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

Al-Rajab, Murad; Lu, Joan

doi:10.1504/ijdmb.2016.074685

Cited by 7 publications

(2 citation statements)

References 27 publications

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“…Other algorithms have been used for the purpose of genetic classification in the field of cancer research, featuring both machine learning algorithms and feature selection [39]. Some of these have been very successful in identifying signs of colon cancer [22,40,41]; the taxonomic approaches used include Genetic Algorithm (GA), Particle Swarm Optimisation (PSO), Information Gain (IG), minimum Redundancy Maximum Relevance (mRMR) (for feature selection), Support Vector Machines (SVM), Naïve Bayes (NB), Decision Trees (DT) and Genetic Programming (GP) [39,40,[42][43][44][45].…”

Section: Background and Literature Reviewmentioning

confidence: 99%

“…This combination is different from previous two stage approached, and we tested the accuracy using four classifiers: SVM, NB, DT and KNN to ensure the investigation is conducted rigorously. The reasons to employ these algorithms are: 1) They had shown better performance than other selection algorithms in the field, and had reflected very good effectiveness in many colorectal cancer research studies [39][40][41]; 2) GA has the ability to manage high dimensionality datasets for the colon cancer [65][66][67]; 3) GA can achieve interesting results when combined with other algorithms [68]; 4) GA is easily integrated and worked in parallel with other algorithms; 5) IG had advantages in eliminating redundant genes and reducing noise [26,69,70]. ; 6) Combining the GA and IG in stage 1 of this framework model will achieve the target of generating a subset of features which are top ranked and with very good quality; 7) utilizing the mRMR in as a multifilter in stage 2 will refine the subset generated from stage 1 through another subset selection of features.…”

Section: The Objectives Of Investigationmentioning

confidence: 99%

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A framework model using multifilter feature selection to enhance colon cancer classification

Al-Rajab

2021

PLoS ONE

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Gene expression profiles can be utilized in the diagnosis of critical diseases such as cancer. The selection of biomarker genes from these profiles is significant and crucial for cancer detection. This paper presents a framework proposing a two-stage multifilter hybrid model of feature selection for colon cancer classification. Colon cancer is being extremely common nowadays among other types of cancer. There is a need to find fast and an accurate method to detect the tissues, and enhance the diagnostic process and the drug discovery. This paper reports on a study whose objective has been to improve the diagnosis of cancer of the colon through a two-stage, multifilter model of feature selection. The model described deals with feature selection using a combination of Information Gain and a Genetic Algorithm. The next stage is to filter and rank the genes identified through this method using the minimum Redundancy Maximum Relevance (mRMR) technique. The final phase is to further analyze the data using correlated machine learning algorithms. This two-stage approach, which involves the selection of genes before classification techniques are used, improves success rates for the identification of cancer cells. It is found that Decision Tree, K-Nearest Neighbor, and Naïve Bayes classifiers had showed promising accurate results using the developed hybrid framework model. It is concluded that the performance of our proposed method has achieved a higher accuracy in comparison with the existing methods reported in the literatures. This study can be used as a clue to enhance treatment and drug discovery for the colon cancer cure.

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Section: Background and Literature Reviewmentioning

confidence: 99%

Section: The Objectives Of Investigationmentioning

confidence: 99%

A framework model using multifilter feature selection to enhance colon cancer classification

Al-Rajab

2021

PLoS ONE

Self Cite

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Interactive Visual Analytics of Big Data

Leung

Carmichael

Johnstone

et al. 2017

Ontologies and Big Data Considerations for Effective Intelligence

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High volumes of a wide variety of data can be easily generated at a high velocity in many real-life applications. Implicitly embedded in these big data is previously unknown and potentially useful knowledge such as frequently occurring sets of items, merchandise, or events. Different algorithms have been proposed for either retrieving information about the data or mining the data to find frequent sets, which are usually presented in a lengthy textual list. As “a picture is worth a thousand words”, the use of visual representations can enhance user understanding of the inherent relationships among the mined frequent sets. However, many of the existing visualizers were not designed to visualize these mined frequent sets. This book chapter presents an interactive next-generation visual analytic system. The system enables the management, visualization, and advanced analysis of the original big data and the frequent sets mined from the data.

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Importance of Information Working With Colon Cancer Research

2021

Machine Learning in Cancer Research With Applications in Colon Cancer and Big Data Analysis

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Modern science helps us to understand the changing world around us, across fields such as biology, computer science, mathematics, statistics, chemistry, computational biology, biotechnology, biochemistry, and many others. An important branch of science that has had a large impact on the medical field is bioinformatics. This chapter introduces the importance of information science into colon cancer research. According to the American Cancer Association, in the United States in 2018, 97,220 new cases of colon cancer (CC) were identified. The research into this topic area is an immediate need to save many lives and improve people's living standards.

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

Cited by 7 publications

References 27 publications

A framework model using multifilter feature selection to enhance colon cancer classification

A framework model using multifilter feature selection to enhance colon cancer classification

Interactive Visual Analytics of Big Data

Importance of Information Working With Colon Cancer Research

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