A study on the most common algorithms implemented for cancer gene search and classifications

Murad M. Al-Rajab, Joan Lu

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Understanding gene expression is an important factor for cancer diagnosis. One target of this understanding is implementing cancer gene search and classification methods. However, cancer gene search and classification is a challenge in that there is no obvious exact algorithm that can be implemented individually for various cancer cells. In this paper, a research is conducted through the most common top-ranked algorithms implemented for cancer gene search and classification, and on how they are implemented to reach a better performance. The paper will distinguish algorithms implemented for bio-image analysis for cancer cells and algorithms implemented based on DNA microarray data. The paper will also explore the road map towards presenting the most current algorithms implemented for cancer gene search and classification, as well as focusing on the importance of search algorithms and how they are implemented to enhance search and the factors that affect the performance.

Original languageEnglish
Pages (from-to)159-176
Number of pages18
JournalInternational Journal of Data Mining and Bioinformatics
Volume14
Issue number2
Early online date12 Feb 2016
DOIs
Publication statusPublished - 2016

Fingerprint

Neoplasm Genes
cancer
Genes
Cells
Neoplasms
Microarrays
Oligonucleotide Array Sequence Analysis
Gene expression
Image analysis
DNA
performance
Gene Expression
Research

Cite this

@article{b9e58b75ccac47c0bad846d3b8257a82,
title = "A study on the most common algorithms implemented for cancer gene search and classifications",
abstract = "Understanding gene expression is an important factor for cancer diagnosis. One target of this understanding is implementing cancer gene search and classification methods. However, cancer gene search and classification is a challenge in that there is no obvious exact algorithm that can be implemented individually for various cancer cells. In this paper, a research is conducted through the most common top-ranked algorithms implemented for cancer gene search and classification, and on how they are implemented to reach a better performance. The paper will distinguish algorithms implemented for bio-image analysis for cancer cells and algorithms implemented based on DNA microarray data. The paper will also explore the road map towards presenting the most current algorithms implemented for cancer gene search and classification, as well as focusing on the importance of search algorithms and how they are implemented to enhance search and the factors that affect the performance.",
keywords = "Bioinformatics, Cancer genes, Classification algorithms, Data mining, Genes, Performance evaluation, Search algorithms, Cancer gene search, Cancer gene classification, Gene expression, Cancer Diagnosis, Bio-image, Analysis, Cancer Cells, DNA microarray data",
author = "Al-Rajab, {Murad M.} and Joan Lu",
year = "2016",
doi = "10.1504/IJDMB.2016.074685",
language = "English",
volume = "14",
pages = "159--176",
journal = "International Journal of Data Mining and Bioinformatics",
issn = "1748-5673",
publisher = "Inderscience Enterprises Ltd",
number = "2",

}

A study on the most common algorithms implemented for cancer gene search and classifications. / Al-Rajab, Murad M.; Lu, Joan.

In: International Journal of Data Mining and Bioinformatics, Vol. 14, No. 2, 2016, p. 159-176.

Research output: Contribution to journalArticle

TY - JOUR

T1 - A study on the most common algorithms implemented for cancer gene search and classifications

AU - Al-Rajab, Murad M.

AU - Lu, Joan

PY - 2016

Y1 - 2016

N2 - Understanding gene expression is an important factor for cancer diagnosis. One target of this understanding is implementing cancer gene search and classification methods. However, cancer gene search and classification is a challenge in that there is no obvious exact algorithm that can be implemented individually for various cancer cells. In this paper, a research is conducted through the most common top-ranked algorithms implemented for cancer gene search and classification, and on how they are implemented to reach a better performance. The paper will distinguish algorithms implemented for bio-image analysis for cancer cells and algorithms implemented based on DNA microarray data. The paper will also explore the road map towards presenting the most current algorithms implemented for cancer gene search and classification, as well as focusing on the importance of search algorithms and how they are implemented to enhance search and the factors that affect the performance.

AB - Understanding gene expression is an important factor for cancer diagnosis. One target of this understanding is implementing cancer gene search and classification methods. However, cancer gene search and classification is a challenge in that there is no obvious exact algorithm that can be implemented individually for various cancer cells. In this paper, a research is conducted through the most common top-ranked algorithms implemented for cancer gene search and classification, and on how they are implemented to reach a better performance. The paper will distinguish algorithms implemented for bio-image analysis for cancer cells and algorithms implemented based on DNA microarray data. The paper will also explore the road map towards presenting the most current algorithms implemented for cancer gene search and classification, as well as focusing on the importance of search algorithms and how they are implemented to enhance search and the factors that affect the performance.

KW - Bioinformatics

KW - Cancer genes

KW - Classification algorithms

KW - Data mining

KW - Genes

KW - Performance evaluation

KW - Search algorithms

KW - Cancer gene search

KW - Cancer gene classification

KW - Gene expression

KW - Cancer Diagnosis

KW - Bio-image

KW - Analysis

KW - Cancer Cells

KW - DNA microarray data

UR - http://www.scopus.com/inward/record.url?scp=84958696267&partnerID=8YFLogxK

U2 - 10.1504/IJDMB.2016.074685

DO - 10.1504/IJDMB.2016.074685

M3 - Article

VL - 14

SP - 159

EP - 176

JO - International Journal of Data Mining and Bioinformatics

JF - International Journal of Data Mining and Bioinformatics

SN - 1748-5673

IS - 2

ER -