Themed collection Computational Approaches in Multi-Omics Analysis

5 items
Open Access Review Article

Multi-omics data integration considerations and study design for biological systems and disease

Multi-omics data integration is used to investigate biological regulation of systems.

Graphical abstract: Multi-omics data integration considerations and study design for biological systems and disease
Open Access Research Article

Comprehensive analysis of epigenetic signatures of human transcription control

Advances in sequencing technologies have enabled exploration of epigenetic and transcriptional profiles at a genome-wide level.

Graphical abstract: Comprehensive analysis of epigenetic signatures of human transcription control
Research Article

Comparison between instrumental variable and mediation-based methods for reconstructing causal gene networks in yeast

Causal networks inferred from genomics and transcriptomics data overlap with known yeast transcriptional interactions and inform on causal hotspot genes.

Graphical abstract: Comparison between instrumental variable and mediation-based methods for reconstructing causal gene networks in yeast
Research Article

Prediction of cancer dependencies from expression data using deep learning

Novel deep learning methods for predicting gene dependencies and drug sensitivities from gene expression measurements.

Graphical abstract: Prediction of cancer dependencies from expression data using deep learning
Research Article

Identification of stem cells from large cell populations with topological scoring

Machine learning and topological analysis methods are becoming increasingly used on various large-scale omics datasets.

Graphical abstract: Identification of stem cells from large cell populations with topological scoring
5 items

About this collection

The rapid development of the multi-omics field has led to innovative computational methods to address computational and statistical challenges in large-scale data integration. This special web collection, Guest Edited by Professors Mike Washburn and Hyungwon Choi, is dedicated to showcasing the latest advances in Computational Approaches in Multi-omics Analysis. 
The focus of this collection encompasses, but is not limited to:
  • Statistical modeling and bioinformatic methods for the integration of genomics, transcriptomics, metabolomics and/or proteomics
  • Methods to integrate gene-centric omics data with epigenetic marks or small molecules
  • Computational approaches for proteogenomics analysis
  • Use of biological networks in the integration of two or more -omics data
  • Data visualization of omics technologies
  • Utilization of computational approaches to interrogate important biological problems with omics-scale data in human health and disease
New articles will be added as soon as they are published. Please return frequently to see the collection as it grows. 

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