A Comparative Study of Microarray Data Analysis for Cancer Classification

Chitode, Kshipra; Nagori, Meghana

doi:10.5120/14198-2392

Cited by 8 publications

(3 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Weka machine learning environment is employed in this research https://ai.waikato.ac.nz/weka/ , as the Weka resource provides a number of techniques that can be used for data validation. Two such techniques are ‘leave-one-out cross-validation’, or LOOCV, and k -fold cross-validation, both of which randomly classify items of data as being part of either ‘training’ or a ‘testing’ set [ 81 , 82 ]. The LOOCV approach involves a ‘classifier’ being learned for all bar one of a sample and tested on that one data point [ 83 ].…”

Section: Dataset Tools and Techniques Appliedmentioning

confidence: 99%

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

Al-Rajab

2021

PLoS ONE

View full text Add to dashboard Cite

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.

show abstract

Section: Dataset Tools and Techniques Appliedmentioning

confidence: 99%

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

Al-Rajab

2021

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…However, there are plenty of public available gene expression datasets commonly used by researchers in the field of cancer selection and classification experiments. Lists of the most publicly available colon cancer datasets can be found in [6,8,26,27,28].…”

Section: Background Of Datasetsmentioning

confidence: 99%

“…A set of algorithms that have previously demonstrated effectiveness in solving classification problems applied in machine learning studies has been adopted for the current study [19]. Chitode and Nogari defined classification as the process of discovering a prototype that designates and discriminates among different data classes (types) [8]. Classification accuracy is measured in terms of the proportion of expected samples to the overall number of samples, as represented in Equation 1 below [31]:…”

Section: Background Of Datasetsmentioning

confidence: 99%

Examining applying high performance genetic data feature selection and classification algorithms for colon cancer diagnosis

Al-Rajab

2017

Computer Methods and Programs in Biomedicine

View full text Add to dashboard Cite

show abstract

A reductionist approach to extract robust molecular markers from microarray data series – Isolating markers to track osseointegration

Barik

Banerjee

Dhara

et al. 2017

Journal of Biomedical Informatics

View full text Add to dashboard Cite

Complexities in the full genome expression studies hinder the extraction of tracker genes to analyze the course of biological events. In this study, we demonstrate the applications of supervised machine learning methods to reduce the irrelevance in microarray data series and thereby extract robust molecular markers to track biological processes. The methodology has been illustrated by analyzing whole genome expression studies on bone-implant integration (ossointegration). Being a biological process, osseointegration is known to leave a trail of genetic footprint during the course. In spite of existence of enormous amount of raw data in public repositories, researchers still do not have access to a panel of genes that can definitively track osseointegration. The results from our study revealed panels comprising of matrix metalloproteinases and collagen genes were able to track osseointegration on implant surfaces (MMP9 and COL1A2 on micro-textured; MMP12 and COL6A3 on superimposed nano-textured surfaces) with 100% classification accuracy, specificity and sensitivity. Further, our analysis showed the importance of the progression of the duration in establishment of the mechanical connection at bone-implant surface. The findings from this study are expected to be useful to researchers investigating osseointegration of novel implant materials especially at the early stage. The methodology demonstrated can be easily adapted by scientists in different fields to analyze large databases for other biological processes.

show abstract

A Comparative Study of Microarray Data Analysis for Cancer Classification

Cited by 8 publications

References 9 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

Examining applying high performance genetic data feature selection and classification algorithms for colon cancer diagnosis

A reductionist approach to extract robust molecular markers from microarray data series – Isolating markers to track osseointegration

Contact Info

Product

Resources

About