An Efficient Mixed-Model for Screening Differentially Expressed Genes of Breast Cancer Based on LR-RF

Sun, Mengmeng; Ding, Tao; Yu, Keming

doi:10.1109/tcbb.2018.2829519

Cited by 12 publications

(4 citation statements)

References 29 publications

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“…ese samples are compared to stored samples in order to predict cancer considering features extracted from the present dataset using a convolution neural network mechanism [27]. e size of the dataset is taken along with the comparison time.…”

Section: Resultsmentioning

confidence: 99%

“…e authors have also used random forest algorithm for breast cancer diagnosis [12], genetic algorithm-based ensemble approach [22], and Bayesian logistic regression [26] for breast cancer prediction. Breast mass classification and its diagnosis have been made using mammograms using ensemble convolution neural networks [27]. Some researchers are performing the extraction of features in an unsupervised fashion with the help of deep learning [29].…”

Section: Problem Statement Existing Researches Made Use Of Breast Cancer Categorization Of Graphical Content With Help Of Cnnmentioning

confidence: 99%

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Edge-Based Convolutional Neural Network for Improving Breast Cancer Prediction Performance

Madhu

Kumar

2021

Mathematical Problems in Engineering

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There are many research studies in the field of breast cancer prediction, but it has been observed that the time taken for prediction needs to be reduced. The problem in the existing research is space consumption by graphical content. The proposed research is supposed to minimize the prediction time and space consumption. In this paper, research has focused on the study of existing breast cancer research and techniques and eliminating their limitation. It has been observed that when the number of datasets increases, every comparison makes a huge gap in size and comparison time. This research proposes a methodology for breast cancer prediction using an edge-based CNN (convolutional neural network) algorithm. The elimination of useless content from the graphical image before applying CNN has reduced the time consumption along with space consumption. The edge detection mechanism would retail only edges from the image sample in order to detect the pattern to predict breast cancer. The proposed work is supposed to implement the proposed methodology. A comparison of the proposed methodology and algorithm with the existing algorithm is made during simulation. The proposed work is found to be more efficient compared to the existing techniques used in breast cancer prediction. The utilization of proposed in the work area of medical science is supposed to enhance the capability in case of CNN at the time of decision-making. The proposed work is supposed to be more accurate compared to the existing works. It has been observed that the proposed work is fourteen to fifteen percent more accurate. It is taking 9/4 times less space and 1.0849004/0.178971 times less time compared to the general CNN model. Accuracy might vary as per size of the image and alteration performed in dataset of the image.

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Section: Resultsmentioning

confidence: 99%

Section: Problem Statement Existing Researches Made Use Of Breast Cancer Categorization Of Graphical Content With Help Of Cnnmentioning

confidence: 99%

Edge-Based Convolutional Neural Network for Improving Breast Cancer Prediction Performance

Madhu

Kumar

2021

Mathematical Problems in Engineering

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“…Two sets of breast cancer datasets were downloaded from the gene expression omnibus (GEO) [19], [20] for this study. GSE45255 and GSE15852 are the accession numbers, and the chip platform is GP96.…”

Section: Procedures 31 Microarray Breast Cancer Datasetmentioning

confidence: 99%

A case study of microarray breast cancer classification using machine learning algorithms with grid search cross validation

2023

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Breast cancer is one of the leading causes of death and most frequently diagnosed cancer amongst women. Annually, almost half a million women do not survive the disease and die from breast cancer. Machine learning is a subfield of artificial intelligence (AI) and computer science that uses data and algorithms to mimic how humans learn, and gradually improving its accuracy. In this work, simple machine learning methods are used to classify breast cancer microarray data to normal and relapse. The data is from the gene expression omnibus (GEO) website namely GSE45255 and GSE15852. These two datasets are integrated and combined to form a single dataset. The study involved three machine learning algorithms, random forest (RF), extra tree (ET), and support vector machine (SVM). Grid search cross validation (CV) is applied for hyperparameter tuning of the algorithms. The result shows that the tuned SVM is best among the tested algorithms with accuracy of 97.78%. In the future it is recommended to include feature selection method to get the optimal features and better classification accuracies.

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“…Secondly, using Granger causality test, the undirected feature gene interaction network was transformed to directed network. A stepwise character selection based on Random Forests (RF) model [23] was further proposed to identify predictors from feature gens. In the last step, we tested the prediction capacity of the predictors, by applying to six independent datasets; the results presented excellent accuracy.…”

Section: Introductionmentioning

confidence: 99%

Extracting predictors for lung adenocarcinoma based on Granger causality test and stepwise character selection

Fan

Wang

2019

BMC Bioinformatics

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Background Lung adenocarcinoma is the most common type of lung cancer, with high mortality worldwide. Its occurrence and development were thoroughly studied by high-throughput expression microarray, which produced abundant data on gene expression, DNA methylation, and miRNA quantification. However, the hub genes, which can be served as bio-markers for discriminating cancer and healthy individuals, are not well screened. Result Here we present a new method for extracting gene predictors, aiming to obtain the least predictors without losing the efficiency. We firstly analyzed three different expression microarrays and constructed multi-interaction network, since the individual expression dataset is not enough for describing biological behaviors dynamically and systematically. Then, we transformed the undirected interaction network to directed network by employing Granger causality test, followed by the predictors screened with the use of the stepwise character selection algorithm. Six predictors, including TOP2A, GRK5, SIRT7, MCM7, EGFR , and COL1A2 , were ultimately identified. All the predictors are the cancer-related, and the number is very small fascinating diagnosis. Finally, the validation of this approach was verified by robustness analyses applied to six independent datasets; the precision is up to 95.3% ∼ 100%. Conclusion Although there are complicated differences between cancer and normal cells in gene functions, cancer cells could be differentiated in case that a group of special genes expresses abnormally. Here we presented a new, robust, and effective method for extracting gene predictors. We identified as low as 6 genes which can be taken as predictors for diagnosing lung adenocarcinoma.

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An Efficient Mixed-Model for Screening Differentially Expressed Genes of Breast Cancer Based on LR-RF

Cited by 12 publications

References 29 publications

Edge-Based Convolutional Neural Network for Improving Breast Cancer Prediction Performance

Edge-Based Convolutional Neural Network for Improving Breast Cancer Prediction Performance

A case study of microarray breast cancer classification using machine learning algorithms with grid search cross validation

Extracting predictors for lung adenocarcinoma based on Granger causality test and stepwise character selection

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