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Article ; Online: CoVar: A generalizable machine learning approach to identify the coordinated regulators driving variational gene expression.

Roy, Satyaki / Sheikh, Shehzad Z / Furey, Terrence S

2024 Volume 20, Issue 4, Page(s) e1012016

Abstract: Network inference is used to model transcriptional, signaling, and metabolic interactions among genes, proteins, and metabolites that identify biological pathways influencing disease pathogenesis. Advances in machine learning (ML)-based inference models ... ...

Abstract	Network inference is used to model transcriptional, signaling, and metabolic interactions among genes, proteins, and metabolites that identify biological pathways influencing disease pathogenesis. Advances in machine learning (ML)-based inference models exhibit the predictive capabilities of capturing latent patterns in genomic data. Such models are emerging as an alternative to the statistical models identifying causative factors driving complex diseases. We present CoVar, an ML-based framework that builds upon the properties of existing inference models, to find the central genes driving perturbed gene expression across biological states. Unlike differentially expressed genes (DEGs) that capture changes in individual gene expression across conditions, CoVar focuses on identifying variational genes that undergo changes in their expression network interaction profiles, providing insights into changes in the regulatory dynamics, such as in disease pathogenesis. Subsequently, it finds core genes from among the nearest neighbors of these variational genes, which are central to the variational activity and influence the coordinated regulatory processes underlying the observed changes in gene expression. Through the analysis of simulated as well as yeast expression data perturbed by the deletion of the mitochondrial genome, we show that CoVar captures the intrinsic variationality and modularity in the expression data, identifying key driver genes not found through existing differential analysis methodologies.
Language	English
Publishing date	2024-04-17
Publishing country	United States
Document type	Journal Article
ZDB-ID	2193340-6
ISSN	1553-7358 ; 1553-734X
ISSN (online)	1553-7358
ISSN	1553-734X
DOI	10.1371/journal.pcbi.1012016
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Determining the rate of infectious disease testing through contagion potential.

Roy, Satyaki / Biswas, Preetom / Ghosh, Preetam

PLOS global public health

2023 Volume 3, Issue 8, Page(s) e0002229

Abstract: The emergence of new strains, varying in transmissibility, virulence, and presentation, makes the existing epidemiological statistics an inadequate representation of COVID-19 contagion. Asymptomatic individuals continue to act as carriers for the elderly ...

Abstract	The emergence of new strains, varying in transmissibility, virulence, and presentation, makes the existing epidemiological statistics an inadequate representation of COVID-19 contagion. Asymptomatic individuals continue to act as carriers for the elderly and immunocompromised, making the timing and extent of vaccination and testing extremely critical in curbing contagion. In our earlier work, we proposed contagion potential (CP) as a measure of the infectivity of an individual in terms of their contact with other infectious individuals. Here we extend the idea of CP at the level of a geographical region (termed a zone). We estimate CP in a spatiotemporal model based on infection spread through social mixing as well as SIR epidemic model optimization, under varying conditions of virus strains, reinfection, and superspreader events. We perform experiments on the real daily infection dataset at the country level (Italy and Germany) and state level (New York City, USA). Our analysis shows that CP can effectively assess the number of untested (and asymptomatic) infected and inform the necessary testing rates. Finally, we show through simulations that CP can trace the evolution of the infectivity profiles of zones due to the combination of inter-zonal mobility, vaccination policy, and testing rates in real-world scenarios.
Language	English
Publishing date	2023-08-02
Publishing country	United States
Document type	Journal Article
ISSN	2767-3375
ISSN (online)	2767-3375
DOI	10.1371/journal.pgph.0002229
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Hierarchical Vaccine Allocation Based on Epidemiological and Behavioral Considerations.

Roy, Satyaki / Dutta, Pratyay / Ghosh, Preetam

IEEE/ACM transactions on computational biology and bioinformatics

2023 Volume 20, Issue 5, Page(s) 2981–2991

Abstract: Vaccines have proven useful in curbing contagion from new strains of the SARS-CoV-2 virus. However, equitable vaccine allocation continues to be a significant challenge worldwide, necessitating a comprehensive allocation strategy incorporating ... ...

Abstract	Vaccines have proven useful in curbing contagion from new strains of the SARS-CoV-2 virus. However, equitable vaccine allocation continues to be a significant challenge worldwide, necessitating a comprehensive allocation strategy incorporating heterogeneity in epidemiological and behavioral considerations. In this paper, we present a hierarchical allocation strategy that assigns vaccines to zones and their constituent neighborhoods cost-effectively, based on their population density, susceptibility, infected count, and attitude towards vaccinations. Moreover, it includes a module that tackles vaccine shortages in certain zones by locally transferring vaccines from zones with surplus vaccines. We leverage the epidemiological, socio-demographic, and social media datasets from Chicago and Greece and their constituent community areas to show that the proposed allocation approach assigns vaccines based on the chosen criteria and captures the effects of disparate vaccine adoption rates. We conclude the paper with a lowdown on future efforts to extend this study to design models for effective public policies and vaccination strategies that curtail vaccine purchase costs.
MeSH term(s)	Humans ; Vaccines ; SARS-CoV-2 ; Social Media
Chemical Substances	Vaccines
Language	English
Publishing date	2023-10-09
Publishing country	United States
Document type	Journal Article ; Research Support, U.S. Gov't, Non-P.H.S.
ISSN	1557-9964
ISSN (online)	1557-9964
DOI	10.1109/TCBB.2023.3265317
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Article ; Online: Examining indicators of complex network vulnerability across diverse attack scenarios

Ahmad F. Al Musawi / Satyaki Roy / Preetam Ghosh

Scientific Reports, Vol 13, Iss 1, Pp 1-

2023 Volume 14

Abstract: Abstract Complex networks capture the structure, dynamics, and relationships among entities in real-world networked systems, encompassing domains like communications, society, chemistry, biology, ecology, politics, etc. Analysis of complex networks lends ...

Abstract	Abstract Complex networks capture the structure, dynamics, and relationships among entities in real-world networked systems, encompassing domains like communications, society, chemistry, biology, ecology, politics, etc. Analysis of complex networks lends insight into the critical nodes, key pathways, and potential points of failure that may impact the connectivity and operational integrity of the underlying system. In this work, we investigate the topological properties or indicators, such as shortest path length, modularity, efficiency, graph density, diameter, assortativity, and clustering coefficient, that determine the vulnerability to (or robustness against) diverse attack scenarios. Specifically, we examine how node- and link-based network growth or depletion based on specific attack criteria affect their robustness gauged in terms of the largest connected component (LCC) size and diameter. We employ partial least squares discriminant analysis to quantify the individual contribution of the indicators on LCC preservation while accounting for the collinearity stemming from the possible correlation between indicators. Our analysis of 14 complex network datasets and 5 attack models invariably reveals high modularity and disassortativity to be prime indicators of vulnerability, corroborating prior works that report disassortative modular networks to be particularly susceptible to targeted attacks. We conclude with a discussion as well as an illustrative example of the application of this work in fending off strategic attacks on critical infrastructures through models that adaptively and distributively achieve network robustness.
Keywords	Medicine ; R ; Science ; Q
Subject code	006
Language	English
Publishing date	2023-10-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Inferring links in directed complex networks through feed forward loop motifs

Satyaki Roy / Ahmad F. Al Musawi / Preetam Ghosh

Humanities & Social Sciences Communications, Vol 10, Iss 1, Pp 1-

2023 Volume 13

Abstract: Abstract Complex networks are mathematical abstractions of real-world systems using sets of nodes and edges representing the entities and their interactions. Prediction of unknown interactions in such networks is a problem of interest in biology, ... ...

Abstract	Abstract Complex networks are mathematical abstractions of real-world systems using sets of nodes and edges representing the entities and their interactions. Prediction of unknown interactions in such networks is a problem of interest in biology, sociology, physics, engineering, etc. Most complex networks exhibit the recurrence of subnetworks, called network motifs. Within the realm of social science, link prediction (LP) models are employed to model opinions, trust, privacy, rumor spreading in social media, academic and corporate collaborations, liaisons among lawbreakers, and human mobility resulting in contagion. We present an LP metric based on a motif in directed complex networks, called feed-forward loop (FFL). Unlike nearest neighbor-based metrics and machine learning-based techniques that gauge the likelihood of a link based on node similarity, the proposed approach leverages a known dichotomy in the motif distribution of directed networks. Complex networks are sparse, causing most nodes and their associated links to have low motif participation. Yet, due to intrinsic network motif-richness, few links participate in many distinct motif substructures. Thus, the FFL-based metric combines the presence and absence of motifs as a signature to outperform baseline metrics on ten directed social and biological network datasets. We conclude with the future of the FFL-based metric in dynamic network inference as well as its use in designing combined metrics using network motifs of varying orders as features.
Keywords	History of scholarship and learning. The humanities ; AZ20-999 ; Social Sciences ; H
Subject code	006
Language	English
Publishing date	2023-06-01T00:00:00Z
Publisher	Springer Nature
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Examining indicators of complex network vulnerability across diverse attack scenarios.

Al Musawi, Ahmad F / Roy, Satyaki / Ghosh, Preetam

Scientific reports

2023 Volume 13, Issue 1, Page(s) 18208

Abstract: Complex networks capture the structure, dynamics, and relationships among entities in real-world networked systems, encompassing domains like communications, society, chemistry, biology, ecology, politics, etc. Analysis of complex networks lends insight ... ...

Abstract	Complex networks capture the structure, dynamics, and relationships among entities in real-world networked systems, encompassing domains like communications, society, chemistry, biology, ecology, politics, etc. Analysis of complex networks lends insight into the critical nodes, key pathways, and potential points of failure that may impact the connectivity and operational integrity of the underlying system. In this work, we investigate the topological properties or indicators, such as shortest path length, modularity, efficiency, graph density, diameter, assortativity, and clustering coefficient, that determine the vulnerability to (or robustness against) diverse attack scenarios. Specifically, we examine how node- and link-based network growth or depletion based on specific attack criteria affect their robustness gauged in terms of the largest connected component (LCC) size and diameter. We employ partial least squares discriminant analysis to quantify the individual contribution of the indicators on LCC preservation while accounting for the collinearity stemming from the possible correlation between indicators. Our analysis of 14 complex network datasets and 5 attack models invariably reveals high modularity and disassortativity to be prime indicators of vulnerability, corroborating prior works that report disassortative modular networks to be particularly susceptible to targeted attacks. We conclude with a discussion as well as an illustrative example of the application of this work in fending off strategic attacks on critical infrastructures through models that adaptively and distributively achieve network robustness.
Language	English
Publishing date	2023-10-24
Publishing country	England
Document type	Journal Article
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-023-45218-9
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Article: CoVar: A generalizable machine learning approach to identify the coordinated regulators driving variational gene expression.

Roy, Satyaki / Sheikh, Shehzad Z / Furey, Terrence S

bioRxiv : the preprint server for biology

2023

Abstract	Network inference is used to model transcriptional, signaling, and metabolic interactions among genes, proteins, and metabolites that identify biological pathways influencing disease pathogenesis. Advances in machine learning (ML)-based inference models exhibit the predictive capabilities of capturing latent patterns in genomic data. Such models are emerging as an alternative to the statistical models identifying causative factors driving complex diseases. We present CoVar, an inference framework that builds upon the properties of existing inference models, to find the central genes driving perturbed gene expression across biological states. We leverage ML-based network inference to find networks that capture the strength of regulatory interactions. Our model first pinpoints a subset of genes, termed variational, whose expression variabilities typify the differences in network connectivity between the control and perturbed data. Variational genes, by being differentially expressed themselves or possessing differentially expressed neighbor genes, capture gene expression variability. CoVar then creates subnetworks comprising variational genes and their strongly connected neighbor genes and identifies core genes central to these subnetworks that influence the bulk of the variational activity. Through the analysis of yeast expression data perturbed by the deletion of the mitochondrial genome, we show that CoVar identifies key genes not found through independent differential expression analysis.
Language	English
Publishing date	2023-01-12
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.01.12.523808
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Article ; Online: A Comparative Study on Distancing, Mask and Vaccine Adoption Rates from Global Twitter Trends

Satyaki Roy / Preetam Ghosh

Healthcare, Vol 9, Iss 488, p

2021 Volume 488

Abstract: COVID-19 is a global health emergency that has fundamentally altered human life. Public perception about COVID-19 greatly informs public policymaking and charts the course of present and future mitigation strategies. Existing approaches to gain insights ... ...

Abstract	COVID-19 is a global health emergency that has fundamentally altered human life. Public perception about COVID-19 greatly informs public policymaking and charts the course of present and future mitigation strategies. Existing approaches to gain insights into the evolving nature of public opinion has led to the application of natural language processing on public interaction data acquired from online surveys and social media. In this work, we apply supervised and unsupervised machine learning approaches on global Twitter data to learn the opinions about adoption of mitigation strategies such as social distancing, masks, and vaccination, as well as the effect of socioeconomic, demographic, political, and epidemiological features on perceptions. Our study reveals the uniform polarity in public sentiment on the basis of spatial proximity or COVID-19 infection rates. We show the reservation about the adoption of social distancing and vaccination across the world and also quantify the influence of airport traffic, homelessness, followed by old age and race on sentiment of netizens within the US.
Keywords	COVID-19 ; machine learning ; tweets ; sentiment analysis ; adoption rates ; socioeconomic ; Medicine ; R
Subject code	306
Language	English
Publishing date	2021-04-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Scalable and distributed strategies for socially distanced human mobility

Satyaki Roy / Preetam Ghosh

Applied Network Science, Vol 6, Iss 1, Pp 1-

2021 Volume 19

Abstract: Abstract COVID-19 is a global health crisis that has caused ripples in every aspect of human life. Amid widespread vaccinations testing, manufacture and distribution efforts, nations still rely on human mobility restrictions to mitigate infection and ... ...

Abstract	Abstract COVID-19 is a global health crisis that has caused ripples in every aspect of human life. Amid widespread vaccinations testing, manufacture and distribution efforts, nations still rely on human mobility restrictions to mitigate infection and death tolls. New waves of infection in many nations, indecisiveness on the efficacy of existing vaccinations, and emerging strains of the virus call for intelligent mobility policies that utilize contact pattern and epidemiological data to check contagion. Our earlier work leveraged network science principles to design social distancing optimization approaches that show promise in slowing infection spread however, they prove to be computationally prohibitive and require complete knowledge of the social network. In this work, we present scalable and distributed versions of the optimization approaches based on Markov Chain Monte Carlo Gibbs sampling and grid-based spatial parallelization that tackle both the challenges faced by the optimization strategies. We perform extensive simulation experiments to show the ability of the proposed strategies to meet necessary network science measures and yield performance comparable to the optimal counterpart, while exhibiting significant speed-up. We study the scalability of the proposed strategies as well as their performance in realistic scenarios when a fraction of the population temporarily flouts the location recommendations.
Keywords	Social distancing ; Network science ; Clustering ; Sampling ; Parallelization ; Applied mathematics. Quantitative methods ; T57-57.97
Subject code	006
Language	English
Publishing date	2021-12-01T00:00:00Z
Publisher	SpringerOpen
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: A Comparative Study on Distancing, Mask and Vaccine Adoption Rates from Global Twitter Trends.

Roy, Satyaki / Ghosh, Preetam

Healthcare (Basel, Switzerland)

2021 Volume 9, Issue 5

Abstract	COVID-19 is a global health emergency that has fundamentally altered human life. Public perception about COVID-19 greatly informs public policymaking and charts the course of present and future mitigation strategies. Existing approaches to gain insights into the evolving nature of public opinion has led to the application of natural language processing on public interaction data acquired from online surveys and social media. In this work, we apply supervised and unsupervised machine learning approaches on global Twitter data to learn the opinions about adoption of mitigation strategies such as social distancing, masks, and vaccination, as well as the effect of socioeconomic, demographic, political, and epidemiological features on perceptions. Our study reveals the uniform polarity in public sentiment on the basis of spatial proximity or COVID-19 infection rates. We show the reservation about the adoption of social distancing and vaccination across the world and also quantify the influence of airport traffic, homelessness, followed by old age and race on sentiment of netizens within the US.
Language	English
Publishing date	2021-04-21
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2721009-1
ISSN	2227-9032
ISSN	2227-9032
DOI	10.3390/healthcare9050488
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