Challenges and opportunities in network-based solutions for biological questions

Guo, Margaret; Sosa, Daniel N.; Altman, Russ B.

doi:10.1093/bib/bbab437

Cited by 17 publications

(9 citation statements)

References 59 publications

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“…The capacity to interpret predicted features and interactions of known biological relevance may take the form of deductive reasoning or semantic similarities to support a hypothesis ( Guo et al, 2022 ). In the context of algorithms, robust node weighting and edge weighting metrics measured based on known evidence (e.g., text mining, contextualized pathway information) is important to make an inference that is potentially biologically grounded and experimentally confirmable, knowing that the association between omics layers extends from one-to-one and one-to-many to many-to-many.…”

Section: Current Challenges and Recommendationsmentioning

confidence: 99%

“…Some multi-omics data integration methods can handle sparse data and also feature reduction methods; however, skewed estimates might result in a biased interpretation of results ( Greenland et al, 2016 ). To address the issue of sparsity in the context of networks, network integration aggregates independent data sources to form a more comprehensive attributed interactome, where the edges are qualified by specific semantic relations or similarity correlation, and the level of confidence in the node pair relationship based on evidence from similarity scores, literature and graph databases ( Guo et al, 2022 ). Also, incorporating autoencoders, a deep learning approach, and its denoising and variational variants autoencoders (e.g., sparse autoencoders) have been used to address this issue in graph neural networks ( Ng, 2011 ).…”

Section: Current Challenges and Recommendationsmentioning

confidence: 99%

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Computational approaches for network-based integrative multi-omics analysis

Agamah

Bayjanov

Niehues

et al. 2022

Front. Mol. Biosci.

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Advances in omics technologies allow for holistic studies into biological systems. These studies rely on integrative data analysis techniques to obtain a comprehensive view of the dynamics of cellular processes, and molecular mechanisms. Network-based integrative approaches have revolutionized multi-omics analysis by providing the framework to represent interactions between multiple different omics-layers in a graph, which may faithfully reflect the molecular wiring in a cell. Here we review network-based multi-omics/multi-modal integrative analytical approaches. We classify these approaches according to the type of omics data supported, the methods and/or algorithms implemented, their node and/or edge weighting components, and their ability to identify key nodes and subnetworks. We show how these approaches can be used to identify biomarkers, disease subtypes, crosstalk, causality, and molecular drivers of physiological and pathological mechanisms. We provide insight into the most appropriate methods and tools for research questions as showcased around the aetiology and treatment of COVID-19 that can be informed by multi-omics data integration. We conclude with an overview of challenges associated with multi-omics network-based analysis, such as reproducibility, heterogeneity, (biological) interpretability of the results, and we highlight some future directions for network-based integration.

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Section: Current Challenges and Recommendationsmentioning

confidence: 99%

Section: Current Challenges and Recommendationsmentioning

confidence: 99%

Computational approaches for network-based integrative multi-omics analysis

Agamah

Bayjanov

Niehues

et al. 2022

Front. Mol. Biosci.

View full text Add to dashboard Cite

show abstract

“…(2) Furthermore, the integration of heterogenous and high-dimensional datasets generally has to deal with disparate, incompatible or missing information [ 145 ]. To merge multiple datasets into a homogenous network would compromise accuracy due to the disregarding of biological and experimental variations affiliated with each dataset [ 146 ].…”

Section: Challenges Of Network-based Drug Repurposing In Psychiatrymentioning

confidence: 99%

Repurposing Drugs via Network Analysis: Opportunities for Psychiatric Disorders

et al. 2022

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Despite advances in pharmacology and neuroscience, the path to new medications for psychiatric disorders largely remains stagnated. Drug repurposing offers a more efficient pathway compared with de novo drug discovery with lower cost and less risk. Various computational approaches have been applied to mine the vast amount of biomedical data generated over recent decades. Among these methods, network-based drug repurposing stands out as a potent tool for the comprehension of multiple domains of knowledge considering the interactions or associations of various factors. Aligned well with the poly-pharmacology paradigm shift in drug discovery, network-based approaches offer great opportunities to discover repurposing candidates for complex psychiatric disorders. In this review, we present the potential of network-based drug repurposing in psychiatry focusing on the incentives for using network-centric repurposing, major network-based repurposing strategies and data resources, applications in psychiatry and challenges of network-based drug repurposing. This review aims to provide readers with an update on network-based drug repurposing in psychiatry. We expect the repurposing approach to become a pivotal tool in the coming years to battle debilitating psychiatric disorders.

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“…Different network embedding approaches capture the network's structural properties using different methods; the focus can be on local or global properties (51)(52)(53). Biological networks are sparse and incomplete (54). Therefore, it is necessary to develop embedding models that take into account the sparsity and incompleteness of biological networks while also accounting for their local and global structural properties.…”

Section: Introductionmentioning

confidence: 99%

BioNE: Integration of network embeddings for supervised learning

Parvizi

Azuaje

Τheodoratou

et al. 2022

Preprint

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SummaryA network embedding approach reduces the analysis complexity of large biological networks by converting them to lowdimensional vector representations (features/embeddings). These lower-dimensional vectors can then be used in machine learning prediction tasks with a wide range of applications in computational biology and bioinformatics. Several network embedding approaches have been proposed with different methods of generating vector representations. These network embedding approaches can be quite diverse in terms of data representation and implementation. Moreover, most were not originally developed for biological networks. Therefore comparing and assessing the performance of these diverse models in practice, in biological contexts, can be challenging. To facilitate such comparisons, we have developed the BioNE framework for integration of different embedding methods in prediction tasks. Using this framework one can easily assess, for instance, whether combined vector representations from multiple embedding methods offer complementary information with regards to the network features and thus better performance on prediction tasks. In this paper, we present the BioNE software suite for embedding integration, which applies network embedding methods following standardised network preparation steps, and integrates the vector representations achieved by these methods using three different techniques. BioNE enables selection of prediction models, oversampling methods, feature selection methods, cross-validation type and cross-validation parameters.Availability and implementationBioNE pipeline and detailed explanation of implementation is freely available on GitHub, at https://github.com/pooryaparvizi/BioNE

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Challenges and opportunities in network-based solutions for biological questions

Cited by 17 publications

References 59 publications

Computational approaches for network-based integrative multi-omics analysis

Computational approaches for network-based integrative multi-omics analysis

Repurposing Drugs via Network Analysis: Opportunities for Psychiatric Disorders

BioNE: Integration of network embeddings for supervised learning

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