8 research outputs found
Causal Similarity-Based Hierarchical Bayesian Models
The key challenge underlying machine learning is generalisation to new data.
This work studies generalisation for datasets consisting of related tasks that
may differ in causal mechanisms. For example, observational medical data for
complex diseases suffers from heterogeneity in causal mechanisms of disease
across patients, creating challenges for machine learning algorithms that need
to generalise to new patients outside of the training dataset. Common
approaches for learning supervised models with heterogeneous datasets include
learning a global model for the entire dataset, learning local models for each
tasks' data, or utilising hierarchical, meta-learning and multi-task learning
approaches to learn how to generalise from data pooled across multiple tasks.
In this paper we propose causal similarity-based hierarchical Bayesian models
to improve generalisation to new tasks by learning how to pool data from
training tasks with similar causal mechanisms. We apply this general modelling
principle to Bayesian neural networks and compare a variety of methods for
estimating causal task similarity (for both known and unknown causal models).
We demonstrate the benefits of our approach and applicability to real world
problems through a range of experiments on simulated and real data
Characterizing personalized effects of family information on disease risk using graph representation learning
Family history is considered a risk factor for many diseases because it
implicitly captures shared genetic, environmental and lifestyle factors. A
nationwide electronic health record (EHR) system spanning multiple generations
presents new opportunities for studying a connected network of medical
histories for entire families. In this work we present a graph-based deep
learning approach for learning explainable, supervised representations of how
each family member's longitudinal medical history influences a patient's
disease risk. We demonstrate that this approach is beneficial for predicting
10-year disease onset for 5 complex disease phenotypes, compared to
clinically-inspired and deep learning baselines for a nationwide EHR system
comprising 7 million individuals with up to third-degree relatives. Through the
use of graph explainability techniques, we illustrate that a graph-based
approach enables more personalized modeling of family information and disease
risk by identifying important relatives and features for prediction
Characterizing personalized effects of family information on disease risk using graph representation learning
Family history is considered a risk factor for many diseases because it implicitly captures shared genetic, environmental and lifestyle factors. Finland’s nationwide electronic health record (EHR) system spanning multiple generations presents new opportunities for studying a connected network of medical histories for entire families. In this work we present a graph-based deep learning approach for learning explainable, supervised representations of how each family member’s longitudinal medical history influences a patient’s disease risk. We demonstrate that this approach is beneficial for predicting 10-year disease onset for 5 complex disease phenotypes, compared to clinically-inspired and deep learning baselines for Finland’s nationwide EHR system comprising 7 million individuals with up to third-degree relatives. Through the use of graph explainability techniques, we illustrate that a graph-based approach enables more personalized modeling of family information and disease risk by identifying important relatives and features for prediction
HAPNEST : efficient, large-scale generation and evaluation of synthetic datasets for genotypes and phenotypes
| openaire: EC/H2020/101016775/EU//INTERVENEMOTIVATION: Existing methods for simulating synthetic genotype and phenotype datasets have limited scalability, constraining their usability for large-scale analyses. Moreover, a systematic approach for evaluating synthetic data quality and a benchmark synthetic dataset for developing and evaluating methods for polygenic risk scores are lacking. RESULTS: We present HAPNEST, a novel approach for efficiently generating diverse individual-level genotypic and phenotypic data. In comparison to alternative methods, HAPNEST shows faster computational speed and a lower degree of relatedness with reference panels, while generating datasets that preserve key statistical properties of real data. These desirable synthetic data properties enabled us to generate 6.8 million common variants and nine phenotypes with varying degrees of heritability and polygenicity across 1 million individuals. We demonstrate how HAPNEST can facilitate biobank-scale analyses through the comparison of seven methods to generate polygenic risk scoring across multiple ancestry groups and different genetic architectures. AVAILABILITY AND IMPLEMENTATION: A synthetic dataset of 1 008 000 individuals and nine traits for 6.8 million common variants is available at https://www.ebi.ac.uk/biostudies/studies/S-BSST936. The HAPNEST software for generating synthetic datasets is available as Docker/Singularity containers and open source Julia and C code at https://github.com/intervene-EU-H2020/synthetic_data.Peer reviewe
HAPNEST : efficient, large-scale generation and evaluation of synthetic datasets for genotypes and phenotypes
| openaire: EC/H2020/101016775/EU//INTERVENEMOTIVATION: Existing methods for simulating synthetic genotype and phenotype datasets have limited scalability, constraining their usability for large-scale analyses. Moreover, a systematic approach for evaluating synthetic data quality and a benchmark synthetic dataset for developing and evaluating methods for polygenic risk scores are lacking. RESULTS: We present HAPNEST, a novel approach for efficiently generating diverse individual-level genotypic and phenotypic data. In comparison to alternative methods, HAPNEST shows faster computational speed and a lower degree of relatedness with reference panels, while generating datasets that preserve key statistical properties of real data. These desirable synthetic data properties enabled us to generate 6.8 million common variants and nine phenotypes with varying degrees of heritability and polygenicity across 1 million individuals. We demonstrate how HAPNEST can facilitate biobank-scale analyses through the comparison of seven methods to generate polygenic risk scoring across multiple ancestry groups and different genetic architectures. AVAILABILITY AND IMPLEMENTATION: A synthetic dataset of 1 008 000 individuals and nine traits for 6.8 million common variants is available at https://www.ebi.ac.uk/biostudies/studies/S-BSST936. The HAPNEST software for generating synthetic datasets is available as Docker/Singularity containers and open source Julia and C code at https://github.com/intervene-EU-H2020/synthetic_data.Peer reviewe