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Oncogenic Gene Fusion Detection Using Anchored Multiplex Polymerase Chain Reaction Followed by Next Generation Sequencing

Oncogenic Gene Fusion Detection Using Anchored Multiplex Polymerase Chain Reaction Followed by Next Generation Sequencing

Published on: July 5, 2019

Structure-revealing data fusion.

Evrim Acar¹, Evangelos E Papalexakis, Gözde Gürdeniz

¹Department of Food Science, Faculty of Science, University of Copenhagen, Frederiksberg C, Denmark. evrim@life.ku.dk.

BMC Bioinformatics

|July 13, 2014

Summary

This summary is machine-generated.

This study introduces a novel unsupervised data fusion model for analyzing complex datasets. The model effectively identifies shared and unshared components in heterogeneous, incomplete data, enhancing knowledge discovery.

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Oncogenic Gene Fusion Detection Using Anchored Multiplex Polymerase Chain Reaction Followed by Next Generation Sequencing

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Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Published on: July 14, 2015

Area of Science:

Data Science
Bioinformatics
Computational Chemistry

Background:

Data fusion enhances knowledge discovery by integrating information from multiple sources.
Challenges in data fusion include data heterogeneity (tensors, matrices), incompleteness, and mixed shared/unshared components.
Existing coupled matrix and tensor factorization methods often fail to capture both shared and unshared factors.

Purpose of the Study:

To introduce a novel unsupervised data fusion model capable of jointly analyzing heterogeneous and incomplete datasets.
To address the limitations of traditional methods in capturing both shared and unshared components.
To demonstrate the model's effectiveness in revealing underlying structures in complex data.

Main Methods:

Development of a novel unsupervised data fusion model based on joint factorization of matrices and higher-order tensors.
Incorporation of modeling constraints to automatically differentiate between shared and unshared components.
Validation using numerical experiments and real-world chemical mixture analysis via LC-MS and NMR.

Main Results:

The proposed model successfully identifies both shared and unshared components in complex datasets.
Demonstrated effectiveness in analyzing chemical mixtures using Liquid Chromatography - Mass Spectrometry (LC-MS) and Nuclear Magnetic Resonance (NMR) data.
Accurate extraction of chemical concentrations and identification of shared/unshared chemicals within mixtures.

Conclusions:

A structure-revealing data fusion model was developed for joint analysis of heterogeneous, incomplete data with shared and unshared components.
The model shows promising performance in both simulated and real-world data analysis.
Potential limitations were identified, guiding future research directions.