Themed collection Computational Approaches in Multi-Omics Analysis
5 items
Review Article
Multi-omics data integration considerations and study design for biological systems and disease
Stefan Graw, Kevin Chappell, Charity L. Washam, Allen Gies, Jordan Bird, Michael S. Robeson and Stephanie D. Byrum
Multi-omics data integration is used to investigate biological regulation of systems.
From the themed collection:
Computational Approaches in Multi-Omics Analysis
The article was first published on 21 Dec 2020
Mol. Omics, 2021,17, 170-185
https://doi.org/10.1039/D0MO00041H
Mol. Omics, 2021,17, 170-185
https://doi.org/10.1039/D0MO00041H
Research Article
Comprehensive analysis of epigenetic signatures of human transcription control
Guillaume Devailly and Anagha Joshi
Advances in sequencing technologies have enabled exploration of epigenetic and transcriptional profiles at a genome-wide level.
From the themed collection:
Computational Approaches in Multi-Omics Analysis
The article was first published on 21 Jun 2021
Mol. Omics, 2021,17, 692-705
https://doi.org/10.1039/D0MO00130A
Mol. Omics, 2021,17, 692-705
https://doi.org/10.1039/D0MO00130A
Research Article
Comparison between instrumental variable and mediation-based methods for reconstructing causal gene networks in yeast
Adriaan-Alexander Ludl and Tom Michoel
Causal networks inferred from genomics and transcriptomics data overlap with known yeast transcriptional interactions and inform on causal hotspot genes.
From the themed collection:
Computational Approaches in Multi-Omics Analysis
The article was first published on 17 Dec 2020
Mol. Omics, 2021,17, 241-251
https://doi.org/10.1039/D0MO00140F
Mol. Omics, 2021,17, 241-251
https://doi.org/10.1039/D0MO00140F
Research Article
Prediction of cancer dependencies from expression data using deep learning
Nitay Itzhacky and Roded Sharan
Novel deep learning methods for predicting gene dependencies and drug sensitivities from gene expression measurements.
From the themed collection:
Computational Approaches in Multi-Omics Analysis
The article was first published on 02 Nov 2020
Mol. Omics, 2021,17, 66-71
https://doi.org/10.1039/D0MO00042F
Mol. Omics, 2021,17, 66-71
https://doi.org/10.1039/D0MO00042F
Research Article
Identification of stem cells from large cell populations with topological scoring
Mihaela E. Sardiu, Andrew C. Box, Jeffrey S. Haug and Michael P. Washburn
Machine learning and topological analysis methods are becoming increasingly used on various large-scale omics datasets.
From the themed collection:
Computational Approaches in Multi-Omics Analysis
The article was first published on 13 Aug 2020
Mol. Omics, 2021,17, 59-65
https://doi.org/10.1039/D0MO00039F
Mol. Omics, 2021,17, 59-65
https://doi.org/10.1039/D0MO00039F
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.