Dimension Reduction of Microarray Data Using Gene Ontology and Correlation Filter

被引：1

作者：

Banerjee, Ayan ^{[1
]}

Pati, Soumen Kumar ^{[2
]}

Gupta, Manan Kumar ^{[2
]}

机构：

[1] Jalpaiguri Govt Engn Coll, Dept Comp Sci, Jalpaiguri, W Bengal, India

[2] Maulana Abul Kalam Azad Univ Technol, Dept Bioinformat, Nadia, W Bengal, India

来源：

COMPUTATIONAL INTELLIGENCE IN PATTERN RECOGNITION, CIPR 2020 | 2020年 / 1120卷

关键词：

Gene ontology; Protein-protein interaction; Pearson's correlation coefficient; Gene selection;

D O I：

10.1007/978-981-15-2449-3_26

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Analysis of every variable at a microscopic level is not a feasible way. It might take a long time to perform any meaningful analysis. So, the valuable time and money are wasted for analysis of high dimensional data. In this paper, a better way is given to deal with high dimensional data and proposed a novel dimension reduction technique based on gene ontology and Pearson's correlation coefficient. The names of genes are extracted from microarray data and classify them based on biological processes of gene ontology and find their relation with cellular components. Next, the gene correlation factor is identified on every network of each of the group. Lastly, the independent component (IC) value of the genes related to the gene ontology is calculated and selects only those genes having the highest IC value of their corresponding network and eliminates the rest of the genes. It reduces size of the dataset at least 40% from its original size.

引用

页码：303 / 313

页数：11

共 50 条

[31] BayGO: Bayesian analysis of ontology term enrichment in microarray data [J].

Ricardo ZN Vêncio ;

Tie Koide ;

Suely L Gomes ;

Carlos A de B Pereira .

BMC Bioinformatics, 7

[32] Classification and diagnostic prediction of cancers using gene microarray data analysis [J].

Osareh, Alireza ;

Shadgar, Bita .

Journal of Applied Sciences, 2009, 9 (03) :459-468

[33] Gene selection for classifying microarray data using grey relation analysis [J].

Zhang, Li-Juan ;

Li, Zhou-Jun .

DISCOVERY SCIENCE, PROCEEDINGS, 2006, 4265 :378-382

[34] Gene Selection for Cancer Classification from Microarray Data Using Data Overlap Measure [J].

Sarbazi-Azad, Saeed ;

Abadeh, Mohammad Saniee .

2018 25TH IRANIAN CONFERENCE ON BIOMEDICAL ENGINEERING AND 2018 3RD INTERNATIONAL IRANIAN CONFERENCE ON BIOMEDICAL ENGINEERING (ICBME), 2018, :257-262

[35] Handling gene redundancy in microarray data using Grey Relational Analysis [J].

Zhang, Li-Juan ;

Li, Zhou-Jun .

INTERNATIONAL JOURNAL OF DATA MINING AND BIOINFORMATICS, 2008, 2 (02) :134-144

[36] Gene selection for microarray data analysis using principal component analysis [J].

Wang, AT ;

Gehan, EA .

STATISTICS IN MEDICINE, 2005, 24 (13) :2069-2087

[37] Microarray data mining using landmark gene-guided clustering [J].

Pankaj Chopra ;

Jaewoo Kang ;

Jiong Yang ;

HyungJun Cho ;

Heenam Stanley Kim ;

Min-Goo Lee .

BMC Bioinformatics, 9

[38] Gene selection and classification from microarray data using kernel machine [J].

Cho, JH ;

Lee, D ;

Park, JH ;

Lee, IB .

FEBS LETTERS, 2004, 571 (1-3) :93-98

[39] Gene selection for tumor classification using microarray gone expression data [J].

Yendrapalli, K. ;

Basnet, R. ;

Mukkamala, S. ;

Sung, A. H. .

WORLD CONGRESS ON ENGINEERING 2007, VOLS 1 AND 2, 2007, :290-+

[40] Membrane gene ontology bias in sequencing and microarray obtained by housekeeping-gene analysis [J].

Zhang, Yijuan ;

Akintola, Oluwafemi S. ;

Liu, Ken J. A. ;

Sun, Bingyun .

GENE, 2016, 575 (02) :559-566

← 1 2 3 4 5 →