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About:
Effectiveness of Social Networks for Studying Biological Agents and Identifying Cancer Biomarkers
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schema:ScholarlyArticle
, within Data Space :
wasabi.inria.fr
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Type:
Academic Article
research paper
schema:ScholarlyArticle
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type
Academic Article
research paper
schema:ScholarlyArticle
isDefinedBy
Covid-on-the-Web dataset
has title
Effectiveness of Social Networks for Studying Biological Agents and Identifying Cancer Biomarkers
Creator
Alhajj, R
Alhajj, Reda
Demetrick, D
Demetrick, Douglas
Elsheikh, A
Elsheikh, Abdallah
Gao, S
Gao, Shang
Kianmehr, K
Kianmehr, Keivan
Nagi, M
Nagi, Mohamad
Naji, G
Naji, Ghada
Ridley, M
Ridley, Mick
Rokne, Jon
Rokne, J
topic
covid:c843c3ae661490959ed78113441b59d9e9555d59#this
Source
PMC
abstract
Social networks form phenomena that exist and evolve; they are dynamic. These phenomena have been realized and studied by the anthropology and sociology research communities since 1930. However, the recent rapid development in information technology and the internet has increased the interest in social networks and as a model they have been adapted to more applications and domains. Though researchers first studied social networks of humans, for our study described in this chapter we argue that genes and proteins act collaboratively and exist in communities analogous to humans, animals, insects, etc. They complement each other and collectively achieve specific tasks where some would have major roles appearing upfront and others may play minor background roles. However, molecules turn into aggressive actors when their internal structure is augmented; consequently, they may deviate from their target, change camp, and disturb other molecules leading to disaster. Such mutations may be uncontrolled and unintentionally occur inside a body, or they may be intentional and controlled by humans to serve one of two purposes, treatment or bioterrorism. In other words, mutation in the molecules (genes) can lead to a change in behavior. This may lead to good or bad effect, e.g., recovery from illness or diseases that may severely affect the body causing disability or death. Once mutated outside the body, molecules may turn into harmful biological weapons of mass destruction. The latter process does not require sophisticated equipment and hence is extremely dangerous with the uprising global terrorism activities. Bioterrorism is therefore a serious concern for humanity. One could say that mutated biological agents outside the body once misused could be way more dangerous than mutated molecules within the body. In this chapter, we will elaborate on bioterrorism and its consequences; we will also propose a model to study social networks of genes within the body leading to the identification of disease biomarkers.
has issue date
2011-03-13
(
xsd:dateTime
)
bibo:doi
10.1007/978-3-7091-0388-3_15
has license
no-cc
sha1sum (hex)
c843c3ae661490959ed78113441b59d9e9555d59
schema:url
https://doi.org/10.1007/978-3-7091-0388-3_15
resource representing a document's title
Effectiveness of Social Networks for Studying Biological Agents and Identifying Cancer Biomarkers
has PubMed Central identifier
PMC7176268
schema:publication
Counterterrorism and Open Source Intelligence
resource representing a document's body
covid:c843c3ae661490959ed78113441b59d9e9555d59#body_text
is
http://vocab.deri.ie/void#inDataset
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proxy:http/ns.inria.fr/covid19/c843c3ae661490959ed78113441b59d9e9555d59
is
schema:about
of
named entity 'uncontrolled'
named entity 'adapted'
named entity 'deviate'
named entity 'domains'
named entity 'change'
named entity 'RECENT'
named entity 'ROLES'
named entity 'INTEREST'
named entity 'APPLICATIONS'
named entity 'SPECIFIC'
named entity 'MODEL'
named entity 'TURN'
named entity 'PHENOMENA'
named entity 'OCCUR'
named entity 'phenomena'
named entity 'However'
named entity 'turn'
named entity 'evolve'
named entity 'structure'
named entity 'animals'
named entity 'exist'
named entity 'internal'
named entity 'argue'
named entity 'exist'
named entity 'internal structure'
named entity 'information technology'
named entity 'target'
named entity 'biological weapons'
named entity 'climate change'
named entity 'web pages'
named entity 'Acute lymphocytic leukemia'
named entity 'Data mining'
named entity 'p21'
named entity 'Apriori algorithm'
named entity 'terrorism'
named entity 'Apriori algorithm'
named entity 'social networks'
named entity 'data structures'
named entity 'long time'
named entity 'biomarker'
named entity 'frequent pattern mining'
named entity 'social network'
named entity 'frequent pattern mining'
named entity 'k-means clustering'
named entity 'bipartite graph'
named entity 'set of genes'
named entity 'bioterrorism'
named entity 'pharmacology'
named entity 'social interactions'
named entity 'neural network'
named entity 'competitive advantage'
named entity 'social network'
named entity 'SVMs'
named entity 'gene'
named entity 'Autoassociative Neural network'
named entity 'directed graphs'
named entity 'eigenvalue'
named entity 'mRNA'
named entity '13.5'
named entity 'Data preprocessing'
named entity 'gene expression'
named entity 'k-means clustering'
named entity 'acute myelogenous leukemia'
named entity 'algorithm'
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