Cancer subtype identification pipeline: a classifusion approach
Classification of cancer patients into treatment groups is essential for appropriate diagnosis to increase survival. Previously, a series of papers, largely published in the breast cancer domain have leveraged Computational Intelligence (CI) developments and tools, resulting in ground breaking advan...
| Main Authors: | , , |
|---|---|
| Format: | Conference or Workshop Item |
| Published: |
2016
|
| Subjects: | |
| Online Access: | https://eprints.nottingham.ac.uk/39199/ |
| _version_ | 1848795786038476800 |
|---|---|
| author | Agrawal, Utkarsh Soria, Daniele Wagner, Christian |
| author_facet | Agrawal, Utkarsh Soria, Daniele Wagner, Christian |
| author_sort | Agrawal, Utkarsh |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Classification of cancer patients into treatment groups is essential for appropriate diagnosis to increase survival. Previously, a series of papers, largely published in the breast cancer domain have leveraged Computational Intelligence (CI) developments and tools, resulting in ground breaking advances such as the classification of cancer into newly identified classes - leading to improved treatment options. However, the current literature on the use of CI to achieve this is fragmented, making further advances challenging. This paper captures developments in this area so far, with the goal to establish a clear, step-by-step pipeline for cancer subtype identification. Based on establishing the pipeline, the paper identifies key potential advances in CI at the individual steps, thus establishing a roadmap for future research. As such, it is the aim of the paper to engage the CI community to address the research challenges and leverage the strong potential of CI in this important area. Finally, we present a small set of recent findings on the Nottingham Tenovus Primary Breast Carcinoma Series enabling the classification of a higher number of patients into one of the identified breast cancer groups, and introduce Classifusion: a combination of results of multiple classifiers. |
| first_indexed | 2025-11-14T19:37:37Z |
| format | Conference or Workshop Item |
| id | nottingham-39199 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:37:37Z |
| publishDate | 2016 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-391992020-05-04T18:20:21Z https://eprints.nottingham.ac.uk/39199/ Cancer subtype identification pipeline: a classifusion approach Agrawal, Utkarsh Soria, Daniele Wagner, Christian Classification of cancer patients into treatment groups is essential for appropriate diagnosis to increase survival. Previously, a series of papers, largely published in the breast cancer domain have leveraged Computational Intelligence (CI) developments and tools, resulting in ground breaking advances such as the classification of cancer into newly identified classes - leading to improved treatment options. However, the current literature on the use of CI to achieve this is fragmented, making further advances challenging. This paper captures developments in this area so far, with the goal to establish a clear, step-by-step pipeline for cancer subtype identification. Based on establishing the pipeline, the paper identifies key potential advances in CI at the individual steps, thus establishing a roadmap for future research. As such, it is the aim of the paper to engage the CI community to address the research challenges and leverage the strong potential of CI in this important area. Finally, we present a small set of recent findings on the Nottingham Tenovus Primary Breast Carcinoma Series enabling the classification of a higher number of patients into one of the identified breast cancer groups, and introduce Classifusion: a combination of results of multiple classifiers. 2016-11-21 Conference or Workshop Item PeerReviewed Agrawal, Utkarsh, Soria, Daniele and Wagner, Christian (2016) Cancer subtype identification pipeline: a classifusion approach. In: 2016 IEEE Congress on Evolutionary Computation (CEC), 24-29 July 2016, Vancouver, Canada. Breast Cancer; Classification methods; Consensus Classification (Classifusion); Consensus Clustering http://ieeexplore.ieee.org/document/7744150/ |
| spellingShingle | Breast Cancer; Classification methods; Consensus Classification (Classifusion); Consensus Clustering Agrawal, Utkarsh Soria, Daniele Wagner, Christian Cancer subtype identification pipeline: a classifusion approach |
| title | Cancer subtype identification pipeline: a classifusion approach |
| title_full | Cancer subtype identification pipeline: a classifusion approach |
| title_fullStr | Cancer subtype identification pipeline: a classifusion approach |
| title_full_unstemmed | Cancer subtype identification pipeline: a classifusion approach |
| title_short | Cancer subtype identification pipeline: a classifusion approach |
| title_sort | cancer subtype identification pipeline: a classifusion approach |
| topic | Breast Cancer; Classification methods; Consensus Classification (Classifusion); Consensus Clustering |
| url | https://eprints.nottingham.ac.uk/39199/ https://eprints.nottingham.ac.uk/39199/ |