Tyck till om SwePub Sök
här!
Sökning: AMNE:(MEDICAL AND HEALTH SCIENCES Clinical Medicine Cancer and Oncology)
> (2020) >
Domain expertise–ag...
Domain expertise–agnostic feature selection for the analysis of breast cancer data*
-
- Pozzoli, Susanna (författare)
- KTH,Programvaruteknik och datorsystem, SCS,KTH Royal Institute of Technology, Sweden; Politecnico di Milano, Italy
-
- Soliman, Amira (författare)
- RISE,RISE SICS
-
- Bahri, Leila (författare)
- KTH,Programvaruteknik och datorsystem, SCS,KTH Royal Institute of Technology, Sweden
-
visa fler...
-
- Branca, Rui (författare)
- Karolinska Institutet
-
- Girdzijauskas, Sarunas (författare)
- KTH,RISE,Datavetenskap,KTH Royal Institute of Technology, Sweden,Programvaruteknik och datorsystem, SCS
-
- Brambilla, Marco (författare)
- Politecnico di Milano, Italy
-
visa färre...
-
(creator_code:org_t)
- Elsevier B.V. 2020
- 2020
- Engelska.
-
Ingår i: Artificial Intelligence in Medicine. - : Elsevier B.V.. - 0933-3657 .- 1873-2860. ; 108
- Relaterad länk:
-
https://zenodo.org/r...
-
visa fler...
-
https://urn.kb.se/re...
-
https://doi.org/10.1...
-
https://urn.kb.se/re...
-
http://kipublication...
-
visa färre...
Abstract
Ämnesord
Stäng
- Progress in proteomics has enabled biologists to accurately measure the amount of protein in a tumor. This work is based on a breast cancer data set, result of the proteomics analysis of a cohort of tumors carried out at Karolinska Institutet. While evidence suggests that an anomaly in the protein content is related to the cancerous nature of tumors, the proteins that could be markers of cancer types and subtypes and the underlying interactions are not completely known. This work sheds light on the potential of the application of unsupervised learning in the analysis of the aforementioned data sets, namely in the detection of distinctive proteins for the identification of the cancer subtypes, in the absence of domain expertise. In the analyzed data set, the number of samples, or tumors, is significantly lower than the number of features, or proteins; consequently, the input data can be thought of as high-dimensional data. The use of high-dimensional data has already become widespread, and a great deal of effort has been put into high-dimensional data analysis by means of feature selection, but it is still largely based on prior specialist knowledge, which in this case is not complete. There is a growing need for unsupervised feature selection, which raises the issue of how to generate promising subsets of features among all the possible combinations, as well as how to evaluate the quality of these subsets in the absence of specialist knowledge. We hereby propose a new wrapper method for the generation and evaluation of subsets of features via spectral clustering and modularity, respectively. We conduct experiments to test the effectiveness of the new method in the analysis of the breast cancer data, in a domain expertise–agnostic context. Furthermore, we show that we can successfully augment our method by incorporating an external source of data on known protein complexes. Our approach reveals a large number of subsets of features that are better at clustering the samples than the state-of-the-art classification in terms of modularity and shows a potential to be useful for future proteomics research.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences (hsv//eng)
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Cancer och onkologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Cancer and Oncology (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Medicinteknik -- Annan medicinteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Medical Engineering -- Other Medical Engineering (hsv//eng)
Nyckelord
- Breast cancer
- Clustering
- Clustering performance evaluation
- Dimensionality reduction
- Feature selection
- Proteomics
- Unsupervised learning
- Clustering algorithms
- Diseases
- Feature extraction
- Proteins
- Set theory
- Tumors
- Breast cancer data
- High dimensional data
- High-dimensional data analysis
- Number of samples
- Protein complexes
- Proteomics research
- Spectral clustering
- Unsupervised feature selection
- Quality control
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
Hitta via bibliotek
Till lärosätets databas