Computational Modeling and Analysis of Microarray Data: New Horizons

Ruskin, Heather J.

doi:10.3390/microarrays5040026

Cited by 6 publications

(3 citation statements)

References 8 publications

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“…A continuous mutation in the normal cell causes damage to the deoxyribonucleic acid (DNA). The impairment of cell replication is also one of the main causes of forming malignant tumor cells [13]. Microarray is one of the breakthroughs in experimental molecular biology that allows us to monitor the expression levels of tens of thousands of genes simultaneously.…”

Section: Introductionmentioning

confidence: 99%

A weighted ensemble-based active learning model to label microarray data

Chakraborty

Chatterjee

et al. 2020

Med Biol Eng Comput

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Classification of cancerous genes from microarray data is an important research area in bioinformatics. Large amount of microarray data are available, but it is very costly to label them. This paper proposes an active learning model, a semisupervised classification approach, to label the microarray data using which predictions can be made even with lesser amount of labeled data. Initially, a pool of unlabeled instances is given from which some instances are randomly chosen for labeling. Successive selection of instances to be labeled from unlabeled pool is determined by selection algorithms. The proposed method is devised following an ensemble approach to combine the decisions of three classifiers in order to arrive at a consensus which provides a more accurate prediction of the class label to ensure that each individual classifier learns in an uncorrelated manner. Our method combines the heuristic techniques used by an active learning algorithm to choose training samples with the multiple learning paradigm attained by an ensemble to optimize the search space by choosing efficiently from an already sparse learning pool. On evaluating the proposed method on 10 microarray datasets, we achieve performance which is comparable with state-ofthe-art methods. The code and datasets are given at https://github.com/anuran-Chakraborty/Active-learning.

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Section: Introductionmentioning

confidence: 99%

A weighted ensemble-based active learning model to label microarray data

Chakraborty

Chatterjee

et al. 2020

Med Biol Eng Comput

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“…Nowadays, various kinds of machine learning and statistical approaches are used to classify tumour cells accurate such as support vector machines [15], k-nearest neighbor [30], and neural network techniques [24]. Also, several researchers thus hybridize the classification techniques with optimization algorithms for further enhancement of accuracy [1][22].…”

Section: Introductionmentioning

confidence: 99%

Gene Microarray Cancer Classification using Correlation Based Feature Selection Algorithm and Rules Classifiers

Al-Batah¹,

Zaqaibeh²,

Alomari³

et al. 2019

Int. J. Onl. Eng.

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Gene microarray classification problems are considered a challenge task since the datasets contain few number of samples with high number of genes (features). The genes subset selection in microarray data play an important role for minimizing the computational load and solving classification problems. In this paper, the Correlation-based Feature Selection (CFS) algorithm is utilized in the feature selection process to reduce the dimensionality of data and finding a set of discriminatory genes. Then, the Decision Table, JRip, and OneR are employed for classification process. The proposed approach of gene selection and classification is tested on 11 microarray datasets and the performances of the filtered datasets are compared with the original datasets. The experimental results showed that CFS can effectively screen irrelevant, redundant, and noisy features. In addition, the results for all datasets proved that the proposed approach with a small number of genes can achieve high prediction accuracy and fast computational speed. Considering the average accuracy for all the analysis of microarray data, the JRip achieved the best result as compared to Decision Table, and OneR classifier. The proposed approach has a remarkable impact on the classification accuracy especially when the data is complicated with multiple classes and high number of genes.

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“…Microarray is a multiplex technology used in molecular biology and medicine that enables biologists to monitor expression levels of thousands of genes [1]. Many microarray experiments have been designed to investigate the genetic mechanisms of cancer [2] and to discover new drug designs in the pharmaceutical industry [3].…”

Section: Introductionmentioning

confidence: 99%

Gene Selection for Microarray Cancer Data Classification by a Novel Rule-Based Algorithm

Angulo

2018

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Due to the disproportionate difference between the number of genes and samples, microarray data analysis is considered an extremely difficult task in sample classification. Feature selection mitigates this problem by removing irrelevant and redundant genes from data. In this paper, we propose a new methodology for feature selection that aims to detect relevant, non-redundant and interacting genes by analysing the feature value space instead of the feature space. Following this methodology, we also propose a new feature selection algorithm, namely Pavicd (Probabilistic Attribute-Value for Class Distinction). Experiments in fourteen microarray cancer datasets reveal that Pavicd obtains the best performance in terms of running time and classification accuracy when using Ripper-k and C4.5 as classifiers. When using SVM (Support Vector Machine), the Gbc (Genetic Bee Colony) wrapper algorithm gets the best results. However, Pavicd is significantly faster.

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Computational Modeling and Analysis of Microarray Data: New Horizons

Cited by 6 publications

References 8 publications

A weighted ensemble-based active learning model to label microarray data

A weighted ensemble-based active learning model to label microarray data

Gene Microarray Cancer Classification using Correlation Based Feature Selection Algorithm and Rules Classifiers

Gene Selection for Microarray Cancer Data Classification by a Novel Rule-Based Algorithm

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