- probabilistic multi|omics data matching method for detecting sample ...🔍
- A probabilistic multi|omics data matching method for detecting ...🔍
- proMODMatcher🔍
- A Detailed Catalogue of Multi|Omics Methodologies for Identification ...🔍
- Multi|omics single|cell data integration and regulatory inference with ...🔍
- Deep learning|based approaches for multi|omics data integration ...🔍
- Deterministic vs probabilistic matching🔍
- Deterministic and Probabilistic Matching🔍
probabilistic multi|omics data matching method for detecting sample ...
probabilistic multi-omics data matching method for detecting sample ...
We developed a robust probabilistic multi-omics data matching procedure, proMODMatcher, to curate data and identify and correct data annotation and errors in ...
A probabilistic multi-omics data matching method for detecting ...
More importantly, data analysis with sample errors could lead to wrong scientific conclusions. We developed a robust probabilistic multi-omics ...
(PDF) A probabilistic multi-omics data matching method for detecting ...
More importantly, data analysis with sample errors could lead to wrong scientific conclusions. We developed a robust probabilistic multi-omics data matching ...
A probabilistic multi-omics data matching method for detecting ...
There were. 519 tumor samples with both mRNA expression measured in. Agilent microarray and miRNA expression measured by small-. RNA sequencing ...
A probabilistic multi-omics data matching method for detecting ...
Application of our proMODMatcher to multi-omics datasets in The Cancer Genome Atlas and International Cancer Genome Consortium identified sample errors in ...
A probabilistic multi-omics data matching method for detecting ...
Abstract Background Data errors, including sample swapping and mis-labeling, are inevitable in the process of large-scale omics data generation.
A probabilistic multi-omics data matching method for detecting ...
Abstract Background Data errors, including sample swapping and mis-labeling, are inevitable in the process of large-scale omics data generation.
... at dkNET. Lee E, et al. (2019) A probabilistic multi-omics data matching method for detecting sample errors in integrative analysis. GigaScience, 8(7).
A Detailed Catalogue of Multi-Omics Methodologies for Identification ...
One representative example of published studies using cancer genomic data on a global scale is The Cancer Genome Atlas (TCGA) consortium, a landmark cancer ...
Multi-omics single-cell data integration and regulatory inference with ...
Despite the emergence of experimental methods for simultaneous measurement of multiple omics modalities in single cells, most single-cell ...
Deep learning-based approaches for multi-omics data integration ...
Multiomics, or integrative omics or panomics, is a comprehensive approach to biological analysis. It involves simultaneously studying multiple ' ...
Deterministic vs probabilistic matching - Melissa Data
Deterministic matching is a technique used to find an exact match between records. Probabilistic matching involves matching records based on the degree of ...
Deterministic and Probabilistic Matching: How Do They Work?
Probabilistic matching uses various data sets (listed above) and algorithms to identify the same user across different devices and applications. Let's imagine ...
A guide to multi-omics data collection and integration for ...
However, the complexity of integrating these datasets has triggered new questions regarding the appropriateness of the available computational methods.
What Is Probabilistic Matching and Why Is It Superior to Fuzzy ...
The Expectation-Maximization (EM) algorithm is commonly used in probabilistic linkage to handle missing data and optimize the match ...
Missing data in multi-omics integration: Recent advances through ...
A principal challenge to multi-omic integration is missing data because all biomolecules are not measured in all samples.
Integration of Meta-Multi-Omics Data Using Probabilistic Graphs and ...
In this paper, we present an algorithm that uses probabilistic graph representations and external knowledge to perform optimal structure ...
Probabilistic Machine Learning with Omics Data and Biological Prior ...
For example, we found that the method used more than 32GB of RAM on problems ... Single Sample Gene-set Analysis of Multiple Omics Data *. Molecular ...
Knowledge-guided learning methods for integrative analysis of multi ...
For instance, -omics data are usually high-dimensional, and sample sizes in multi-omics studies tend to be modest. Furthermore, when genes in an important ...
sume that the sequence data are uniformly sampled. (i.e., uniformly spaced in time). The generalization to the non-uniformly sampled case is straightforward.