An immune-inspired semi-supervised algorithm for breast cancer diagnosis
•We investigate an immune-inspired semi-supervised learning algorithm to reduce the need for labeled data.•Experimental results prove that our algorithm is a promising automatic diagnosis method for breast cancer.•The proposed algorithm has clonal selection, non-linear, and such excellent immune cha...
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| Published in: | Computer methods and programs in biomedicine Vol. 134; pp. 259 - 265 |
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| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier Ireland Ltd
01.10.2016
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| ISSN: | 0169-2607, 1872-7565, 1872-7565 |
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| Abstract | •We investigate an immune-inspired semi-supervised learning algorithm to reduce the need for labeled data.•Experimental results prove that our algorithm is a promising automatic diagnosis method for breast cancer.•The proposed algorithm has clonal selection, non-linear, and such excellent immune characteristics.
Breast cancer is the most frequently and world widely diagnosed life-threatening cancer, which is the leading cause of cancer death among women. Early accurate diagnosis can be a big plus in treating breast cancer. Researchers have approached this problem using various data mining and machine learning techniques such as support vector machine, artificial neural network, etc. The computer immunology is also an intelligent method inspired by biological immune system, which has been successfully applied in pattern recognition, combination optimization, machine learning, etc. However, most of these diagnosis methods belong to a supervised diagnosis method. It is very expensive to obtain labeled data in biology and medicine. In this paper, we seamlessly integrate the state-of-the-art research on life science with artificial intelligence, and propose a semi-supervised learning algorithm to reduce the need for labeled data. We use two well-known benchmark breast cancer datasets in our study, which are acquired from the UCI machine learning repository. Extensive experiments are conducted and evaluated on those two datasets. Our experimental results demonstrate the effectiveness and efficiency of our proposed algorithm, which proves that our algorithm is a promising automatic diagnosis method for breast cancer. |
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| AbstractList | •We investigate an immune-inspired semi-supervised learning algorithm to reduce the need for labeled data.•Experimental results prove that our algorithm is a promising automatic diagnosis method for breast cancer.•The proposed algorithm has clonal selection, non-linear, and such excellent immune characteristics.
Breast cancer is the most frequently and world widely diagnosed life-threatening cancer, which is the leading cause of cancer death among women. Early accurate diagnosis can be a big plus in treating breast cancer. Researchers have approached this problem using various data mining and machine learning techniques such as support vector machine, artificial neural network, etc. The computer immunology is also an intelligent method inspired by biological immune system, which has been successfully applied in pattern recognition, combination optimization, machine learning, etc. However, most of these diagnosis methods belong to a supervised diagnosis method. It is very expensive to obtain labeled data in biology and medicine. In this paper, we seamlessly integrate the state-of-the-art research on life science with artificial intelligence, and propose a semi-supervised learning algorithm to reduce the need for labeled data. We use two well-known benchmark breast cancer datasets in our study, which are acquired from the UCI machine learning repository. Extensive experiments are conducted and evaluated on those two datasets. Our experimental results demonstrate the effectiveness and efficiency of our proposed algorithm, which proves that our algorithm is a promising automatic diagnosis method for breast cancer. Breast cancer is the most frequently and world widely diagnosed life-threatening cancer, which is the leading cause of cancer death among women. Early accurate diagnosis can be a big plus in treating breast cancer. Researchers have approached this problem using various data mining and machine learning techniques such as support vector machine, artificial neural network, etc. The computer immunology is also an intelligent method inspired by biological immune system, which has been successfully applied in pattern recognition, combination optimization, machine learning, etc. However, most of these diagnosis methods belong to a supervised diagnosis method. It is very expensive to obtain labeled data in biology and medicine. In this paper, we seamlessly integrate the state-of-the-art research on life science with artificial intelligence, and propose a semi-supervised learning algorithm to reduce the need for labeled data. We use two well-known benchmark breast cancer datasets in our study, which are acquired from the UCI machine learning repository. Extensive experiments are conducted and evaluated on those two datasets. Our experimental results demonstrate the effectiveness and efficiency of our proposed algorithm, which proves that our algorithm is a promising automatic diagnosis method for breast cancer.Breast cancer is the most frequently and world widely diagnosed life-threatening cancer, which is the leading cause of cancer death among women. Early accurate diagnosis can be a big plus in treating breast cancer. Researchers have approached this problem using various data mining and machine learning techniques such as support vector machine, artificial neural network, etc. The computer immunology is also an intelligent method inspired by biological immune system, which has been successfully applied in pattern recognition, combination optimization, machine learning, etc. However, most of these diagnosis methods belong to a supervised diagnosis method. It is very expensive to obtain labeled data in biology and medicine. In this paper, we seamlessly integrate the state-of-the-art research on life science with artificial intelligence, and propose a semi-supervised learning algorithm to reduce the need for labeled data. We use two well-known benchmark breast cancer datasets in our study, which are acquired from the UCI machine learning repository. Extensive experiments are conducted and evaluated on those two datasets. Our experimental results demonstrate the effectiveness and efficiency of our proposed algorithm, which proves that our algorithm is a promising automatic diagnosis method for breast cancer. Breast cancer is the most frequently and world widely diagnosed life-threatening cancer, which is the leading cause of cancer death among women. Early accurate diagnosis can be a big plus in treating breast cancer. Researchers have approached this problem using various data mining and machine learning techniques such as support vector machine, artificial neural network, etc. The computer immunology is also an intelligent method inspired by biological immune system, which has been successfully applied in pattern recognition, combination optimization, machine learning, etc. However, most of these diagnosis methods belong to a supervised diagnosis method. It is very expensive to obtain labeled data in biology and medicine. In this paper, we seamlessly integrate the state-of-the-art research on life science with artificial intelligence, and propose a semi-supervised learning algorithm to reduce the need for labeled data. We use two well-known benchmark breast cancer datasets in our study, which are acquired from the UCI machine learning repository. Extensive experiments are conducted and evaluated on those two datasets. Our experimental results demonstrate the effectiveness and efficiency of our proposed algorithm, which proves that our algorithm is a promising automatic diagnosis method for breast cancer. Highlights • In this paper, we seamlessly integrate the state-of-the-art in life science and artificial intelligence, and investigate a semi-supervised learning algorithm to reduce the need for labeled data. In the proposed algorithm, the Kent chaotic helps to search the best solution in the whole antibody cells feature vector space. Considering that the value of k is sensitive to the experiment results, we use the weighted k nearest neighbor algorithm to diagnose the breast cancer. • We used two well-known benchmark breast cancer datasets in our study, which were acquired from the UCI machine learning repository. Extensive experiments are conducted and evaluated on those two datasets demonstrating the effectiveness and efficiency of our proposed algorithm, which proves that our algorithm is a promising automatic diagnosis method for breast cancer. • The proposed algorithm has clonal selection, non-linear, immunological memory attractive property, and such excellent immune characteristics. The algorithm is also general one, which can be used not only in the research of the medical automatic diagnosis, but also in other related research areas, such as pattern recognition, optimization, etc. |
| Author | Zhang, Jiandong Chen, Wenbin Yang, Jin Li, Fufang Zhou, Wubai Peng, Lingxi |
| Author_xml | – sequence: 1 givenname: Lingxi surname: Peng fullname: Peng, Lingxi organization: School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou, 510006, China – sequence: 2 givenname: Wenbin surname: Chen fullname: Chen, Wenbin organization: School of Computer Science and Education, Guangzhou University, Guangzhou, 510006, China – sequence: 3 givenname: Wubai surname: Zhou fullname: Zhou, Wubai organization: School of Computing & Information Sciences, Florida International University, Miami, FL 33199, USA – sequence: 4 givenname: Fufang surname: Li fullname: Li, Fufang organization: School of Computer Science and Education, Guangzhou University, Guangzhou, 510006, China – sequence: 5 givenname: Jin surname: Yang fullname: Yang, Jin organization: Department of Computer Science, Leshan Normal Univ., Leshan 614000, China – sequence: 6 givenname: Jiandong surname: Zhang fullname: Zhang, Jiandong email: zjd2003@163.com, flyingday@139.com organization: Department of Computer Science, Leshan Normal Univ., Leshan 614000, China |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/27480748$$D View this record in MEDLINE/PubMed |
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| Snippet | •We investigate an immune-inspired semi-supervised learning algorithm to reduce the need for labeled data.•Experimental results prove that our algorithm is a... Highlights • In this paper, we seamlessly integrate the state-of-the-art in life science and artificial intelligence, and investigate a semi-supervised... Breast cancer is the most frequently and world widely diagnosed life-threatening cancer, which is the leading cause of cancer death among women. Early accurate... |
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| SubjectTerms | Algorithms Artificial immune Breast cancer diagnosis Breast Neoplasms - diagnosis Breast Neoplasms - immunology Female Humans Internal Medicine Machine learning Other |
| Title | An immune-inspired semi-supervised algorithm for breast cancer diagnosis |
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