ntvНаучно-технический вестник информационных технологий, механики и оптикиScientific and Technical Journal of Information Technologies, Mechanics and Optics2226-14942500-0373Университет ИТМО10.17586/2226-1494-2024-24-1-101-111ntv-119Research ArticleКОМПЬЮТЕРНЫЕ СИСТЕМЫ И ИНФОРМАЦИОННЫЕ ТЕХНОЛОГИИCOMPUTER SCIENCEОснованное на особом интересе прогнозирование протоонкогена и обнаружение возможностей его мутации в онкоген на основе первоначального анализа последовательности аминокислотDeep attention based Proto-oncogene prediction and Oncogene transition possibility detection using moments and position based amino acid featureshttps://orcid.org/0009-0001-2012-3169ВиджаялакшмиМ.VijayalakshmiM.

Виджаялакшми Маникам — научный сотрудник; доцент   

Абишекапати, Тируневелли-627012

Тирунелвели, 627011

 

Manickam Vijayalakshmi — Research Scholar; Assistant Professor

 Abishekapatti, Tirunelveli-627012

 Tirunelveli, 627011

vijimarthresearch@gmail.comhttps://orcid.org/0009-0006-0552-0138ВаллинаягиМ.VallinayagiM.

Валлинаяги Махеш — PhD, руководитель, доцент

 Тирунелвели, 627011

 

Mahesh Vallinayagi — PhD, Head, Associate Professor

 Tirunelveli, 627011

vallinayagimahesh@gmail.comУниверситет Манонманиам Сундаранар; Женский колледж Шри СарадаИндияAffiliated to Manonmaniam Sundaranar University; Sri Sarada College for WomenIndiaЖенский колледж Шри СарадаИндияSri Sarada College for WomenIndia202413122024241101111Copyright © Виджаялакшми М., Валлинаяги М., 20242024Виджаялакшми М., Валлинаяги М.Vijayalakshmi M., Vallinayagi M.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://ntv.elpub.ru/jour/article/view/119

Утрата регуляторной функции генов, подавляющих опухоль, и мутации в протоонкогенах являются общими механизмами, лежащими в основе неконтролируемого роста опухолей при разнообразном комплексе заболеваний, известных как рак. Онкоген можно излечить путем диагностики и лечения возможностей протоонкогена на ранних стадиях. В последнее время подходы машинного обучения помогают сосредоточить внимание и предоставить информацию о возможностях протоонкогена, который может превращаться в онкоген при различных типах рака или изменять его на ранних стадиях. Предложен эффективный и уникальный предиктор протоонкогена с помощью нейронной сети Bi-Directional Long Short Term Memory (BiLSTM), дополненный концепцией ухода за больными. Этот подход также позволяет определить вероятность перехода от протоонкогена к онкогену с использованием статистических моментов, представления аминокислотного состава на основе положения и глубоких особенностей, извлеченных из последовательности. В работе применен классификатор K-Nearest Neighbor с помощью, которого можно определить вероятность перехода от протоонкогена к раковому онкогену.

The loss of the regulatory function of tumor suppression genes and mutations in Proto-oncogene are the common underlying mechanisms for uncontrolled tumor growth in the varied complex of disorders known as cancer. Oncogene can be curable by means of diagnosing and treating the possibilities of Proto-oncogene at earlier stages. Recently, machine learning approaches helps to focus and provide information about the possibilities of Proto-oncogene that may change into oncogene in different cancer types. This study helps to diagnose the possibilities of Proto-oncogene which are possible to change oncogenes at earlier stage. Thus, this present study proposed an efficient unique predictor of Proto[1]oncogene with the help of Bi-Directional Long Short Term Memory added with attention concept. This approach also find the probability of Proto-oncogene to oncogene using statistical moments, position based amino-acid composition representation and deep features extracted from the sequence. Consequently, this study suggests that using a K-Nearest Neighbor classifier it is possible to find probability of changing from Proto-oncogene to cancerous oncogene.

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The authors declare that there are no conflicts of interest present.