Diffusion kernel to identify missing PPIs in protein network biomarker

Bett, Dominic K.; Mondal, Ananda Mohan

doi:10.1109/bibm.2015.7359917

Cited by 6 publications

(4 citation statements)

References 9 publications

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“…The advancement in research and technology has been slowly shifting the focus of lung cancer diagnosis, prognosis and treatment towards understanding the underlying cause of disease progression using protein-protein interaction (PPI) networks, gene co-expression networks and molecular pathways. Though the PPI and co-expression networks are static in nature, these come with rich information about the dynamic processes such as behavior of genetic networks in response to DNA damage [6], prediction of protein subcellular localization [7][8][9][10][11][12], protein function [13], genetic interaction [14], process of aging [15], and protein network biomarkers [16][17][18][19][20][21][22]. The networks are of special interest because the genes do not act alone.…”

Section: Introductionmentioning

confidence: 99%

Computational identification of biomarker genes for lung cancer considering treatment and non-treatment studies

et al. 2020

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Background Lung cancer is the number one cancer killer in the world with more than 142,670 deaths estimated in the United States alone in the year 2019. Consequently, there is an overreaching need to identify the key biomarkers for lung cancer. The aim of this study is to computationally identify biomarker genes for lung cancer that can aid in its diagnosis and treatment. The gene expression profiles of two different types of studies, namely non-treatment and treatment, are considered for discovering biomarker genes. In non-treatment studies healthy samples are control and cancer samples are cases. Whereas, in treatment studies, controls are cancer cell lines without treatment and cases are cancer cell lines with treatment. Results The Differentially Expressed Genes (DEGs) for lung cancer were isolated from Gene Expression Omnibus (GEO) database using R software tool GEO2R. A total of 407 DEGs (254 upregulated and 153 downregulated) from non-treatment studies and 547 DEGs (133 upregulated and 414 downregulated) from treatment studies were isolated. Two Cytoscape apps, namely, CytoHubba and MCODE, were used for identifying biomarker genes from functional networks developed using DEG genes. This study discovered two distinct sets of biomarker genes – one from non-treatment studies and the other from treatment studies, each set containing 16 genes. Survival analysis results show that most non-treatment biomarker genes have prognostic capability by indicating low-expression groups have higher chance of survival compare to high-expression groups. Whereas, most treatment biomarkers have prognostic capability by indicating high-expression groups have higher chance of survival compare to low-expression groups. Conclusion A computational framework is developed to identify biomarker genes for lung cancer using gene expression profiles. Two different types of studies – non-treatment and treatment – are considered for experiment. Most of the biomarker genes from non-treatment studies are part of mitosis and play vital role in DNA repair and cell-cycle regulation. Whereas, most of the biomarker genes from treatment studies are associated to ubiquitination and cellular response to stress. This study discovered a list of biomarkers, which would help experimental scientists to design a lab experiment for further exploration of detail dynamics of lung cancer development.

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Section: Introductionmentioning

confidence: 99%

Computational identification of biomarker genes for lung cancer considering treatment and non-treatment studies

et al. 2020

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“…With a competitive accuracy and balanced performance for simple and complex structures (based on Table 3, considering the number of domains as an indicator of protein complexity) despite relying on a relatively naive criterion to choose optimal decomposition, KluDo revealed that diffusion kernels on graphs in particular, and kernel functions in general are promising measures to facilitate parsing proteins into domains and also performing different structural analysis on proteins. Graph node kernels are trending tools widely adopted in real-world applications, particularly in biological data in recent years; examples are gene association studies [67,94] and PPI network analysis [95]. The size and interconnectedness of protein graphs make them promising targets for diffusion kernels as efficient measures of affinity between amino acids.…”

Section: Discussionmentioning

confidence: 99%

Assignment of structural domains in proteins using diffusion kernels on graphs

et al. 2022

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Though proposing algorithmic approaches for protein domain decomposition has been of high interest, the inherent ambiguity to the problem makes it still an active area of research. Besides, accurate automated methods are in high demand as the number of solved structures for complex proteins is on the rise. While majority of the previous efforts for decomposition of 3D structures are centered on the developing clustering algorithms, employing enhanced measures of proximity between the amino acids has remained rather uncharted. If there exists a kernel function that in its reproducing kernel Hilbert space, structural domains of proteins become well separated, then protein structures can be parsed into domains without the need to use a complex clustering algorithm. Inspired by this idea, we developed a protein domain decomposition method based on diffusion kernels on protein graphs. We examined all combinations of four graph node kernels and two clustering algorithms to investigate their capability to decompose protein structures. The proposed method is tested on five of the most commonly used benchmark datasets for protein domain assignment plus a comprehensive non-redundant dataset. The results show a competitive performance of the method utilizing one of the diffusion kernels compared to four of the best automatic methods. Our method is also able to offer alternative partitionings for the same structure which is in line with the subjective definition of protein domain. With a competitive accuracy and balanced performance for the simple and complex structures despite relying on a relatively naive criterion to choose optimal decomposition, the proposed method revealed that diffusion kernels on graphs in particular, and kernel functions in general are promising measures to facilitate parsing proteins into domains and performing different structural analysis on proteins. The size and interconnectedness of the protein graphs make them promising targets for diffusion kernels as measures of affinity between amino acids. The versatility of our method allows the implementation of future kernels with higher performance. The source code of the proposed method is accessible at https://github.com/taherimo/kludo. Also, the proposed method is available as a web application from https://cbph.ir/tools/kludo.

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“…Their exponentials are also referred to as heat kernels by analogy to the continuous heat equations that involve the continuous Laplace operator [9][10][11]. Heat kernels are also known as diffusion kernels, and have the same eigenvectors as Laplacians for discrete state spaces, or eigenfunctions for continuous state spaces [12,13].…”

Section: Laplacian Eigenspace Methodsmentioning

confidence: 99%

“…The previous formulation defined in [37] did not mention the applications to density function unmixing/separation via (13) and the connection to discrete approximations of Smoluchowski equations as described in section 0.6.…”

Section: Previous Formulationsmentioning

confidence: 99%

Laplacian mixture modeling for network analysis and unsupervised learning on graphs

Korenblum¹

2018

PLoS ONE

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Laplacian mixture models identify overlapping regions of influence in unlabeled graph and network data in a scalable and computationally efficient way, yielding useful low-dimensional representations. By combining Laplacian eigenspace and finite mixture modeling methods, they provide probabilistic or fuzzy dimensionality reductions or domain decompositions for a variety of input data types, including mixture distributions, feature vectors, and graphs or networks. Provable optimal recovery using the algorithm is analytically shown for a nontrivial class of cluster graphs. Heuristic approximations for scalable high-performance implementations are described and empirically tested. Connections to PageRank and community detection in network analysis demonstrate the wide applicability of this approach. The origins of fuzzy spectral methods, beginning with generalized heat or diffusion equations in physics, are reviewed and summarized. Comparisons to other dimensionality reduction and clustering methods for challenging unsupervised machine learning problems are also discussed.

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Diffusion kernel to identify missing PPIs in protein network biomarker

Cited by 6 publications

References 9 publications

Computational identification of biomarker genes for lung cancer considering treatment and non-treatment studies

Computational identification of biomarker genes for lung cancer considering treatment and non-treatment studies

Assignment of structural domains in proteins using diffusion kernels on graphs

Laplacian mixture modeling for network analysis and unsupervised learning on graphs

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