NDLI: Constructing gene similarity networks using co-occurrence probabilities

Content Provider	Springer Nature : BioMed Central
Author	Mirzaei, Golrokh
Abstract	Gene similarity networks play important role in unraveling the intricate associations within diverse cancer types. Conventionally, gauging the similarity between genes has been approached through experimental methodologies involving chemical and molecular analyses, or through the lens of mathematical techniques. However, in our work, we have pioneered a distinctive mathematical framework, one rooted in the co-occurrence of attribute values and single point mutations, thereby establishing a novel approach for quantifying the dissimilarity or similarity among genes. Central to our approach is the recognition of mutations as key players in the evolutionary trajectory of cancer. Anchored in this understanding, our methodology hinges on the consideration of two categorical attributes: mutation type and nucleotide change. These attributes are pivotal, as they encapsulate the critical variations that can precipitate substantial changes in gene behavior and ultimately influence disease progression. Our study takes on the challenge of formulating similarity measures that are intrinsic to genes' categorical data. Taking into account the co-occurrence probability of attribute values within single point mutations, our innovative mathematical approach surpasses the boundaries of conventional methods. We thereby provide a robust and comprehensive means to assess gene similarity and take a significant step forward in refining the tools available for uncovering the subtle yet impactful associations within the complex realm of gene interactions in cancer.
Related Links	https://bmcgenomics.biomedcentral.com/counter/pdf/10.1186/s12864-023-09780-w.pdf
Ending Page	13
Page Count	13
Starting Page	1
File Format	HTM / HTML
ISSN	14712164
DOI	10.1186/s12864-023-09780-w
Journal	BMC Genomics
Issue Number	1
Volume Number	24
Language	English
Publisher	BioMed Central
Publisher Date	2023-11-21
Access Restriction	Open
Subject Keyword	Life Sciences Microarrays Proteomics Animal Genetics and Genomics Microbial Genetics and Genomics Plant Genetics and Genomics Similarity network Gene network Probability Co-ccurrence Cancer
Content Type	Text
Resource Type	Article
Subject	Biotechnology Genetics
Journal Impact Factor	3.5/2023
5-Year Journal Impact Factor	4.1/2023

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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