scholarly journals Improved supervised prediction of aging-related genes via weighted dynamic network analysis

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Qi Li ◽  
Khalique Newaz ◽  
Tijana Milenković
Keyword(s):  
Aging Process ◽  
Dynamic Network ◽  
Specific Information ◽  
Related Gene ◽  
Data Types ◽  
Induced Subgraph ◽  
Protein Protein Interaction ◽  
Dynamic Aging ◽  
Equal Importance ◽  
Network Propagation

Abstract Background This study focuses on the task of supervised prediction of aging-related genes from -omics data. Unlike gene expression methods for this task that capture aging-specific information but ignore interactions between genes (i.e., their protein products), or protein–protein interaction (PPI) network methods for this task that account for PPIs but the PPIs are context-unspecific, we recently integrated the two data types into an aging-specific PPI subnetwork, which yielded more accurate aging-related gene predictions. However, a dynamic aging-specific subnetwork did not improve prediction performance compared to a static aging-specific subnetwork, despite the aging process being dynamic. This could be because the dynamic subnetwork was inferred using a naive Induced subgraph approach. Instead, we recently inferred a dynamic aging-specific subnetwork using a methodologically more advanced notion of network propagation (NP), which improved upon Induced dynamic aging-specific subnetwork in a different task, that of unsupervised analyses of the aging process. Results Here, we evaluate whether our existing NP-based dynamic subnetwork will improve upon the dynamic as well as static subnetwork constructed by the Induced approach in the considered task of supervised prediction of aging-related genes. The existing NP-based subnetwork is unweighted, i.e., it gives equal importance to each of the aging-specific PPIs. Because accounting for aging-specific edge weights might be important, we additionally propose a weighted NP-based dynamic aging-specific subnetwork. We demonstrate that a predictive machine learning model trained and tested on the weighted subnetwork yields higher accuracy when predicting aging-related genes than predictive models run on the existing unweighted dynamic or static subnetworks, regardless of whether the existing subnetworks were inferred using NP or the Induced approach. Conclusions Our proposed weighted dynamic aging-specific subnetwork and its corresponding predictive model could guide with higher confidence than the existing data and models the discovery of novel aging-related gene candidates for future wet lab validation.

scholarly journals Constructing a multiple-layer interactome for SARS-CoV-2 in the context of lung disease: Linking the virus with human genes and co-infecting microbes

2021 ◽  
Author(s):  
Shaoke Lou ◽  
Tianxiao Li ◽  
Mark Gerstein
Keyword(s):  
Viral Infection ◽  
Latent Dirichlet Allocation ◽  
Data Types ◽  
Protein Coding ◽  
Individual Level ◽  
Protein Protein Interaction ◽  
Treatment And Prevention ◽  
Multiple Layer ◽  
Network Propagation ◽  
Topic Distribution

AbstractThe severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has caused millions of deaths worldwide. Many efforts have focused on unraveling the mechanism of the viral infection to develop effective strategies for treatment and prevention. Previous studies have provided some clarity on the protein-protein interaction linkages occurring during the life cycle of viral infection; however, we lack a complete understanding of the full interactome, comprising human miRNAs and protein-coding genes and co-infecting microbes. To comprehensively determine this, we developed a statistical modeling method using latent Dirichlet allocation (called MLCrosstalk, for multiple-layer crosstalk) to fuse many types of data to construct the full interactome of SARS-CoV-2. Specifically, MLCrosstalk is able to integrate samples with multiple layers of information (e.g., miRNA and microbes), enforce a consistent topic distribution on all data types, and infer individual-level linkages (i.e., differing between patients). We also implement a secondary refinement with network propagation to allow our microbe-gene linkages to address larger network structures (e.g., pathways). Using MLCrosstalk, we generated a list of genes and microbes linked to SARS-CoV-2. Interestingly, we found that two of the identified microbes, Rothia mucilaginosa and Prevotella melaninogenica, show distinct patterns representing synergistic and antagonistic relationships with the virus, respectively. We also identified several SARS-COV-2-associated pathways, including the VEGFA-VEGFR2 and immune response pathways, which may provide potential targets for drug design.

scholarly journals Omics in a Digital World: The Role of Bioinformatics in Providing New Insights Into Human Aging

2021 ◽  
Vol 12 ◽  
Author(s):  
Serena Dato ◽  
Paolina Crocco ◽  
Nicola Rambaldi Migliore ◽  
Francesco Lescai
Keyword(s):  
High Throughput ◽  
Aging Process ◽  
Single Cell Analysis ◽  
Physiological Age ◽  
Data Types ◽  
Aging Research ◽  
Digital World ◽  
Age Related ◽  
Critical Issues ◽  
The Individual

BackgroundAging is a complex phenotype influenced by a combination of genetic and environmental factors. Although many studies addressed its cellular and physiological age-related changes, the molecular causes of aging remain undetermined. Considering the biological complexity and heterogeneity of the aging process, it is now clear that full understanding of mechanisms underlying aging can only be achieved through the integration of different data types and sources, and with new computational methods capable to achieve such integration.Recent AdvancesIn this review, we show that an omics vision of the age-dependent changes occurring as the individual ages can provide researchers with new opportunities to understand the mechanisms of aging. Combining results from single-cell analysis with systems biology tools would allow building interaction networks and investigate how these networks are perturbed during aging and disease. The development of high-throughput technologies such as next-generation sequencing, proteomics, metabolomics, able to investigate different biological markers and to monitor them simultaneously during the aging process with high accuracy and specificity, represents a unique opportunity offered to biogerontologists today.Critical IssuesAlthough the capacity to produce big data drastically increased over the years, integration, interpretation and sharing of high-throughput data remain major challenges. In this paper we present a survey of the emerging omics approaches in aging research and provide a large collection of datasets and databases as a useful resource for the scientific community to identify causes of aging. We discuss their peculiarities, emphasizing the need for the development of methods focused on the integration of different data types.Future DirectionsWe critically review the contribution of bioinformatics into the omics of aging research, and we propose a few recommendations to boost collaborations and produce new insights. We believe that significant advancements can be achieved by following major developments in bioinformatics, investing in diversity, data sharing and community-driven portable bioinformatics methods. We also argue in favor of more engagement and participation, and we highlight the benefits of new collaborations along these lines. This review aims at being a useful resource for many researchers in the field, and a call for new partnerships in aging research.

scholarly journals Venn-diaNet : venn diagram based network propagation analysis framework for comparing multiple biological experiments

2019 ◽  
Vol 20 (S23) ◽  
Author(s):  
Benjamin Hur ◽  
Dongwon Kang ◽  
Sangseon Lee ◽  
Ji Hwan Moon ◽  
Gung Lee ◽  
...  
Keyword(s):  
Interaction Network ◽  
Venn Diagram ◽  
Analysis Framework ◽  
Seed Selection ◽  
Systematic Analysis ◽  
Main Research ◽  
Protein Protein Interaction ◽  
Propagation Analysis ◽  
Network Propagation ◽  
Main Research Topic

Abstract Background The main research topic in this paper is how to compare multiple biological experiments using transcriptome data, where each experiment is measured and designed to compare control and treated samples. Comparison of multiple biological experiments is usually performed in terms of the number of DEGs in an arbitrary combination of biological experiments. This process is usually facilitated with Venn diagram but there are several issues when Venn diagram is used to compare and analyze multiple experiments in terms of DEGs. First, current Venn diagram tools do not provide systematic analysis to prioritize genes. Because that current tools generally do not fully focus to prioritize genes, genes that are located in the segments in the Venn diagram (especially, intersection) is usually difficult to rank. Second, elucidating the phenotypic difference only with the lists of DEGs and expression values is challenging when the experimental designs have the combination of treatments. Experiment designs that aim to find the synergistic effect of the combination of treatments are very difficult to find without an informative system. Results We introduce Venn-diaNet, a Venn diagram based analysis framework that uses network propagation upon protein-protein interaction network to prioritizes genes from experiments that have multiple DEG lists. We suggest that the two issues can be effectively handled by ranking or prioritizing genes with segments of a Venn diagram. The user can easily compare multiple DEG lists with gene rankings, which is easy to understand and also can be coupled with additional analysis for their purposes. Our system provides a web-based interface to select seed genes in any of areas in a Venn diagram and then perform network propagation analysis to measure the influence of the selected seed genes in terms of ranked list of DEGs. Conclusions We suggest that our system can logically guide to select seed genes without additional prior knowledge that makes us free from the seed selection of network propagation issues. We showed that Venn-diaNet can reproduce the research findings reported in the original papers that have experiments that compare two, three and eight experiments. Venn-diaNet is freely available at: http://biohealth.snu.ac.kr/software/venndianet

Photoluminescence characterisations of a dynamic aging process of organic–inorganic CH3NH3PbBr3perovskite

Nanoscale ◽  
2016 ◽  
Vol 8 (4) ◽  
pp. 1926-1931 ◽  
Author(s):  
R. Sheng ◽  
X. Wen ◽  
S. Huang ◽  
X. Hao ◽  
S. Chen ◽  
...  
Keyword(s):  
Fluorescence Lifetime ◽  
Aging Process ◽  
Fluorescence Lifetime Imaging Microscopy ◽  
Fluorescence Lifetime Imaging ◽  
Lifetime Imaging ◽  
Dynamic Aging

PL decay traces (left) and fluorescence lifetime imaging microscopy (FLIM) image (right) of 2 weeks air stored perovskite film.

scholarly journals Omics Data Complementarity Underlines Functional Cross-Communication in Yeast

2017 ◽  
Vol 14 (2) ◽  
Author(s):  
Noël Malod-Dognin ◽  
Nataša Pržulj
Keyword(s):  
Functional Organization ◽  
Genetic Interaction ◽  
Molecular Data ◽  
Omics Data ◽  
Functional Communication ◽  
Data Types ◽  
Protein Protein Interaction ◽  
Exact Answer ◽  
A Cell ◽  
Computational Intractability

AbstractMapping the complete functional layout of a cell and understanding the cross-talk between different processes are fundamental challenges. They elude us because of the incompleteness and noisiness of molecular data and because of the computational intractability of finding the exact answer. We perform a simple integration of three types of baker’s yeast omics data to elucidate the functional organization and lines of cross-functional communication. We examine protein–protein interaction (PPI), co-expression (COEX) and genetic interaction (GI) data, and explore their relationship with the gold standard of functional organization, the Gene Ontology (GO). We utilize a simple framework that identifies functional cross-communication lines in each of the three data types, in GO, and collectively in the integrated model of the three omics data types; we present each of them in our new Functional Organization Map (FOM) model. We compare the FOMs of the three omics datasets with the FOM of GO and find that GI is in best agreement with GO, followed COEX and PPI. We integrate the three FOMs into a unified FOM and find that it is in better agreement with the FOM of GO than those of any omics dataset alone, demonstrating functional complementarity of different omics data.

Effect of deformation induced precipitation on dynamic aging process and improvement of mechanical/corrosion properties AA7055 aluminum alloy

2017 ◽  
Vol 708 ◽  
pp. 1131-1140 ◽  
Author(s):  
Jinrong Zuo ◽  
Longgang Hou ◽  
Jintao Shi ◽  
Hua Cui ◽  
Linzhong Zhuang ◽  
...  
Keyword(s):  
Aluminum Alloy ◽  
Aging Process ◽  
Corrosion Properties ◽  
Dynamic Aging

scholarly journals Interpretable network propagation with application to expanding the repertoire of human proteins that interact with SARS-CoV-2

GigaScience ◽  
2021 ◽  
Vol 10 (12) ◽  
Author(s):  
Jeffrey N Law ◽  
Kyle Akers ◽  
Nure Tasnina ◽  
Catherine M Della Santina ◽  
Shay Deutsch ◽  
...  
Keyword(s):  
Broad Class ◽  
Interaction Network ◽  
Emerging Viruses ◽  
Protein Protein Interaction ◽  
Functional Relationships ◽  
Manual Adjustment ◽  
Network Propagation ◽  
Human Proteins ◽  
Adjustment Of Parameters ◽  
Protein Protein Interaction Network

Abstract Background Network propagation has been widely used for nearly 20 years to predict gene functions and phenotypes. Despite the popularity of this approach, little attention has been paid to the question of provenance tracing in this context, e.g., determining how much any experimental observation in the input contributes to the score of every prediction. Results We design a network propagation framework with 2 novel components and apply it to predict human proteins that directly or indirectly interact with SARS-CoV-2 proteins. First, we trace the provenance of each prediction to its experimentally validated sources, which in our case are human proteins experimentally determined to interact with viral proteins. Second, we design a technique that helps to reduce the manual adjustment of parameters by users. We find that for every top-ranking prediction, the highest contribution to its score arises from a direct neighbor in a human protein-protein interaction network. We further analyze these results to develop functional insights on SARS-CoV-2 that expand on known biology such as the connection between endoplasmic reticulum stress, HSPA5, and anti-clotting agents. Conclusions We examine how our provenance-tracing method can be generalized to a broad class of network-based algorithms. We provide a useful resource for the SARS-CoV-2 community that implicates many previously undocumented proteins with putative functional relationships to viral infection. This resource includes potential drugs that can be opportunistically repositioned to target these proteins. We also discuss how our overall framework can be extended to other, newly emerging viruses.

scholarly journals Identification of a novel autophagy-related gene signature for predicting metastasis and survival in patients with osteosarcoma

2020 ◽  
Author(s):  
Guangzhi Zhang ◽  
Yajun Deng ◽  
Zuolong Wu ◽  
Enhui Ren ◽  
Wenhua Yuan ◽  
...  
Keyword(s):  
Muscle Tissue ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Tissue Expression ◽  
Gene Signature ◽  
Related Gene ◽  
Normal Muscle ◽  
Tissue Samples ◽  
Protein Protein Interaction ◽  
Gene Expression Profile Data

Abstract Background: Osteosarcoma (OS) is a bone malignant tumor that occurs in children and adolescents. Due to a lack of reliable prognostic biomarkers, the prognosis of OS patients is often uncertain. This study aimed to construct an autophagy-related gene signature to predict the prognosis of OS patients.Methods: The gene expression profile data of OS and normal muscle tissue samples were downloaded separately from the Therapeutically Applied Research To Generate Effective Treatments (TARGET) and Genotype-Tissue Expression (GTEx) databases . The differentially expressed autophagy-related genes (DEARGs) in OS and normal muscle tissue samples were screened using R software, before being subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. A protein-protein interaction (PPI) network was constructed and hub autophagy-related genes were screened. Finally, the screened autophagy-related genes were subjected to univariate Cox regression, Lasso Cox regression, survival analysis, and clinical correlation analysis.Results: A total of 120 DEARGs and 10 hub autophagy-related genes were obtained. A prognostic autophagy-related gene signature consisting of 9 genes ( BNIP3 , MYC , BAG1 , CALCOCO2 , ATF4 , AMBRA1 , EGFR , MAPK1 , and PEX ) was constructed. This signature was significantly correlated to the prognosis ( P <0.0001) and distant metastasis of OS patients ( P = 0.013).Conclusion: This signature based on 9 autophagy-related genes could predict metastasis and survival in patients with OS.

scholarly journals Dynamic Aging: Channeled Through Microenvironment

2021 ◽  
Vol 12 ◽  
Author(s):  
Qing Tan ◽  
Na Liang ◽  
Xiaoqian Zhang ◽  
Jun Li
Keyword(s):  
Aging Process ◽  
Therapeutic Strategies ◽  
Dynamic Aging ◽  
New Findings ◽  
Complicated Process ◽  
Multiple Levels ◽  
Cellular Dysfunction

Aging process is a complicated process that involves deteriorated performance at multiple levels from cellular dysfunction to organ degeneration. For many years research has been focused on how aging changes things within cell. However, new findings suggest that microenvironments, circulating factors or inter-tissue communications could also play important roles in the dynamic progression of aging. These out-of-cell mechanisms pass on the signals from the damaged aging cells to other healthy cells or tissues to promote systematic aging phenotypes. This review discusses the mechanisms of how senescence and their secretome, NAD+ metabolism or circulating factors change microenvironments to regulate systematic aging, as well as the potential therapeutic strategies based on these findings for anti-aging interventions.

Linking Performance Data and Geospatial Information of Manufacturing Assets Through Standard Representations

2020 ◽  
Author(s):  
Aaron Hanke ◽  
Teodor Vernica ◽  
William Z. Bernstein
Keyword(s):  
Situational Awareness ◽  
Time Series Data ◽  
Critical Issue ◽  
Industrial Applications ◽  
Series Data ◽  
Specific Information ◽  
Data Types ◽  
Geospatial Information ◽  
Prototype Development ◽  
Production Facilities

Abstract Interoperability across emerging visualization modalities, including augmented reality (AR) and virtual reality (VR), remains a challenge with respect to industrial applications. One critical issue relates to the lack of standard approaches for coordinating geospatial representations that are required to facilitate AR/VR scenes with domain-specific information in the form of time-series data, solid models, among other data types. In this paper, we focus on the linking of manufacturing asset data via the MTConnect standard with geospatial data via the IndoorGML standard. To this end, we demonstrate the utility of this integration through two visualization-based prototype implementations, including one focused on (i) monitoring production facilities to improve situational awareness and (ii) evaluating and delivering suggested navigation paths in production facilities. We then comment on implications of such standards-driven approaches for related domains, including AR prototype development and automatic guided vehicles.

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