A Joint Markov Model for Communities, Connectivity and Signals Defined Over Graphs

Colonnese, Stefania; Lorenzo, Paolo Di; Cattai, Tiziana; Scarano, Gaetano; Fallani, Fabrizio De Vico

doi:10.1109/lsp.2020.3005053

Cited by 7 publications

(10 citation statements)

References 30 publications

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“…The numerical experiments are conducted in Matlab. Specifically, we implemented the model in [21], we equipped it with the community mining library in [25], and particularized it with the above defined clique potential function. Then, we the prediction to the different databases in Table 2, considering 10 Montecarlo runs.…”

Section: Resultsmentioning

confidence: 99%

“…To solve this problem, we resort to the aforementioned Markovian SoG model [21], which relates the graph edges on the protein network with the local connectivity features of the associated pair of nodes. Thus, the Maximum a Posteriori estimate of the unknown coefficients a ij ∈ S a is by min- imization of the potential function U (S x , S γ , S a ).…”

Section: Stage 3: Map Estimation For the Markovian Sogmentioning

confidence: 99%

“…Besides, the protein network is represented as a multidimensional Signal on Graph (SoG) representing the local protein connectivity features. Then, we design the PPI prediction as a network topology inference problem [18], also referred to in the literature as graph learning problem [19], [20], and we solve it using a Markovian Signal on Graph model [21] representing biologically relevant network connectivity features.…”

Section: Introductionmentioning

confidence: 99%

“…It clearly appears that the shape of the clique potential function V X (x 1 − x 2 ) molds U (S x , S γ , S a )| a12=1 and p(S x , S γ , S a )| a12=1 , definitely determining the probabilistic description of the SoG. In[21] the authors solve both a Maximum A Posteriori graph topology inference problem and a joint graph inference and signal reconstruction problem leveraging the minimization of U (S x , S γ , S a ) with respect to S a and to (S x , S a ), respectively. In the following, we apply this model to the protein network by suitable definition of the SoG and design of the clique potential function.…”

mentioning

confidence: 99%

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Protein-Protein Interaction Prediction via Graph Signal Processing

Colonnese¹,

Petti

Farina

et al. 2021

IEEE Access

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This paper tackles the problem of predicting the protein-protein interactions that arise in all living systems. Inference of protein-protein interactions is of paramount importance for understanding fundamental biological phenomena, including cross-species protein-protein interactions, such as those causing the 2020-21 pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Furthermore, it is relevant also for applications such as drug repurposing, where a known authorized drug is applied to novel diseases. On the other hand, a large fraction of existing protein interactions are not known, and their experimental measurement is resource consuming. To this purpose, we adopt a Graph Signal Processing based approach modeling the protein-protein interaction (PPI) network (a.k.a. the interactome) as a graph and some connectivity related node features as a signal on the graph. We then leverage the signal on graph features to infer links between graph nodes, corresponding to interactions between proteins. Specifically, we develop a Markovian model of the signal on graph that enables the representation of connectivity properties of the nodes, and exploit it to derive an algorithm to infer the graph edges. Performance assessment by several metrics recognized in the literature proves that the proposed approach, named GRAph signal processing Based PPI prediction (GRABP), effectively captures underlying biologically grounded properties of the PPI network.

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Section: Resultsmentioning

confidence: 99%

Section: Stage 3: Map Estimation For the Markovian Sogmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Protein-Protein Interaction Prediction via Graph Signal Processing

Colonnese¹,

Petti

Farina

et al. 2021

IEEE Access

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“…GSP has a number of relevant applications, from spatiotemporal analysis of brain data [193]; to analyze vulnerabilities in power grid data [194]; to topological data analysis [195], chemoinformatics [196] and single cell transcriptomic analysis [197], to mention but a few examples. Statistical learning techniques have also being founded on a combination of MRFs and GSP [198,199], taking advantage of both the networked structure, the statistical dependence relationships and the temporal correlations of the signals [200][201][202]. Random field approaches to GSP have also been applied in the context of deep convolutional networks [203,204], often invoking features of the underlying joint conditional probability distributions such as ergodicity [205] and stationarity [206].…”

Section: Random Fields and Graph Signal Theorymentioning

confidence: 99%

Random Fields in Physics, Biology and Data Science

Hernández‐Lemus

2021

Front. Phys.

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A random field is the representation of the joint probability distribution for a set of random variables. Markov fields, in particular, have a long standing tradition as the theoretical foundation of many applications in statistical physics and probability. For strictly positive probability densities, a Markov random field is also a Gibbs field, i.e., a random field supplemented with a measure that implies the existence of a regular conditional distribution. Markov random fields have been used in statistical physics, dating back as far as the Ehrenfests. However, their measure theoretical foundations were developed much later by Dobruschin, Lanford and Ruelle, as well as by Hammersley and Clifford. Aside from its enormous theoretical relevance, due to its generality and simplicity, Markov random fields have been used in a broad range of applications in equilibrium and non-equilibrium statistical physics, in non-linear dynamics and ergodic theory. Also in computational molecular biology, ecology, structural biology, computer vision, control theory, complex networks and data science, to name but a few. Often these applications have been inspired by the original statistical physics approaches. Here, we will briefly present a modern introduction to the theory of random fields, later we will explore and discuss some of the recent applications of random fields in physics, biology and data science. Our aim is to highlight the relevance of this powerful theoretical aspect of statistical physics and its relation to the broad success of its many interdisciplinary applications.

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Assessment of community efforts to advance network-based prediction of protein–protein interactions

et al. 2023

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Comprehensive understanding of the human protein-protein interaction (PPI) network, aka the human interactome, can provide important insights into the molecular mechanisms of complex biological processes and diseases. Despite the remarkable experimental efforts undertaken to date to determine the structure of the human interactome, many PPIs remain unmapped. Computational approaches, especially network-based methods, can facilitate the identification of previously uncharacterized PPIs. Many such methods have been proposed. Yet, a systematic evaluation of existing network-based methods in predicting PPIs is still lacking. Here, we report community efforts initiated by the International Network Medicine Consortium to benchmark the ability of 26 representative network-based methods to predict PPIs across six different interactomes of four different organisms: A. thaliana, C. elegans, S. cerevisiae, and H. sapiens. Through extensive computational and experimental validations, we found that advanced similarity-based methods, which leverage the underlying network characteristics of PPIs, show superior performance over other general link prediction methods in the interactomes we considered.

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A Joint Markov Model for Communities, Connectivity and Signals Defined Over Graphs

Cited by 7 publications

References 30 publications

Protein-Protein Interaction Prediction via Graph Signal Processing

Protein-Protein Interaction Prediction via Graph Signal Processing

Random Fields in Physics, Biology and Data Science

Assessment of community efforts to advance network-based prediction of protein–protein interactions

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