolla jono peräkkäisiä havaintoja, tässä tapauksessa esimerkiksi DNA:n muodostavia peräkkäisiä emäspareja. Toisenlaisen esimerkin sekvenssimuotoisesta aineistosta tarjoavat proteiinit, joiden rakenteen määrää peräkkäisten aminohappojen muodostama ketju. Koska aminohappojen järjestys proteiinissa on geenien määräämä, on myös tämäntyyppistä aineistoa käyttämällä mahdollista saada epäsuorasti tietoa tutkittavan yksilön genomista. Geeni- tai proteiinisekvensseihin perustuva aineisto tarjoaa oivan lähtökohdan yksilöiden evolutiivisten suhteiden arvioimiseen. Näiden evolutiivisten suhteiden selvittäminen tarjoaa mielenkiintoista tietoa evoluutiosta itsestään sekä sen mekanismeista ja vaikutuksesta esimerkiksi uusien lajien syntyyn. Käytännön sovellutusten kannalta on kiinnostavaa tutkia esimerkiksi kuinka bakteerit kehittävät lääkkeille vastustuskykyisiä kantoja, tai pyrkiä tunnistamaan automaattisesti aminohapposekvenssien pohjalta niitä kohtia sekvenssissä, jotka ovat proteiinin toiminnan kannalta keskeisiä. Vastausten ymmärtäminen näihin kysymyksiin antaa perustaa muunmuassa tulevaa lääkkeiden kehitystyötä varten. Tässä työssä kehitämme sekvenssimuotoiselle aineistolle soveltuvia malllinnustyökaluja, joita käyttämällä tutkijoilla on mahdollisuus ymmärtää havaittujen yksilöiden evolutiivisia suhteita, sekä mallintaa evoluutioon vaikuttavia mekanismeja. Lähtökohtana on todennäköisyyksiin perustuva ositusmalli, jolla kuvataan aineistoon kuuluvien yksilöiden muodostamia eriytyneitä ryhmiä. Osatöissä kehitetään mallista eri tilanteisiin soveltuvia muotoiluja ja yleistyksiä. Aineistojen iso koko aiheuttaa käytännössä lisähaasteen laskennan toteuttamiselle. Tähän haasteeseen on työssä vastattu kehittämällä uudenlaisia laskennallisia lähestymistapoja, jotka mahdollistavat isojenkin aineistojen analysoinnin kohtuullisessa ajassa

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

Efficient Bayesian approach for multilocus association mapping including gene-gene interactions

Peer reviewe

Parallel Gaussian Process Surrogate Bayesian Inference with Noisy Likelihood Evaluations

We consider Bayesian inference when only a limited number of noisy log-likelihood evaluations can be obtained. This occurs for example when complex simulator-based statistical models are fitted to data, and synthetic likelihood (SL) method is used to form the noisy log-likelihood estimates using computationally costly forward simulations. We frame the inference task as a sequential Bayesian experimental design problem, where the log-likelihood function is modelled with a hierarchical Gaussian process (GP) surrogate model, which is used to efficiently select additional log-likelihood evaluation locations. Motivated by recent progress in the related problem of batch Bayesian optimisation, we develop various batch-sequential design strategies which allow to run some of the potentially costly simulations in parallel. We analyse the properties of the resulting method theoretically and empirically. Experiments with several toy problems and simulation models suggest that our method is robust, highly parallelisable, and sample-efficient.Peer reviewe