An autoencoder learning method for predicting breast cancer subtypes.
Heterogeneity of breast cancer poses several challenges for detection and treatment. With next-generation sequencing, we can now map the transcriptional profile of each patient's breast tissue, which has the potential for identifying and characterizing cancer subtypes. However, the large dimens...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0327773 |
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| _version_ | 1850063221457682432 |
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| author | Zahra Rostami Kavitha Mukund Maryam Masnadi-Shirazi Shankar Subramaniam |
| author_facet | Zahra Rostami Kavitha Mukund Maryam Masnadi-Shirazi Shankar Subramaniam |
| author_sort | Zahra Rostami |
| collection | DOAJ |
| description | Heterogeneity of breast cancer poses several challenges for detection and treatment. With next-generation sequencing, we can now map the transcriptional profile of each patient's breast tissue, which has the potential for identifying and characterizing cancer subtypes. However, the large dimensionality of this transcriptomic data and the heterogeneity between the molecular profiles of breast cancers poses a barrier to identifying minimal markers and mechanistic consequences. In this study, we develop an autoencoder to identify a reduced set of gene markers that characterize the four major breast cancer subtypes with the accuracy of 82.38%. The reduced feature space created by our model captures the functional characteristics of each breast cancer subtype highlighting mechanisms that are unique to each subtype as well as those that are shared. Our high prediction accuracy shows that our markers can be valuable for breast cancer subtype detection and have the potential to provide insights into mechanisms associated with each subtype. |
| format | Article |
| id | doaj-art-20af67b459254bc987dc77a618559c8e |
| institution | DOAJ |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-20af67b459254bc987dc77a618559c8e2025-08-20T02:49:43ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01207e032777310.1371/journal.pone.0327773An autoencoder learning method for predicting breast cancer subtypes.Zahra RostamiKavitha MukundMaryam Masnadi-ShiraziShankar SubramaniamHeterogeneity of breast cancer poses several challenges for detection and treatment. With next-generation sequencing, we can now map the transcriptional profile of each patient's breast tissue, which has the potential for identifying and characterizing cancer subtypes. However, the large dimensionality of this transcriptomic data and the heterogeneity between the molecular profiles of breast cancers poses a barrier to identifying minimal markers and mechanistic consequences. In this study, we develop an autoencoder to identify a reduced set of gene markers that characterize the four major breast cancer subtypes with the accuracy of 82.38%. The reduced feature space created by our model captures the functional characteristics of each breast cancer subtype highlighting mechanisms that are unique to each subtype as well as those that are shared. Our high prediction accuracy shows that our markers can be valuable for breast cancer subtype detection and have the potential to provide insights into mechanisms associated with each subtype.https://doi.org/10.1371/journal.pone.0327773 |
| spellingShingle | Zahra Rostami Kavitha Mukund Maryam Masnadi-Shirazi Shankar Subramaniam An autoencoder learning method for predicting breast cancer subtypes. PLoS ONE |
| title | An autoencoder learning method for predicting breast cancer subtypes. |
| title_full | An autoencoder learning method for predicting breast cancer subtypes. |
| title_fullStr | An autoencoder learning method for predicting breast cancer subtypes. |
| title_full_unstemmed | An autoencoder learning method for predicting breast cancer subtypes. |
| title_short | An autoencoder learning method for predicting breast cancer subtypes. |
| title_sort | autoencoder learning method for predicting breast cancer subtypes |
| url | https://doi.org/10.1371/journal.pone.0327773 |
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