Correction to: Moonlighting protein prediction using physico‑chemical and evolutional properties via machine learning methods

Shirafkan, Farshid; Gharaghani, Sajjad; Rahimian, Karim; Sajedi, Reza H.; Zahiri, Javad

doi:10.1186/s12859-021-04257-7

Cited by 2 publications

(1 citation statement)

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“…The SVM-based prediction technique is often used to manage vast amounts of data, and it has been demonstrated to perform well in a number of biological data processing applications such as classification, protein functions and type identification [29][30][31]. In this study, we used SVM to analyze the performance of the classifiers and five-fold cross validation [32][33][34]. The generated approach model's performance was assessed using the original and additional protein datasets.…”

Section: Machine Learning Based Support Vector Machine (Svm)mentioning

confidence: 99%

Computational method for aromatase-related proteins using machine learning approach

Selvaraj

Kaur

2023

PLoS ONE

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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.

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