Rajnish Kumar

Dr. Rajnish Kumar

Queen's University Belfast, U.K.

Research Focus & Metrics

Academic Career Length

8Years (2019-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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13 June 2020 in Annals of Operations Research
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Journal Article
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DCDhrubajit Choudhury
Dhrubajit Choudhury
Surajit BorkotokeySurajit Borkotokey
Surajit Borkotokey
Rajnish KumarRajnish Kumar
Rajnish Kumar
SSSudipta Sarangi
Sudipta Sarangi

1 October 2020 in European Journal of Operational Research
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Journal Article
Closed access
Surajit BorkotokeySurajit Borkotokey
Surajit Borkotokey
DCDhrubajit Choudhury
Dhrubajit Choudhury
Loyimee GogoiLoyimee Gogoi
Loyimee Gogoi
Rajnish KumarRajnish Kumar
Rajnish Kumar

9 June 2025 in Engineering Applications of Artificial Intelligence
Verified
Journal Article
Closed access
PDPalash Dutta
Palash Dutta
AKAlakananda Konwar
Alakananda Konwar
Rajnish KumarRajnish Kumar
Rajnish Kumar
NPNiladri Palit
Niladri Palit

29 July 2020 in Operational Research
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Journal Article
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Surajit BorkotokeySurajit Borkotokey
Surajit Borkotokey
Loyimee GogoiLoyimee Gogoi
Loyimee Gogoi
DCDhrubajit Choudhury
Dhrubajit Choudhury
Rajnish KumarRajnish Kumar
Rajnish Kumar

13 August 2020 in Diagnostics
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Journal Article
Open access
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Papori BoraPapori Bora
Papori Bora
VBVishwa Jyoti Baruah
Vishwa Jyoti Baruah
Surajit BorkotokeySurajit Borkotokey
Surajit Borkotokey
Loyimee GogoiLoyimee Gogoi
Loyimee Gogoi
PMPriyakshi Mahanta
Priyakshi Mahanta
Stefano MorettiStefano Moretti
Stefano Moretti
Microarray techniques are used to generate a large amount of information on gene expression. This information can be statistically processed and analyzed to identify the genes useful for the diagnosis and prognosis of genetic diseases. Game theoretic tools are applied to analyze the gene expression data. Gene co-expression networks are increasingly used to explore the system-level functionality of genes, where the roles of the genes in building networks in addition to their independent activities are also considered. In this paper, we develop a novel microarray network game by constructing a gene co-expression network and defining a game on this network. The notion of the Link Relevance Index (LRI) for this network game is introduced and characterized. The LRI successfully identifies the relevant cancer biomarkers. It also enables identifying salient genes in the colon cancer dataset. Network games can more accurately describe the interactions among genes as their basic premises are to consider the interactions among players prescribed by a network structure. LRI presents a tool to identify the underlying salient genes involved in cancer or other metabolic syndromes.

4 August 2023 in Annals of Operations Research
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Journal Article
Open access
PDF file
SCSubhadip Chakrabarti
Subhadip Chakrabarti
LGLoyimee Gogoi
Loyimee Gogoi
Robert GillesRobert Gilles
Robert Gilles
Surajit BorkotokeySurajit Borkotokey
Surajit Borkotokey
Rajnish KumarRajnish Kumar
Rajnish Kumar
A network game assigns a level of collectively generated wealth to every network that can form on a given set of players. A variable network game combines a network game with a network formation probability distribution, describing certain restrictions on network formation. Expected levels of collectively generated wealth and expected individual payoffs can be formulated in this setting. We investigate properties of the resulting expected wealth levels as well as the expected variants of well-established network game values as allocation rules that assign to every variable network game a payoff to the players in a variable network game. We establish two axiomatizations of the Expected Myerson Value, originally formulated and proven on the class of communication situations, based on the well-established component balance, equal bargaining power and balanced contributions properties. Furthermore, we extend an established axiomatization of the Position Value based on the balanced link contribution property to the Expected Position Value.