Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Australian Genomics: A Federated Model for Integrating Genomics into Healthcare.

ABSTRACT: Australian Genomics is a national collaborative research partnership of more than 80 organizations piloting a whole-of-system approach to integrating genomics into healthcare that is based on federation principles. The aim of Australian Genomics is to assess the application of genomic testing in healthcare at the translational interface between research and clinical delivery, with an emphasis on robust evaluation of outcomes. It encompasses two bodies of work: a research program prospectively providing genomic testing through exemplar clinical projects in rare diseases, cancers, and reproductive carrier screening and interdependent programs for advancing the diagnostic, health informatics, regulatory, ethical, policy, and workforce infrastructure necessary for the integration of genomics into the Australian health system.

SUBMITTER: Stark Z

PROVIDER: S-EPMC6612707 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Australian Genomics: A Federated Model for Integrating Genomics into Healthcare.

Stark Zornitza Z Boughtwood Tiffany T Phillips Peta P Christodoulou John J Hansen David P DP Braithwaite Jeffrey J Newson Ainsley J AJ Gaff Clara L CL Sinclair Andrew H AH North Kathryn N KN

American journal of human genetics 20190701 1

Australian Genomics is a national collaborative research partnership of more than 80 organizations piloting a whole-of-system approach to integrating genomics into healthcare that is based on federation principles. The aim of Australian Genomics is to assess the application of genomic testing in healthcare at the translational interface between research and clinical delivery, with an emphasis on robust evaluation of outcomes. It encompasses two bodies of work: a research program prospectively pr ...[more]

PMID: 31271757

Similar Datasets

Integrating Transcriptomics, Genomics, and Imaging in Alzheimer's Disease: A Federated Model.

Project description:Alzheimer's disease (AD) affects more than 1 in 9 people age 65 and older and becomes an urgent public health concern as the global population ages. In clinical practice, structural magnetic resonance imaging (sMRI) is the most accessible and widely used diagnostic imaging modality. Additionally, genome-wide association studies (GWAS) and transcriptomics-the study of gene expression-also play an important role in understanding AD etiology and progression. Sophisticated imaging genetics systems have been developed to discover genetic factors that consistently affect brain function and structure. However, most studies to date focused on the relationships between brain sMRI and GWAS or brain sMRI and transcriptomics. To our knowledge, few methods have been developed to discover and infer multimodal relationships among sMRI, GWAS, and transcriptomics. To address this, we propose a novel federated model, Genotype-Expression-Imaging Data Integration (GEIDI), to identify genetic and transcriptomic influences on brain sMRI measures. The relationships between brain imaging measures and gene expression are allowed to depend on a person's genotype at the single-nucleotide polymorphism (SNP) level, making the inferences adaptive and personalized. We performed extensive experiments on publicly available Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Experimental results demonstrated our proposed method outperformed state-of-the-art expression quantitative trait loci (eQTL) methods for detecting genetic and transcriptomic factors related to AD and has stable performance when data are integrated from multiple sites. Our GEIDI approach may offer novel insights into the relationship among image biomarkers, genotypes, and gene expression and help discover novel genetic targets for potential AD drug treatments.

| S-EPMC10365097 | biostudies-literature

Integrating Genomics into Healthcare: A Global Responsibility.

Project description:Genomic sequencing is rapidly transitioning into clinical practice, and implementation into healthcare systems has been supported by substantial government investment, totaling over US$4 billion, in at least 14 countries. These national genomic-medicine initiatives are driving transformative change under real-life conditions while simultaneously addressing barriers to implementation and gathering evidence for wider adoption. We review the diversity of approaches and current progress made by national genomic-medicine initiatives in the UK, France, Australia, and US and provide a roadmap for sharing strategies, standards, and data internationally to accelerate implementation.

| S-EPMC6323624 | biostudies-literature

Detecting model misconducts in decentralized healthcare federated learning.

Project description:BackgroundTo accelerate healthcare/genomic medicine research and facilitate quality improvement, researchers have started cross-institutional collaborations to use artificial intelligence on clinical/genomic data. However, there are real-world risks of incorrect models being submitted to the learning process, due to either unforeseen accidents or malicious intent. This may reduce the incentives for institutions to participate in the federated modeling consortium. Existing methods to deal with this "model misconduct" issue mainly focus on modifying the learning methods, and therefore are more specifically tied with the algorithm.Basic proceduresIn this paper, we aim at solving the problem in an algorithm-agnostic way by (1) designing a simulator to generate various types of model misconduct, (2) developing a framework to detect the model misconducts, and (3) providing a generalizable approach to identify model misconducts for federated learning. We considered the following three categories: Plagiarism, Fabrication, and Falsification, and then developed a detection framework with three components: Auditing, Coefficient, and Performance detectors, with greedy parameter tuning.Main findingsWe generated 10 types of misconducts from models learned on three datasets to evaluate our detection method. Our experiments showed high recall with low added computational cost. Our proposed detection method can best identify the misconduct on specific sites from any learning iteration, whereas it is more challenging to precisely detect misconducts for a specific site and at a specific iteration.Principal conclusionsWe anticipate our study can support the enhancement of the integrity and reliability of federated machine learning on genomic/healthcare data.

| S-EPMC10017272 | biostudies-literature

Precision genomics in healthcare

Project description:Workflow for the implementation of precision genomics in healthcare

| PRJEB38157 | ENA

Federated learning framework integrating REFINED CNN and Deep Regression Forests

Project description:Abstract Summary Predictive learning from medical data incurs additional challenge due to concerns over privacy and security of personal data. Federated learning, intentionally structured to preserve high level of privacy, is emerging to be an attractive way to generate cross-silo predictions in medical scenarios. However, the impact of severe population-level heterogeneity on federated learners is not well explored. In this article, we propose a methodology to detect presence of population heterogeneity in federated settings and propose a solution to handle such heterogeneity by developing a federated version of Deep Regression Forests. Additionally, we demonstrate that the recently conceptualized REpresentation of Features as Images with NEighborhood Dependencies CNN framework can be combined with the proposed Federated Deep Regression Forests to provide improved performance as compared to existing approaches. Availability and implementation The Python source code for reproducing the main results are available on GitHub: https://github.com/DanielNolte/FederatedDeepRegressionForests. Contact ranadip.pal@ttu.edu Supplementary information Supplementary data are available at Bioinformatics Advances online.

| S-EPMC10074025 | biostudies-literature

A Surface-Based Federated Chow Test Model for Integrating APOE Status, Tau Deposition Measure, and Hippocampal Surface Morphometry.

Project description:BackgroundAlzheimer's disease (AD) is the most common type of age-related dementia, affecting 6.2 million people aged 65 or older according to CDC data. It is commonly agreed that discovering an effective AD diagnosis biomarker could have enormous public health benefits, potentially preventing or delaying up to 40% of dementia cases. Tau neurofibrillary tangles are the primary driver of downstream neurodegeneration and subsequent cognitive impairment in AD, resulting in structural deformations such as hippocampal atrophy that can be observed in magnetic resonance imaging (MRI) scans.ObjectiveTo build a surface-based model to 1) detect differences between APOE subgroups in patterns of tau deposition and hippocampal atrophy, and 2) use the extracted surface-based features to predict cognitive decline.MethodsUsing data obtained from different institutions, we develop a surface-based federated Chow test model to study the synergistic effects of APOE, a previously reported significant risk factor of AD, and tau on hippocampal surface morphometry.ResultsWe illustrate that the APOE-specific morphometry features correlate with AD progression and better predict future AD conversion than other MRI biomarkers. For example, a strong association between atrophy and abnormal tau was identified in hippocampal subregion cornu ammonis 1 (CA1 subfield) and subiculum in e4 homozygote cohort.ConclusionOur model allows for identifying MRI biomarkers for AD and cognitive decline prediction and may uncover a corner of the neural mechanism of the influence of APOE and tau deposition on hippocampal morphology.

| S-EPMC10329869 | biostudies-literature

Integrating Healthcare Services for Indigenous Australian Students at Boarding Schools: A Mixed-Methods Sequential Explanatory Study.

Project description:Introduction:Many Aboriginal and Torres Strait Islander Australian adolescents from remote communities attend boarding schools, requiring integrated healthcare between home and schools. This study explored students' health status, healthcare service use and satisfaction. Methodology:A two-phased mixed-methods explanatory design was implemented. 32 Indigenous primary and 188 secondary boarding school students were asked their health status, psychological distress, use of healthcare services in community and boarding school, and service satisfaction. Results were fed back to students, parents and community members, and education and healthcare staff to elicit further explanation and interpretation. Results:In the previous year, 75% of primary and 81% of secondary boarding school students had visited a doctor. More than 90% were satisfied with healthcare services used. Despite 27.1% reporting high psychological distress, students did not perceive distress as reducing their overall health, nor was distress associated with mental healthcare service use. Discussion:Despite high levels of service use and satisfaction, this study highlighted the need for improved healthcare integration for Indigenous adolescents between school-based and remote community services. Further research is needed to identify students' expectations and models for healthcare integration. Conclusion:With resourcing, schools could play a greater role in facilitating access to healthcare.

| S-EPMC7068848 | biostudies-literature

Australian Genomics

Project description:Data Access Committee EGAC00001003369

| EGAC00001003369 | EGA

Blockchain-Based Healthcare Workflow for Tele-Medical Laboratory in Federated Hospital IoT Clouds.

Project description:In a pandemic situation such as that we are living at the time of writing of this paper due to the Covid-19 virus, the need of tele-healthcare service becomes dramatically fundamental to reduce the movement of patients, thence reducing the risk of infection. Leveraging the recent Cloud computing and Internet of Things (IoT) technologies, this paper aims at proposing a tele-medical laboratory service where clinical exams are performed on patients directly in a hospital by technicians through IoT medical devices and results are automatically sent via the hospital Cloud to doctors of federated hospitals for validation and/or consultation. In particular, we discuss a distributed scenario where nurses, technicians and medical doctors belonging to different hospitals cooperate through their federated hospital Clouds to form a virtual health team able to carry out a healthcare workflow in secure fashion leveraging the intrinsic security features of the Blockchain technology. In particular, both public and hybrid Blockchain scenarios are discussed and assessed using the Ethereum platform.

| S-EPMC7249089 | biostudies-literature

Childhood acute lymphoblastic leukemia: Integrating genomics into therapy.

Project description:Acute lymphoblastic leukemia (ALL), the most common malignancy of childhood, is a genetically complex entity that remains a major cause of childhood cancer-related mortality. Major advances in genomic and epigenomic profiling during the past decade have appreciably enhanced knowledge of the biology of de novo and relapsed ALL and have facilitated more precise risk stratification of patients. These achievements have also provided critical insights regarding potentially targetable lesions for the development of new therapeutic approaches in the era of precision medicine. In this review, the authors delineate the current genetic landscape of childhood ALL, emphasizing patient outcomes with contemporary treatment regimens as well as therapeutic implications of newly identified genomic alterations in specific subsets of ALL.

| S-EPMC4592406 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data