Inference of a Dynamic Aging-related Biological Subnetwork via Network Propagation

Newaz, Khalique; Milenković, Tijana

doi:10.1109/tcbb.2020.3022767

Cited by 7 publications

(7 citation statements)

References 56 publications

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“…Another potential although imperfect solution might be to rely on predicted novel aging-related genes from existing computational studies, e.g. those predicted via network clustering ( Hulovatyy and Milenković, 2016 ), network alignment ( Faisal et al , 2015 ), unsupervised analyses of aging-specific subnetworks ( Newaz and Milenković, 2020 ) or supervised analyses of aging-specific subnetworks. Note that these computationally predicted novel aging-related genes should not be used to validate prediction methods that share similar properties as the methods that generated the predictions, in order to avoid a circular argument.…”

Section: Discussionmentioning

confidence: 99%

“…Berchtold-HPRD is the benchmark subnetwork from our previous study ( Li et al , 2021 ). Berchtold-HPRD was inferred using a method called NetWalk ( Komurov et al , 2010 ), which was proved to be the best of two state-of-the-art network propagation algorithms for inferring aging-specific subnetworks in our previous study ( Newaz and Milenković, 2020 ). For fairness, we use NetWalk to infer all other considered networks.…”

Section: Methodsmentioning

confidence: 99%

“…Because nodes in our PPIN data have gene IDs, to integrate GE with PPIN data, we need to map probe IDs to gene IDs. To do this, we mimic our previous study ( Newaz and Milenković, 2020 ). That is, by using the DAVID tool ( Huang et al , 2009 ), we are able to map 48 724 of the 54 675 probes to 21 441 unique gene IDs.…”

Section: Methodsmentioning

confidence: 99%

“…Our group has pioneered this research direction by inferring an aging-specific subnetwork that captures both aging-specific information from GE data and interactions from PPIN data through a series of studies/methods ( Faisal and Milenković, 2014 ; Li and Milenković, 2019 , 2021 ; Li et al , 2021 ; Newaz and Milenković, 2020 ). Our most recent finding is that inferring an aging-specific subnetwork that is both weighted and dynamic is superior (in terms of quality of aging-related gene predictions made from it) than inferring an aging-specific subnetwork that is unweighted or static as well as using entire context-unspecific PPIN data ( Li et al , 2021 ).…”

Section: Introductionmentioning

confidence: 99%

“…Similarly, HPRD was a state-of-the-art human PPIN data at that time. Since then, we proposed a series of more advanced methods for inferring or analyzing aging-specific subnetworks ( Li and Milenković, 2019 ; 2021 ; Li et al , 2021 ; Newaz and Milenković, 2020 ). For fairness of evaluation of every new method against previous ones, we kept the input (i.e.…”

Section: Introductionmentioning

confidence: 99%

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Towards future directions in data-integrative supervised prediction of human aging-related genes

Newaz

Milenković

2022

Bioinformatics Advances

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Motivation Identification of human genes involved in the aging process is critical due to the incidence of many diseases with age. A state-of-the-art approach for this purpose infers a weighted dynamic aging-specific subnetwork by mapping gene expression (GE) levels at different ages onto the protein-protein interaction network (PPIN). Then, it analyzes this subnetwork in a supervised manner by training a predictive model to learn how network topologies of known aging- vs. non-aging-related genes change across ages. Finally, it uses the trained model to predict novel aging-related gene candidates. However, the best current subnetwork resulting from this approach still yields suboptimal prediction accuracy. This could be because it was inferred using outdated GE and PPIN data. Here, we evaluate whether analyzing a weighted dynamic aging-specific subnetwork inferred from newer GE and PPIN data improves prediction accuracy upon analyzing the best current subnetwork inferred from outdated data. Results Unexpectedly, we find that not to be the case. To understand this, we perform aging-related pathway and Gene Ontology (GO) term enrichment analyses. We find that the suboptimal prediction accuracy, regardless of which GE or PPIN data is used, may be caused by the current knowledge about which genes are aging-related being incomplete, or by the current methods for inferring or analyzing an aging-specific subnetwork being unable to capture all of the aging-related knowledge. These findings can potentially guide future directions towards improving supervised prediction of aging-related genes via -omics data integration. Supplementary data Attached. Availability and implementation All data and code are available at zenodo, DOI: 10.5281/zenodo.6995045.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Towards future directions in data-integrative supervised prediction of human aging-related genes

Newaz

Milenković

2022

Bioinformatics Advances

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The Molecular Landscape of Premature Aging Diseases Defined by Multilayer Network Exploration

Beust,

Valdeolivas,

Baptista

et al. 2024

Advanced Biology

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Premature Aging (PA) diseases are rare genetic disorders that mimic some aspects of physiological aging at an early age. Various causative genes of PA diseases have been identified in recent years, providing insights into some dysfunctional cellular processes. However, the identification of PA genes also revealed significant genetic heterogeneity and highlighted the gaps in this understanding of PA‐associated molecular mechanisms. Furthermore, many patients remain undiagnosed. Overall, the current lack of knowledge about PA diseases hinders the development of effective diagnosis and therapies and poses significant challenges to improving patient care.Here, a network‐based approach to systematically unravel the cellular functions disrupted in PA diseases is presented. Leveraging a network community identification algorithm, it is delved into a vast multilayer network of biological interactions to extract the communities of 67 PA diseases from their 132 associated genes. It is found that these communities can be grouped into six distinct clusters, each reflecting specific cellular functions: DNA repair, cell cycle, transcription regulation, inflammation, cell communication, and vesicle‐mediated transport. That these clusters collectively represent the landscape of the molecular mechanisms that are perturbed in PA diseases, providing a framework for better understanding their pathogenesis is proposed. Intriguingly, most clusters also exhibited a significant enrichment in genes associated with physiological aging, suggesting a potential overlap between the molecular underpinnings of PA diseases and natural aging.

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Current and future directions in network biology

Zitnik,

Li,

Wells

et al. 2024

Bioinformatics Advances

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Summary Network biology is an interdisciplinary field bridging computational and biological sciences that has proved pivotal in advancing the understanding of cellular functions and diseases across biological systems and scales. Although the field has been around for two decades, it remains nascent. It has witnessed rapid evolution, accompanied by emerging challenges. These stem from various factors, notably the growing complexity and volume of data together with the increased diversity of data types describing different tiers of biological organization. We discuss prevailing research directions in network biology, focusing on molecular/cellular networks but also on other biological network types such as biomedical knowledge graphs, patient similarity networks, brain networks, and social/contact networks relevant to disease spread. In more detail, we highlight areas of inference and comparison of biological networks, multimodal data integration and heterogeneous networks, higher-order network analysis, machine learning on networks, and network-based personalized medicine. Following the overview of recent breakthroughs across these five areas, we offer a perspective on future directions of network biology. Additionally, we discuss scientific communities, educational initiatives, and the importance of fostering diversity within the field. This article establishes a roadmap for an immediate and long-term vision for network biology. Availability and implementation Not applicable.

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Inference of a Dynamic Aging-related Biological Subnetwork via Network Propagation

Cited by 7 publications

References 56 publications

Towards future directions in data-integrative supervised prediction of human aging-related genes

Towards future directions in data-integrative supervised prediction of human aging-related genes

The Molecular Landscape of Premature Aging Diseases Defined by Multilayer Network Exploration

Current and future directions in network biology

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