Computational approaches leveraging integrated connections of multi-omic data toward clinical applications

Demirel, Habibe Cansu; Arici, Muslum Kaan; Tunçbağ, Nurcan

doi:10.1039/d1mo00158b

Cited by 14 publications

(9 citation statements)

References 130 publications

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“…The joint log-likelihood is approximated using a modified Newton–Raphson algorithm to estimate the model parameters. However, it has been criticised about being computationally intensive, and not robust as it requires a large number of runs to find a stable solution [32] . iClusterBayes [31] extends iClusterPlus to model binary genomic variables and count data from RNA sequencing.…”

Section: Predominant Multi-omics Computational Methods For the Select...mentioning

confidence: 99%

A guide to multi-omics data collection and integration for translational medicine

Athieniti¹,

Spyrou²

2023

Computational and Structural Biotechnology Journal

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Section: Predominant Multi-omics Computational Methods For the Select...mentioning

confidence: 99%

A guide to multi-omics data collection and integration for translational medicine

Athieniti¹,

Spyrou²

2023

Computational and Structural Biotechnology Journal

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“…However, in a multi-omics integrations context one seeks above all to connect information from different omics fields (transcriptomics, proteomics, metabolomics, lipidomics, and metabolomics ( Haas et al, 2017 ; Fan, Zhou and Ressom, 2020 ; Cansu Demirel, Kaan Arici and Tuncbag, 2022 ). In this context, multi-layer algorithms for visualization are preferable to force-directed algorithms ( Bodein et al, 2021 ; Dursun, Kwitek and Bozdag, 2021 ; Marín-Llaó et al, 2021 ).…”

Section: Methods Based On Text Miningmentioning

confidence: 99%

Overview of methods for characterization and visualization of a protein–protein interaction network in a multi-omics integration context

Robin

Bodein

Scott‐Boyer

et al. 2022

Front. Mol. Biosci.

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At the heart of the cellular machinery through the regulation of cellular functions, protein–protein interactions (PPIs) have a significant role. PPIs can be analyzed with network approaches. Construction of a PPI network requires prediction of the interactions. All PPIs form a network. Different biases such as lack of data, recurrence of information, and false interactions make the network unstable. Integrated strategies allow solving these different challenges. These approaches have shown encouraging results for the understanding of molecular mechanisms, drug action mechanisms, and identification of target genes. In order to give more importance to an interaction, it is evaluated by different confidence scores. These scores allow the filtration of the network and thus facilitate the representation of the network, essential steps to the identification and understanding of molecular mechanisms. In this review, we will discuss the main computational methods for predicting PPI, including ones confirming an interaction as well as the integration of PPIs into a network, and we will discuss visualization of these complex data.

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“…Moreover, alternative approaches such as deep learning network analysis and inclusion of multi-omics (e.g. genome wide DNAm and gene expression profiles) based datasets within machine learning algorithms will be implemented ( Demirel et al, 2021 ; Li et al, 2021 ; van der Vossen et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Further Introduction of DNA Methylation (DNAm) Arrays in Regular Diagnostics

et al. 2022

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Methylation tests have been used for decades in regular DNA diagnostics focusing primarily on Imprinting disorders or specific loci annotated to specific disease associated gene promotors. With the introduction of DNA methylation (DNAm) arrays such as the Illumina Infinium HumanMethylation450 Beadchip array or the Illumina Infinium Methylation EPIC Beadchip array (850 k), it has become feasible to study the epigenome in a timely and cost-effective way. This has led to new insights regarding the complexity of well-studied imprinting disorders such as the Beckwith Wiedemann syndrome, but it has also led to the introduction of tests such as EpiSign, implemented as a diagnostic test in which a single array experiment can be compared to databases with known episignatures of multiple genetic disorders, especially neurodevelopmental disorders. The successful use of such DNAm tests is rapidly expanding. More and more disorders are found to be associated with discrete episignatures which enables fast and definite diagnoses, as we have shown. The first examples of environmentally induced clinical disorders characterized by discrete aberrant DNAm are discussed underlining the broad application of DNAm testing in regular diagnostics. Here we discuss exemplary findings in our laboratory covering this broad range of applications and we discuss further use of DNAm tests in the near future.

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Computational approaches leveraging integrated connections of multi-omic data toward clinical applications

Abstract: In line with the advances in high-throughput technologies, multiple omic datasets have accumulated to study biological systems and diseases coherently. No single omics data type is capable of fully representing...

Cited by 14 publications

References 130 publications

A guide to multi-omics data collection and integration for translational medicine

A guide to multi-omics data collection and integration for translational medicine

Overview of methods for characterization and visualization of a protein–protein interaction network in a multi-omics integration context

Further Introduction of DNA Methylation (DNAm) Arrays in Regular Diagnostics

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