8 Efficient gene selection for breast cancer classification using Brownian Motion Search Algorithm and Support Vector Machine
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Abrar Yaqoob
Abstract
Gene expression datasets provide extensive information about various biological processes, but identifying important genes in high-dimensional data is challenging due to redundancy and irrelevant genes. To overcome this challenge, numerous feature selection (FS) techniques have been developed to identify significant genes amidst complex biological data. This research introduces a novel approach that combines the Brownian Motion Search Algorithm (BMSA) with the Support Vector Machine (SVM) for gene selection in breast cancer classification. Using the breast cancer dataset, our method efficiently identifies relevant gene subsets using BMSA and uses SVM for precise classification. The BMSA navigates the high-dimensional feature space to select relevant genes by simulating random motion, reducing redundancy and irrelevant genes. SVM evaluates these gene subsets for accurate classification. We assess the performance of the algorithm using diverse metrics, including the confusion matrix for accuracy and error distribution, the precision-recall curve for precision-recall balance, and the ROC curve for diagnostic ability. The findings demonstrate the effectiveness of our proposed approach in achieving high classification accuracy. Specifically, the method achieves a best classification accuracy of 99.14 % on 16 genes, along with notable mean and worst-case performances. These results highlight the potential of the BMSA-SVM approach to provide accurate classifications and valuable insights into breast cancer-associated gene biomarkers, representing a significant advancement in bioinformatics and cancer research.
Abstract
Gene expression datasets provide extensive information about various biological processes, but identifying important genes in high-dimensional data is challenging due to redundancy and irrelevant genes. To overcome this challenge, numerous feature selection (FS) techniques have been developed to identify significant genes amidst complex biological data. This research introduces a novel approach that combines the Brownian Motion Search Algorithm (BMSA) with the Support Vector Machine (SVM) for gene selection in breast cancer classification. Using the breast cancer dataset, our method efficiently identifies relevant gene subsets using BMSA and uses SVM for precise classification. The BMSA navigates the high-dimensional feature space to select relevant genes by simulating random motion, reducing redundancy and irrelevant genes. SVM evaluates these gene subsets for accurate classification. We assess the performance of the algorithm using diverse metrics, including the confusion matrix for accuracy and error distribution, the precision-recall curve for precision-recall balance, and the ROC curve for diagnostic ability. The findings demonstrate the effectiveness of our proposed approach in achieving high classification accuracy. Specifically, the method achieves a best classification accuracy of 99.14 % on 16 genes, along with notable mean and worst-case performances. These results highlight the potential of the BMSA-SVM approach to provide accurate classifications and valuable insights into breast cancer-associated gene biomarkers, representing a significant advancement in bioinformatics and cancer research.
Chapters in this book
- Frontmatter I
- Contents V
- List of Contributing Authors VII
- 1 Introduction: fundamentals of drug discovery, telemedicine, artificial intelligence, computer vision, and IoT 1
- 2 Machine learning transformations in drug discovery: a paradigm shift in development strategies 11
- 3 Explainable AI approaches in drug classification from biomarkers of epileptic seizure 27
- 4 Harnessing predictive analytics and machine learning in personalized medicine: patient outcomes and public health strategies 41
- 5 A data-driven framework for future healthcare diagnosis through predictive analytics 59
- 6 Revolutionizing home healthcare: telemedicine, predictive analytics, and AI-driven drug discovery 71
- 7 AI-driven insights: a machine learning approach to lung cancer diagnosis 91
- 8 Efficient gene selection for breast cancer classification using Brownian Motion Search Algorithm and Support Vector Machine 109
- 9 A hybrid feature gene selection approach by integrating variance filter, extremely randomized tree, and Cuckoo Search algorithm for cancer classification 127
- 10 HySleep_Net: a hybrid deep learning model for automatic sleep stage detection from polysomnographic signals 151
- 11 Ambulance booking and tracking website 183
- 12 Entropy based emergency rescue location selection with uncertain travel time 207
- 13 Performance comparison of different deep learning ensemble models for sentiment classification of movie reviews 225
- 14 Elevating standards in homoeopathic medicine: chemometric standardization of medicinal plant for quality assurance 253
- 15 Evaluation of genetic diversity in Rauvolfia species using Random Amplification of Polymorphic DNA (RAPD) technique 259
- Index
Chapters in this book
- Frontmatter I
- Contents V
- List of Contributing Authors VII
- 1 Introduction: fundamentals of drug discovery, telemedicine, artificial intelligence, computer vision, and IoT 1
- 2 Machine learning transformations in drug discovery: a paradigm shift in development strategies 11
- 3 Explainable AI approaches in drug classification from biomarkers of epileptic seizure 27
- 4 Harnessing predictive analytics and machine learning in personalized medicine: patient outcomes and public health strategies 41
- 5 A data-driven framework for future healthcare diagnosis through predictive analytics 59
- 6 Revolutionizing home healthcare: telemedicine, predictive analytics, and AI-driven drug discovery 71
- 7 AI-driven insights: a machine learning approach to lung cancer diagnosis 91
- 8 Efficient gene selection for breast cancer classification using Brownian Motion Search Algorithm and Support Vector Machine 109
- 9 A hybrid feature gene selection approach by integrating variance filter, extremely randomized tree, and Cuckoo Search algorithm for cancer classification 127
- 10 HySleep_Net: a hybrid deep learning model for automatic sleep stage detection from polysomnographic signals 151
- 11 Ambulance booking and tracking website 183
- 12 Entropy based emergency rescue location selection with uncertain travel time 207
- 13 Performance comparison of different deep learning ensemble models for sentiment classification of movie reviews 225
- 14 Elevating standards in homoeopathic medicine: chemometric standardization of medicinal plant for quality assurance 253
- 15 Evaluation of genetic diversity in Rauvolfia species using Random Amplification of Polymorphic DNA (RAPD) technique 259
- Index
