A hybrid gene selection algorithm based on interaction information for microarray-based cancer classification

Nakariyakul, Songyot

doi:10.1371/journal.pone.0212333

Cited by 33 publications

(17 citation statements)

References 33 publications

(31 reference statements)

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“…We tested our model on a previously reported colon cancer dataset [ 4 , 16 , 35 , 36 ] ( .) containing the expression of 2000 genes with highest minimal intensity across 62 tissues.…”

Section: Resultsmentioning

confidence: 99%

Identifying the Salient Genes in Microarray Data: A Novel Game Theoretic Model for the Co-Expression Network

et al. 2020

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Microarray techniques are used to generate a large amount of information on gene expression. This information can be statistically processed and analyzed to identify the genes useful for the diagnosis and prognosis of genetic diseases. Game theoretic tools are applied to analyze the gene expression data. Gene co-expression networks are increasingly used to explore the system-level functionality of genes, where the roles of the genes in building networks in addition to their independent activities are also considered. In this paper, we develop a novel microarray network game by constructing a gene co-expression network and defining a game on this network. The notion of the Link Relevance Index (LRI) for this network game is introduced and characterized. The LRI successfully identifies the relevant cancer biomarkers. It also enables identifying salient genes in the colon cancer dataset. Network games can more accurately describe the interactions among genes as their basic premises are to consider the interactions among players prescribed by a network structure. LRI presents a tool to identify the underlying salient genes involved in cancer or other metabolic syndromes.

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“…We tested our model on a previously reported colon cancer dataset [ 4 , 16 , 35 , 36 ] ( .) containing the expression of 2000 genes with highest minimal intensity across 62 tissues.…”

Section: Resultsmentioning

confidence: 99%

Identifying the Salient Genes in Microarray Data: A Novel Game Theoretic Model for the Co-Expression Network

et al. 2020

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“…A classification framework applied to cancer gene expression profiles was done by Hijazi and Chan [ 21 ]. A hybrid gene selection algorithm based on interaction information technology was utilized for microarray-based colon cancer classification [ 22 ]. A gene selection methodology based on clustering for classification tasks in colon cancer was done by Garzon and Gonzalez [ 23 ].…”

Section: Introductionmentioning

confidence: 99%

An Amalgamated Approach to Bilevel Feature Selection Techniques Utilizing Soft Computing Methods for Classifying Colon Cancer

Prabhakar

Rajaguru²,

Kim

2020

BioMed Research International

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One of the deadliest diseases which affects the large intestine is colon cancer. Older adults are typically affected by colon cancer though it can happen at any age. It generally starts as small benign growth of cells that forms on the inside of the colon, and later, it develops into cancer. Due to the propagation of somatic alterations that affects the gene expression, colon cancer is caused. A standardized format for assessing the expression levels of thousands of genes is provided by the DNA microarray technology. The tumors of various anatomical regions can be distinguished by the patterns of gene expression in microarray technology. As the microarray data is too huge to process due to the curse of dimensionality problem, an amalgamated approach of utilizing bilevel feature selection techniques is proposed in this paper. In the first level, the genes or the features are dimensionally reduced with the help of Multivariate Minimum Redundancy–Maximum Relevance (MRMR) technique. Then, in the second level, six optimization techniques are utilized in this work for selecting the best genes or features before proceeding to classification process. The optimization techniques considered in this work are Invasive Weed Optimization (IWO), Teaching Learning-Based Optimization (TLBO), League Championship Optimization (LCO), Beetle Antennae Search Optimization (BASO), Crow Search Optimization (CSO), and Fruit Fly Optimization (FFO). Finally, it is classified with five suitable classifiers, and the best results show when IWO is utilized with MRMR, and then classified with Quadratic Discriminant Analysis (QDA), a classification accuracy of 99.16% is obtained.

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“…Moreover, for selecting 116 a feature, they proposed to use χ 2 statistics by showing that all three terms follow χ 2 117 distribution. Moreover, even though it has few good characteristics, by incorporating 118 the term redundancy in gene expression data, informative genes might be discarded [11]. 119 Another issue with gene selection for cancer classification, in contrast to traditional 120 feature selection methods in machine learning, is that the set of genes selected should be 121 biologically relevant to the disease under study.…”

Section: Introduction 18mentioning

confidence: 99%

“…However, this method selected more genes than the wrapper or hybrid algorithms. To 56 solve the limitations of IGIS, improved Interaction information-Guided Incremental 57 Selection (IGIS+) [11] was proposed where the first gene is selected based on the highest 58 accuracy and utilizes Cohen's d test to add a new gene into the selected gene set. One 59 major limitation is that it uses several handcrafted thresholds for Cohen's d effect.…”

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confidence: 99%

Use of relevancy and complementary information for discriminatory gene selection from high-dimensional cancer data

Haque

Sharmin

Ali

et al. 2020

Preprint

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With the advent of high-throughput technologies, life sciences are generating a huge 1 amount of biomolecular data. Global gene expression profiles provide a snapshot of all 2 the genes that are transcribed or not in a cell or in a tissue at a particular moment 3 under a particular condition. The high-dimensionality of such gene expression data 4 (i.e., very large number of features/genes analyzed in relatively much less number of 5 samples) makes it difficult to identify the key genes (biomarkers) that are truly and 6 more significantly attributing to a particular phenotype or condition, such as cancer or 7 disease, de novo. With the increase in the number of genes, simple feature selection 8 methods show poor performance for both selecting the effective and informative features 9 and capturing biological information. Addressing these issues, here we propose Mutual 10 information based Gene Selection method (M GS) for selecting informative genes and 11 two ranking methods based on frequency (M GS f ) and Random Forest (M GS rf ) for 12 ranking the selected genes. We tested our methods on four real gene expression datasets 13 derived from different studies on cancerous and normal samples. Our methods obtained 14 better classification rate with the datasets compared to recently reported methods. Our 15 methods could also detect the key relevant pathways with a causal relationship to the 16 phenotype. 17 25 indicator of a particular state). Identification of these informative genes is very 26 important for elucidating developmental and disease mechanisms, disease diagnosis, 27 February 22, 2020 1/15drug development, etc. Especially, for different cancer diseases, these informative genes 28 may be invaluable for the improvement of diagnosis, prognosis, and treatment. 29Usually, studies to generate cancer specific gene expression profiles comprise a small 30 number of control and patient samples in comparison to tens of thousands of genes 31 (high dimensionality of the data) in each sample where only a few numbers of genes are 32 responsible for a disease. From a large set of genes, identification of a subset that is 33 differently expressed in cancerous cells compared to the normal ones, is a challenging 34 task and is considered as NP hard or NP-complete [1]. Therefore, the feature/gene 35 selection methods can be a useful way to identify a subset of genes relevant to particular 36 cancer for better diagnosis and treatment. In this paper, we use the terms "gene" and 37 "feature" interchangeably. 38In bioinformatics, several gene selection methods have been proposed, particularly 39 for cancer data classification [2][3][4]. "Wrapper"and "Filter"are two popular categories of 40 feature selection methods [5] where wrapper methods are classifier dependent and filter 41 methods are classifier independent and their performance mainly depends on the 42 selection of a criterion. Wrapper based methods select the most discriminant subset of 43 features by minimizing the prediction error of a particular classifier [6]. Suppor...

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A hybrid gene selection algorithm based on interaction information for microarray-based cancer classification

Cited by 33 publications

References 33 publications

Identifying the Salient Genes in Microarray Data: A Novel Game Theoretic Model for the Co-Expression Network

Identifying the Salient Genes in Microarray Data: A Novel Game Theoretic Model for the Co-Expression Network

An Amalgamated Approach to Bilevel Feature Selection Techniques Utilizing Soft Computing Methods for Classifying Colon Cancer

Use of relevancy and complementary information for discriminatory gene selection from high-dimensional cancer data

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