Classification of a DNA Microarray for Diagnosing Cancer Using a Complex Network Based Method

2019 ◽  
Vol 16 (3) ◽  
pp. 801-808 ◽  
Author(s):  
Peng Wu ◽  
Dong Wang
Keyword(s):  
Complex Network ◽  
Dna Microarray

scholarly journals DNA Microarray and miRNA Analyses Reinforce the Classification of Follicular Thyroid Tumors

2013 ◽  
Vol 98 (5) ◽  
pp. E981-E989 ◽  
Author(s):  
Caroline Jacques ◽  
Delphine Guillotin ◽  
Jean-Fred Fontaine ◽  
Brigitte Franc ◽  
Delphine Mirebeau-Prunier ◽  
...  
Keyword(s):  
Dna Microarray ◽  
Thyroid Tumors ◽  
Follicular Thyroid Tumors

scholarly journals Classification of Literary Works: Fractality and Complexity of the Narrative, Essay, and Research Article

Entropy ◽  
2020 ◽  
Vol 22 (8) ◽  
pp. 904
Author(s):  
Aldo Ramirez-Arellano
Keyword(s):  
Complex Network ◽  
Path Length ◽  
Research Topic ◽  
Clustering Coefficient ◽  
Node Degree ◽  
Original Text ◽  
Literary Works ◽  
Research Article ◽  
A Current

A complex network as an abstraction of a language system has attracted much attention during the last decade. Linguistic typological research using quantitative measures is a current research topic based on the complex network approach. This research aims at showing the node degree, betweenness, shortest path length, clustering coefficient, and nearest neighbourhoods’ degree, as well as more complex measures such as: the fractal dimension, the complexity of a given network, the Area Under Box-covering, and the Area Under the Robustness Curve. The literary works of Mexican writers were classify according to their genre. Precisely 87% of the full word co-occurrence networks were classified as a fractal. Also, empirical evidence is presented that supports the conjecture that lemmatisation of the original text is a renormalisation process of the networks that preserve their fractal property and reveal stylistic attributes by genre.

A Complex Network-Based Approach to the Analysis and Classification of Images

2015 ◽  
pp. 322-330 ◽  
Author(s):  
Geovana V. L. de Lima ◽  
Thullyo R. Castilho ◽  
Pedro H. Bugatti ◽  
Priscila T. M. Saito ◽  
Fabrício M. Lopes
Keyword(s):  
Complex Network ◽  
Classification Of Images

Improving pattern classification of DNA microarray data by using PCA and logistic regression

2016 ◽  
Vol 20 (s1) ◽  
pp. S53-S67 ◽  
Author(s):  
Ricardo Ocampo-Vega ◽  
Gildardo Sanchez-Ante ◽  
Marco A. de Luna ◽  
Roberto Vega ◽  
Luis E. Falcón-Morales ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Dna Microarray ◽  
Pattern Classification ◽  
Microarray Data ◽  
Dna Microarray Data

scholarly journals A Genome based Detection and Classification of Coronavirus Infection

2020 ◽  
Vol 9 (08) ◽  
pp. 25148-25155
Author(s):  
Chhanda Ray ◽  
Ankita Sasmal
Keyword(s):  
Gene Expression ◽  
Dna Microarray ◽  
Gene Expression Data ◽  
Microarray Gene Expression Data ◽  
Expression Data ◽  
Microarray Gene Expression ◽  
Chain Code ◽  
Coronavirus Infection ◽  
Microarray Gene

The Coronavirus (COVID-19) infection has become a global threat in recent time. Many researchers have been dedicated to control COVID-19 pandemic. In this paper, an effective method is presented for detection and classification of COVID-19 infection based on genome sequences. First, the COVID-19 infection is detected based on the induction of changes in the DNA microarray gene expression pattern of the host during and after infection and comparing it with DNA sequences of Coronavirus (SARS-CoV-2). In order to analyse DNA microarray gene expression data, a bi-directional string matching algorithm is used and the analytical result is represented in terms of eight-directional chain code sequence. At the end of the work, an approach for categorization of Coronavirus infection is provided based on the distribution probabilities of eight-directional chain code sequences correspond to DNA microarray gene expression data of different Corona viruses by taking random samples from the GenBank. The categorization of Coronavirus infection will be helpful for forecasting rate of mortality, rate of infection, severity of the infection and other issues related to COVID-19. 

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