Selvaraj, Muthu Krishnan and Kaur, Jasmeet (2023) Computational method for aromatase-related proteins using machine learning approach. PLoS One, 18 (3). e0283567.
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Human aromatase enzyme is a microsomal cytochrome P450 and catalyzes aromatization of androgens into estrogens during steroidogenesis. For breast cancer therapy, third-generation aromatase inhibitors (AIs) have proven to be effective; however patients acquire resistance to current AIs. Thus there is a need to predict aromatase-related proteins to develop efficacious AIs. A machine learning method was established to identify aromatase-related proteins using a five-fold cross validation technique. In this study, different SVM approach-based models were built using the following approaches like amino acid, dipeptide composition, hybrid and evolutionary profiles in the form of position-specific scoring matrix (PSSM); with maximum accuracy of 87.42%, 84.05%, 85.12%, and 92.02% respectively. Based on the primary sequence, the developed method is highly accurate to predict the aromatase-related proteins. Prediction scores graphs were developed using the known dataset to check the performance of the method. Based on the approach described above, a webserver for predicting aromatase-related proteins from primary sequence data was developed and implemented at https://bioinfo.imtech.res.in/servers/muthu/aromatase/home.html. We hope that the developed method will be useful for aromatase protein related research.
| Item Type: | Article |
|---|---|
| Additional Information: | copyright of this artice belongs to Public Library of Science (PLoS) |
| Subjects: | Q Science > QR Microbiology |
| Depositing User: | Dr. K.P.S.Sengar |
| Date Deposited: | 25 Mar 2026 04:50 |
| Last Modified: | 25 Mar 2026 04:50 |
| URI: | http://crdd.osdd.net/open/id/eprint/3387 |
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