title: ÐÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ðµ Ð³ÐµÐ½ÐµÑÐ°ÑÐ¸Ð²Ð½Ð¾-ÑÐ¾ÑÑÑÐ·Ð°ÑÐµÐ»ÑÐ½Ð¾Ð¹ ÑÐµÑÐ¸ Ð´Ð»Ñ Ð³ÐµÐ½ÐµÑÐ°ÑÐ¸Ð¸ Ð¸ÑÐºÑÑÑÑÐ²ÐµÐ½Ð½ÑÑ Ð³ÐµÐ½Ð¾Ð¼Ð¾Ð²
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Application of generative adversarial network for artificial genomes generation
Creators: name: ÐÐµÑÐ±Ð¾Ð², ÐÐ°Ð²ÐµÐ»
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2024
Subjects: Ð³ÐµÐ½ÐµÑÐ°ÑÐ¸Ð²Ð½Ð¾-ÑÐ¾ÑÑÑÐ·Ð°ÑÐµÐ»ÑÐ½ÑÐµ ÑÐµÑÐ¸
Ð¸ÑÐºÑÑÑÑÐ²ÐµÐ½Ð½ÑÐµ Ð³ÐµÐ½Ð¾Ð¼Ñ
generative adversarial network
artificial genomes

description: ÐÐ°Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¿Ð¾ÑÐ²ÑÑÐµÐ½Ð° ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐµ REST ÑÐµÑÐ²Ð¸ÑÐ° Ð´Ð»Ñ Ð³ÐµÐ½ÐµÑÐ°ÑÐ¸Ð¸ Ð¸ÑÐºÑÑÑÑÐ²ÐµÐ½Ð½ÑÑ Ð³ÐµÐ½Ð¾Ð¼Ð¾Ð² Ð¿ÑÐ¸ Ð¿Ð¾Ð¼Ð¾ÑÐ¸ Ð³ÐµÐ½ÐµÑÐ°ÑÐ¸Ð²Ð½Ð¾-ÑÐ¾ÑÑÑÐ·Ð°ÑÐµÐ»ÑÐ½Ð¾Ð¹ ÑÐµÑÐ¸. ÐÑÐ¾Ð²ÐµÐ´ÐµÐ½ Ð¾Ð±Ð·Ð¾Ñ ÑÑÑÐµÑÑÐ²ÑÑÑÐ¸Ñ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ð¹ Ð¿Ð¾ Ð³ÐµÐ½ÐµÑÐ°ÑÐ¸Ð¸ Ð³ÐµÐ½Ð¾Ð¼Ð¾Ð², Ð²ÑÑÐ²Ð»ÐµÐ½Ñ ÑÑÐµÐ±Ð¾Ð²Ð°Ð½Ð¸Ñ, ÐºÐ¾ÑÐ¾ÑÑÐ¼ Ð´Ð¾Ð»Ð¶Ð½Ð¾ ÑÐ¾Ð¾ÑÐ²ÐµÑÑÑÐ²Ð¾Ð²Ð°ÑÑ ÑÐ°Ð·ÑÐ°Ð±Ð°ÑÑÐ²Ð°ÐµÐ¼Ð¾Ðµ Ð¿ÑÐ¸Ð»Ð¾Ð¶ÐµÐ½Ð¸Ðµ, ÑÐ´ÐµÐ»Ð°Ð½ ÑÑÐ°Ð²Ð½Ð¸ÑÐµÐ»ÑÐ½ÑÐ¹ Ð°Ð½Ð°Ð»Ð¸Ð· Ð´Ð¾ÑÑÑÐ¿Ð½ÑÑ Ð¸Ð½ÑÑÑÑÐ¼ÐµÐ½ÑÐ¾Ð² Ð¸ ÑÐµÑÐ½Ð¾Ð»Ð¾Ð³Ð¸Ð¹ Ð´Ð»Ñ ÑÐµÑÐµÐ½Ð¸Ñ Ð¿Ð¾ÑÑÐ°Ð²Ð»ÐµÐ½Ð½ÑÑ Ð·Ð°Ð´Ð°Ñ, Ð² ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐµ ÐºÐ¾ÑÐ¾ÑÐ¾Ð³Ð¾ Ð²ÑÐ±ÑÐ°Ð½Ñ Ð½Ð°Ð¸Ð±Ð¾Ð»ÐµÐµ Ð¾Ð¿ÑÐ¸Ð¼Ð°Ð»ÑÐ½ÑÐµ Ð² ÑÐ°Ð¼ÐºÐ°Ñ Ð¿ÑÐµÐ´ÑÑÐ²Ð»ÑÐµÐ¼ÑÑ Ðº ÑÐµÑÐµÐ½Ð¸Ñ ÑÑÐµÐ±Ð¾Ð²Ð°Ð½Ð¸Ð¹. Ð ÑÐ¾Ð´Ðµ ÑÐ°Ð±Ð¾ÑÑ Ñ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸ÐµÐ¼ ÑÐ·ÑÐºÐ° Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Pyton Ð¸ ÑÑÐµÐ¹Ð¼Ð¾ÑÐºÐ° Keras (TensorFlow) ÑÐ¿ÑÐ¾ÐµÐºÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð°, ÑÐµÐ°Ð»Ð¸Ð·Ð¾Ð²Ð°Ð½Ð° Ð¸ Ð¾Ð±ÑÑÐµÐ½Ð° Ð½Ð° Ð¾ÑÐºÑÑÑÐ¾Ð¼ Ð±Ð°Ð½ÐºÐµ Ð´Ð°Ð½Ð½ÑÑ Â«1000 Ð³ÐµÐ½Ð¾Ð¼Ð¾Ð²Â» Ð³ÐµÐ½ÐµÑÐ°ÑÐ¸Ð²Ð½Ð¾-ÑÐ¾ÑÑÑÐ·Ð°ÑÐµÐ»ÑÐ½Ð°Ñ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð°Ñ ÑÐµÑÑ. ÐÐ»Ñ Ð´ÐµÐ¼Ð¾Ð½ÑÑÑÐ°ÑÐ¸Ð¸ ÑÐ°Ð±Ð¾ÑÑ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð½Ð° Ð±Ð°Ð·Ðµ ÑÑÐµÐ¹Ð¼Ð²Ð¾ÑÐºÐ° FastAPI ÑÐµÐ°Ð»Ð¸Ð·Ð¾Ð²Ð°Ð½ REST API ÑÐµÑÐ²Ð¸Ñ, Ð¿ÑÐµÐ´Ð¾ÑÑÐ°Ð²Ð»ÑÑÑÐ¸Ð¹ Ð½Ð°Ð±Ð¾Ñ Ð¾Ð¿ÐµÑÐ°ÑÐ¸Ð¹ Ð´Ð»Ñ Ð°ÑÐ¸Ð½ÑÑÐ¾Ð½Ð½Ð¾Ð¹ Ð³ÐµÐ½ÐµÑÐ°ÑÐ¸Ð¸ ÑÐ°Ð¹Ð»Ð¾Ð², ÑÐ¾Ð´ÐµÑÐ¶Ð°ÑÐ¸Ñ Ð¿Ð¾ÑÐ»ÐµÐ´Ð¾Ð²Ð°ÑÐµÐ»ÑÐ½Ð¾ÑÑÐ¸ Ð½ÑÐºÐ»ÐµÐ¾ÑÐ¸Ð´Ð¾Ð². ÐÐ¾Ð»ÑÑÐµÐ½Ð½Ð°Ñ Ð¼Ð¾Ð´ÐµÐ»Ñ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸ Ð¿ÑÐ¾Ð²ÐµÑÐµÐ½Ð° Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ ÑÐ°Ð·Ð»Ð¸ÑÐ½ÑÑ ÑÑÐ°ÑÐ¸ÑÑÐ¸ÑÐµÑÐºÐ¸Ñ Ð¼ÐµÑÑÐ¸Ðº (ÑÐ°ÐºÐ¸Ñ ÐºÐ°Ðº ÐºÑÐ¸ÑÐµÑÐ¸Ð¹ ÐÐ¾Ð»Ð¼Ð¾Ð³Ð¾ÑÐ¾Ð²Ð°-Ð¡Ð¼Ð¸ÑÐ½Ð¾Ð²Ð°) Ð½Ð° ÑÐ¾Ð¾ÑÐ²ÐµÑÑÑÐ²Ð¸Ðµ ÑÑÐµÐ±Ð¾Ð²Ð°Ð½Ð¸ÑÐ¼ ÐºÐ°ÑÐµÑÑÐ²Ð° Ð¿Ð¾Ð»ÑÑÐµÐ½Ð½ÑÑ ÑÐ¸Ð½ÑÐµÑÐ¸ÑÐµÑÐºÐ¸Ñ Ð´Ð°Ð½Ð½ÑÑ, Ð° ÑÐ°ÐºÐ¶Ðµ Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½Ð¾ Ð¸Ð½ÑÐµÐ³ÑÐ°ÑÐ¸Ð¾Ð½Ð½Ð¾Ðµ ÑÐµÑÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ REST ÑÐµÑÐ²Ð¸ÑÐ° Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ Ð±Ð¸Ð±Ð»Ð¸Ð¾ÑÐµÐºÐ¸ PyTest.
description: This work is devoted to the development of a REST service for generating artificial genomes using a generative adversarial network. A review of existing research on genome generation was carried out, the requirements that the developed application must meet were identified, a comparative analysis of the available tools and technologies for solving the problems was made, as a result of which the most optimal ones were selected in terms of the requirements presented for the solution. Using the Python programming language and the Keras framework (TensorFlow), a generative adversarial neural network was designed, implemented and trained on the open data bank Â«1000 GenomesÂ». To demonstrate the operation of the model, a REST API service providing a set of operations for the asynchronous generation of files containing nucleotide sequences has been implemented on base of the FastAPI framework. The designed model was tested using various statistical metrics (such as the Kolmogorov-Smirnov criterion) to ensure compliance with the quality requirements of the obtained synthetic data and integration testing of the REST service was carried out using the PyTest library.