Protein protein interaction network analysis of differentially expressed genes to understand involved biological processes in coronary artery disease and its different severity

Kashyap, Shiridhar; Kumar, Sudeep; Agarwal, Vikas; Misra, Durga Prasanna; Phadke, Shubha R.; Kapoor, Aditya

doi:10.1016/j.genrep.2018.05.008

Cited by 10 publications

(7 citation statements)

References 44 publications

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“…Out of top down-regulated DEGs at both 2W and 4W, impact of parasitic infection on Glp1r is not clear, however, Schistosoma mansoni infection evoked profound alterations in glucagon pathway-related genes at 10 days post-infection in mice [40]. Glp1r plays a role in the control of glucose and insulin level as a member of gut hormone receptors [41], and has been considered as a hub gene for both neuroactive ligand-receptor interaction and cAMP signaling pathway [42]. In addition, under the injured hepatic tissues of rats, GTP-binding proteins and Blk are reported to be associated with signal transduction [43] and the inflammatory response [44], respectively.…”

Section: Discussionmentioning

confidence: 99%

Bile Ductal Transcriptome Identifies Key Pathways and Hub Genes in Clonorchis sinensis-Infected Sprague-Dawley Rats

et al. 2020

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Clonorchis sinensis is a food-borne trematode that infects more than 15 million people. The liver fluke causes clonorchiasis and chronical cholangitis, and promotes cholangiocarcinoma. The underlying molecular pathogenesis occurring in the bile duct by the infection is little known. In this study, transcriptome profile in the bile ducts infected with C. sinensis were analyzed using microarray methods. Differentially expressed genes (DEGs) were 1,563 and 1,457 at 2 and 4 weeks after infection. Majority of the DEGs were temporally dysregulated at 2 weeks, but 519 DEGs showed monotonically changing expression patterns that formed seven distinct expression profiles. Protein-protein interaction (PPI) analysis of the DEG products revealed 5 sub-networks and 10 key hub proteins while weighted co-expression network analysis (WGCNA)-derived gene-gene interaction exhibited 16 co-expression modules and 13 key hub genes. The DEGs were significantly enriched in 16 Kyoto Encyclopedia of Genes and Genomes pathways, which were related to original systems, cellular process, environmental information processing, and human diseases. This study uncovered a global picture of gene expression profiles in the bile ducts infected with C. sinensis, and provided a set of potent predictive biomarkers for early diagnosis of clonorchiasis.

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Section: Discussionmentioning

confidence: 99%

Bile Ductal Transcriptome Identifies Key Pathways and Hub Genes in Clonorchis sinensis-Infected Sprague-Dawley Rats

et al. 2020

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“…This unstructured nature enables GNNs to naturally handle a wide range of graph analytics problems (i.e., node classification, link prediction, data visualization, graph clustering community detection, anomaly detection) and have been applied effectively across a diverse range of domains, e.g., protein structure prediction (Jumper et al, 2021), untangling the mathematics of knots (Davies et al, 2021), brain networks (Rosenthal et al, 2018;Xu et al, 2020b,0) in brain imaging, molecular networks (Liu et al, 2019) in drug discovery, protein-protein interaction networks (Kashyap et al, 2018) in genetics, social networks (Wang et al, 2019b) in social media, bank-asset networks (Zhou and Li, 2019) in finance, and publication networks (West et al, 2016) in scientific collaborations.…”

Section: Graph Neural Networkmentioning

confidence: 99%

Scalable algorithms for physics-informed neural and graph networks

Shukla¹,

Xu²,

Trask³

et al. 2022

Preprint

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Physics-informed machine learning (PIML) has emerged as a promising new approach for simulating complex physical and biological systems that are governed by complex multiscale processes for which some data are also available. In some instances, the objective is to discover part of the hidden physics from the available data, and PIML has been shown to be particularly effective for such problems for which conventional methods may fail. Unlike commercial machine learning where training of deep neural networks requires big data, in PIML big data are not available. Instead, we can train such networks from additional information obtained by employing the physical laws and evaluating them at random points in the space-time domain. Such physics-informed machine learning integrates multimodality and multifidelity data with mathematical models, and implements them using neural networks or graph networks. Here, we review some of the prevailing trends in embedding physics into machine learning, using physics-informed neural networks (PINNs) based primarily on feed-forward neural networks and automatic differentiation. For more complex systems or systems of systems and unstructured data, graph neural networks (GNNs) present some distinct advantages, and here we review how physics-informed learning can be accomplished with GNNs based on graph exterior calculus to construct differential operators; we refer to these architectures as physics-informed graph networks (PIGNs). We present representative examples for both forward and inverse problems and discuss what advances are needed to scale up PINNs, PIGNs and more broadly GNNs for large-scale engineering problems. Impact StatementMany complex problems in computational engineering can be described by parametrized differential equations while boundary conditions or material properties may not be fully known, e.g., in thermal-fluid or solid mechanics systems. These ill-defined problems cannot be solved with standard numerical methods but if some sparse data are available, progress can be made by recasting them as large-scale minimization problems. Here, we review physics-informed neural networks (PINNs) and physics-informed graph networks (PIGNs) that integrate seamlessly data and mathematical physics models, even in partially understood or uncertain contexts. Using automatic differentiation in PINNs and external graph calculus in PIGNs, the physical laws are enforced by penalizing the residuals on random points in the space-time domain for PINNs and on the nodes of a graph in PIGNs. New multi-GPU algorithms are needed to scale up PINNs and PIGNs to realistic applications.

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“…The dramatic improvement of neural network and deep-learning technology has provided researchers and practitioners with powerful instruments to investigate the world. Increasingly complex phenomena and processes which can often be modeled as graphs or networks, such as, e.g., social networks [Backstrom and Leskovec, 2011], knowledge graphs [Zou, 2020], protein interaction networks [Kashyap et al, 2018], or the World Wide Web (only to mention a few) can be now addressed via Graph Convolutional Networks (GCNs). Compared to other approaches, GCNs effectively manage to represent the data and the relationships between them by explicitly capturing the topology of the underlying graph within a suitably designed convolution operator.…”

Section: Introductionmentioning

confidence: 99%

SigMaNet: One Laplacian to Rule Them All

Fiorini¹,

Coniglio²,

Ciavotta³

et al. 2022

Preprint

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This paper introduces SigMaNet, a generalized Graph Convolutional Network (GCN) capable of handling both undirected and directed graphs with weights not restricted in sign and magnitude. The cornerstone of SigMaNet is the introduction of a generalized Laplacian matrix: the Sign-Magnetic Laplacian (L σ ). The adoption of such a matrix allows us to bridge a gap in the current literature by extending the theory of spectral GCNs to directed graphs with both positive and negative weights. L σ exhibits several desirable properties not enjoyed by the traditional Laplacian matrices on which several state-of-the-art architectures are based. In particular, L σ is completely parameter-free, which is not the case of Laplacian operators such as the Magnetic Laplacian L (q) , where the calibration of the parameter q is an essential yet problematic component of the operator. L σ simplifies the approach, while also allowing for a natural interpretation of the signs of the edges in terms of their directions. The versatility of the proposed approach is amply demonstrated experimentally; the proposed network SigMaNet turns out to be competitive in all the tasks we considered, regardless of the graph structure.

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Protein protein interaction network analysis of differentially expressed genes to understand involved biological processes in coronary artery disease and its different severity

Cited by 10 publications

References 44 publications

Bile Ductal Transcriptome Identifies Key Pathways and Hub Genes in Clonorchis sinensis-Infected Sprague-Dawley Rats

Bile Ductal Transcriptome Identifies Key Pathways and Hub Genes in Clonorchis sinensis-Infected Sprague-Dawley Rats

Scalable algorithms for physics-informed neural and graph networks

SigMaNet: One Laplacian to Rule Them All

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