Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

186e322c-68da-4a70-befc-87249cc1e689 - samples

ABSTRACT: Identification of patients with life-threatening diseases including leukemias or infections such as tuberculosis or COVID-19 is an important goal of modern precision medicine. However, there is an increasing divide between what is technically possible and what is allowed because of privacy legislation. We have recently illustrated that classical machine learning can identify leukemia patients based on their blood transcriptomes. To facilitate integration of any omics data from any data owner world-wide without violating privacy laws, we here introduce Swarm Learning (SL), a decentralized machine learning approach uniting edge computing, artificial intelligence (AI), blockchain and privacy protection without the need for a central coordinator thereby going beyond federated learning. To illustrate its feasibility, using more than 12,000 transcriptomes from peripheral blood mononuclear cells and more than 2,000 peripheral blood transcriptomes we demonstrate that SL of omics data distributed across different individual sites leads to disease classifiers that outperform those developed at individual sites. Yet, SL completely protects local privacy regulations by design. We propose this approach to noticeably accelerate the introduction of precision medicine.

PROVIDER: EGAD00001006231 | EGA |

REPOSITORIES: EGA

ACCESS DATA

Json Xml

Similar Datasets

Swarm Learning to identify COVID-19, tuberculosis and leukemia patients based on blood transcriptomes

Project description:Identification of patients with life-threatening diseases including leukemias or infections such as tuberculosis or COVID-19 is an important goal of modern precision medicine. However, there is an increasing divide between what is technically possible and what is allowed because of privacy legislation. We have recently illustrated that classical machine learning can identify leukemia patients based on their blood transcriptomes. To facilitate integration of any omics data from any data owner world-wide without violating privacy laws, we here introduce Swarm Learning (SL), a decentralized machine learning approach uniting edge computing, artificial intelligence (AI), blockchain and privacy protection without the need for a central coordinator thereby going beyond federated learning. To illustrate its feasibility, using more than 12,000 transcriptomes from peripheral blood mononuclear cells and more than 2,000 peripheral blood transcriptomes we demonstrate that SL of omics data distributed across different individual sites leads to disease classifiers that outperform those developed at individual sites. Yet, SL completely protects local privacy regulations by design. We propose this approach to noticeably accelerate the introduction of precision medicine.

| EGAS00001004502 | EGA

Intergrative Multi-Omics of Asian Pregnant Mother

Project description:As Asians are underrepresented across many omics databases limiting the potential of precision medicine of the global population. It is thus important for multi-omics derived quantitative trait loci (QTLs) to fill the knowledge gap of complex traits in the Asian ancestry. Integrating omics data from genomics and epigenomics (including DNA methylation and RNA-seq from blood), we developed iMOMdb, an open-acesss database to enhance disease prediction models and precision medicine outcomes in Asian pregnant women.

2022-10-04 | GSE182409 | GEO

A Machine-learning approach to define and predict the therapeutic landscape of pan-cancer Hippo pathway dependency (ATAC-seq)

Project description:One challenge of cancer precision medicine is the heterogeneity of genetic and non-genetic alterations that result in dysfunctional molecular pathways. As an emerging drug discovery effort, dysregulation in Hippo pathway signaling is known to drive oncogenesis across numerous cancer types but lacks recurrent mutation(s) that are often found in other canonical signaling pathways. Here, we use first principles approach to develop a machine-learning framework to identify a robust, lineage-independent gene expression signature to quantify Hippo pathway dependency in cancers. Through integrating data from multi-omics platforms, this data-driven approach has enabled identifying a proposed combination with MAPK inhibition for direct targeting of Hippo pathway dependent cancers for which we then elucidate the underlying molecular mechanism. The results underscore how a multifaceted approach, computational models combined with laboratory efforts, can accelerate precision medicine efforts toward co-targeting Hippo and MAPK pathways, an approach that can be generalized to other key cancer signaling pathways to define therapeutic strategies.

2021-01-26 | GSE161018 | GEO

A Machine-learning approach to define and predict the therapeutic landscape of pan-cancer Hippo pathway dependency (RNA-seq)

2021-01-26 | GSE161010 | GEO

A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia

Project description:A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia

| PRJNA422201 | ENA

A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia [array]

Project description:A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia [array]

| PRJNA420193 | ENA

A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia [RNA-Seq]

Project description:A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia [RNA-Seq]

| PRJNA422203 | ENA

Nasal DNA methylation at three CpG sites predicts childhood allergic disease

Project description:We used machine learning models to systematically integrate large-scale multi-omics data from genomics, DNA methylation in blood, and nasal and environmental factors. We firstly evaluated the prediction power of each omics layer and identified that the prediction power was largely attributed to nasal methylation. We further built a prediction model based on only three nasal CpG sites that can predict childhood allergic disease.

| EGAS00001005189 | EGA

Precision medicine strategies for inflammatory bowel disease

Project description:Combining explainable machine learning, demographic and multi-omic data to identify precision medicine strategies for inflammatory bowel disease

| PRJEB43220 | ENA

Project description:Machine Learning-Based Approach Highlights the Use of a Genomic Variant Profile for Precision Medicine in Ovarian Failure

| PRJNA1010150 | ENA