title: Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð° Ð°Ð²ÑÐ¾Ð¼Ð°ÑÐ¸ÑÐµÑÐºÐ¾Ð³Ð¾ Ð°Ð½Ð°Ð»Ð¸Ð·Ð° Ð¸ Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÐ¸ Ð´Ð²ÑÐ¼ÐµÑÐ½ÑÑ ÑÐ»ÐµÐºÑÑÐ¾ÑÐ¾ÑÐµÑÐ¸ÑÐµÑÐºÐ¸Ñ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: Development of an algorithm for automatic analysis and processing of two-dimensional electrophoretic images
Creators: name: ÐÐ°Ð±Ð°ÑÐµÐ½ÐºÐ¾, Ð¤ÐµÐ´Ð¾Ñ
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: 2023
Subjects: ÑÐ»ÐµÐºÑÑÐ¾ÑÐ¾ÑÐµÐ·
Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹
Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
ÑÑÐ°Ð½ÑÑÐµÑÐ½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
Ð´ÐµÑÐµÐºÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ Ð¾Ð±ÑÐµÐºÑÐ¾Ð²
electrophoresis
image processing
neural network
transfer learning
object detection

description: Ð Ð´Ð°Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÐµ Ð¿ÑÐµÐ´ÑÑÐ°Ð²Ð»ÐµÐ½Ñ Ð½Ð¾Ð²ÑÐµ Ð¿Ð¾Ð´ÑÐ¾Ð´Ñ Ðº Ð°Ð½Ð°Ð»Ð¸Ð·Ñ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹ Ð´Ð²ÑÐ¼ÐµÑÐ½Ð¾Ð³Ð¾ ÑÐ»ÐµÐºÑÑÐ¾ÑÐ¾ÑÐµÐ·Ð°. ÐÑÐ»Ð¸ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½Ñ Ð¸ ÑÑÐ¿ÐµÑÐ½Ð¾ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ñ Ð´Ð²Ðµ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ, Ð¾ÑÐ½Ð¾Ð²Ð°Ð½Ð½ÑÐµ Ð½Ð° ÑÐ¾Ð²ÑÐµÐ¼ÐµÐ½Ð½ÑÑ Ð°ÑÑÐ¸ÑÐµÐºÑÑÑÐ°Ñ UNet Ð¸ Xception, Ð´Ð»Ñ Ð°Ð²ÑÐ¾Ð¼Ð°ÑÐ¸Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð½Ð¾Ð³Ð¾ ÑÐµÐ³Ð¼ÐµÐ½ÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð¸ Ð°Ð½Ð½Ð¾ÑÐ°ÑÐ¸Ð¸ Ð±ÐµÐ»ÐºÐ¾Ð²ÑÑ Ð¿ÑÑÐµÐ½ Ð½Ð° ÑÐ»ÐµÐºÑÑÐ¾ÑÐ¾ÑÐµÑÐ¸ÑÐµÑÐºÐ¸Ñ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸ÑÑ. Ð ÐµÐ·ÑÐ»ÑÑÐ°ÑÑ Ð¿Ð¾ÐºÐ°Ð·Ð°Ð»Ð¸, ÑÑÐ¾ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ðµ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð¿Ð¾Ð·Ð²Ð¾Ð»ÑÐµÑ Ð·Ð½Ð°ÑÐ¸ÑÐµÐ»ÑÐ½Ð¾ ÑÐ»ÑÑÑÐ¸ÑÑ ÐºÐ°ÑÐµÑÑÐ²Ð¾ ÑÐµÐ³Ð¼ÐµÐ½ÑÐ°ÑÐ¸Ð¸ Ð¸ Ð°Ð½Ð½Ð¾ÑÐ°ÑÐ¸Ð¸, ÑÑÐ¾ ÑÐ²Ð»ÑÐµÑÑÑ ÐºÐ»ÑÑÐ¾Ð¼ Ðº Ð±Ð¾Ð»ÐµÐµ ÑÐ¾ÑÐ½Ð¾Ð¼Ñ Ð¸ Ð¾Ð±ÑÐµÐºÑÐ¸Ð²Ð½Ð¾Ð¼Ñ Ð°Ð½Ð°Ð»Ð¸Ð·Ñ Ð±ÐµÐ»ÐºÐ¾Ð²ÑÑ Ð¿ÑÑÐµÐ½ Ð½Ð° Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸ÑÑ Ð´Ð²ÑÐ¼ÐµÑÐ½Ð¾Ð³Ð¾ ÑÐ»ÐµÐºÑÑÐ¾ÑÐ¾ÑÐµÐ·Ð°. ÐÐ½ÐµÐ´ÑÐµÐ½Ð¸Ðµ Ð´Ð°Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ½Ð¾Ð»Ð¾Ð³Ð¸Ð¸ Ð¼Ð¾Ð¶ÐµÑ Ð·Ð½Ð°ÑÐ¸ÑÐµÐ»ÑÐ½Ð¾ Ð¾Ð±Ð»ÐµÐ³ÑÐ¸ÑÑ Ð¸ ÑÑÐºÐ¾ÑÐ¸ÑÑ Ð¿ÑÐ¾ÑÐµÑÑ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ Ð±ÐµÐ»ÐºÐ¾Ð²ÑÑ Ð¾Ð±ÑÐ°Ð·ÑÐ¾Ð². Ð­ÑÑÐµÐºÑÐ¸Ð²Ð½Ð¾ÑÑÑ Ð¿ÑÐµÐ´Ð»Ð¾Ð¶ÐµÐ½Ð½Ð¾Ð³Ð¾ Ð¿Ð¾Ð´ÑÐ¾Ð´Ð° Ð±ÑÐ»Ð° Ð¿Ð¾Ð´ÑÐ²ÐµÑÐ¶Ð´ÐµÐ½Ð° Ð½Ð° Ð¾Ð±ÑÐ°Ð·ÑÐ°Ñ Ð¿Ð»Ð°Ð·Ð¼Ñ ÐºÑÐ¾Ð²Ð¸. ÐÐ¾Ð»ÑÑÐµÐ½Ð½ÑÐµ Ð´Ð°Ð½Ð½ÑÐµ ÑÐºÐ°Ð·ÑÐ²Ð°ÑÑ Ð½Ð° Ð²Ð¾Ð·Ð¼Ð¾Ð¶Ð½Ð¾ÑÑÑ ÑÐ°ÑÑÐ¸ÑÐµÐ½Ð¸Ñ Ð¿ÑÐ°ÐºÑÐ¸ÑÐµÑÐºÐ¾Ð³Ð¾ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ñ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½Ð½ÑÑ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð´Ð»Ñ Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÐ¸ Ð¸ Ð°Ð½Ð½Ð¾ÑÐ°ÑÐ¸Ð¸ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹ Ð´Ð²ÑÐ¼ÐµÑÐ½Ð¾Ð³Ð¾ ÑÐ»ÐµÐºÑÑÐ¾ÑÐ¾ÑÐµÐ·Ð° Ð¸Ð· ÑÐ°Ð·Ð»Ð¸ÑÐ½ÑÑ Ð¸ÑÑÐ¾ÑÐ½Ð¸ÐºÐ¾Ð². Ð ÐºÐ°ÑÐµÑÑÐ²Ðµ Ð¿ÐµÑÑÐ¿ÐµÐºÑÐ¸Ð²Ñ, ÑÐ»ÐµÐ´ÑÐµÑ ÑÐ¿Ð¾Ð¼ÑÐ½ÑÑÑ Ð¾ Ð²Ð¾Ð·Ð¼Ð¾Ð¶Ð½Ð¾ÑÑÐ¸ Ð´Ð°Ð»ÑÐ½ÐµÐ¹ÑÐµÐ³Ð¾ ÑÐ°Ð·Ð²Ð¸ÑÐ¸Ñ Ð¸ ÑÐ»ÑÑÑÐµÐ½Ð¸Ñ Ð¿ÑÐµÐ´Ð»Ð¾Ð¶ÐµÐ½Ð½ÑÑ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð². ÐÐ´Ð½Ð¾ Ð¸Ð· Ð½Ð°Ð¿ÑÐ°Ð²Ð»ÐµÐ½Ð¸Ð¹ -- ÑÑÐ¾ ÑÐ°ÑÑÐ¸ÑÐµÐ½Ð¸Ðµ Ð½Ð°Ð±Ð¾ÑÐ° Ð´Ð°Ð½Ð½ÑÑ Ð´Ð»Ñ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð¸ ÑÐµÑÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹, ÑÑÐ¾ Ð¿Ð¾Ð·Ð²Ð¾Ð»Ð¸Ñ ÑÐ»ÑÑÑÐ¸ÑÑ Ð¸Ñ ÑÐ¾ÑÐ½Ð¾ÑÑÑ Ð¸ ÑÐ½Ð¸Ð²ÐµÑÑÐ°Ð»ÑÐ½Ð¾ÑÑÑ. ÐÑÑÐ³Ð¾Ðµ Ð½Ð°Ð¿ÑÐ°Ð²Ð»ÐµÐ½Ð¸Ðµ -- ÑÑÐ¾ Ð¸Ð½ÑÐµÐ³ÑÐ°ÑÐ¸Ñ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½Ð½ÑÑ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ñ Ð´ÑÑÐ³Ð¸Ð¼Ð¸ Ð±Ð¸Ð¾Ð¸Ð½ÑÐ¾ÑÐ¼Ð°ÑÐ¸ÑÐµÑÐºÐ¸Ð¼Ð¸ Ð¸Ð½ÑÑÑÑÐ¼ÐµÐ½ÑÐ°Ð¼Ð¸ Ð¸ Ð±Ð°Ð·Ð°Ð¼Ð¸ Ð´Ð°Ð½Ð½ÑÑ Ð´Ð»Ñ ÑÐ¾Ð·Ð´Ð°Ð½Ð¸Ñ ÐºÐ¾Ð¼Ð¿Ð»ÐµÐºÑÐ½Ð¾Ð³Ð¾ ÑÐµÑÐµÐ½Ð¸Ñ Ð² Ð¾Ð±Ð»Ð°ÑÑÐ¸ Ð±Ð¸Ð¾Ð¸Ð½ÑÐ¾ÑÐ¼Ð°ÑÐ¸ÐºÐ¸.
description: The current work introduces novel approaches to the processing and annotation of two-dimensional electrophoresis images. Two deep learning models, based on the modern architectures of UNet and Xception, were developed and successfully applied for the automated segmentation and annotation of protein spots on electrophoretic images. The results demonstrated that the use of deep learning models significantly improves the quality of segmentation and annotation, which is key to a more accurate and objective analysis of protein spots on two-dimensional electrophoresis images. Implementing this technology can greatly facilitate and accelerate the process of protein samples analysis. The efficacy of the proposed approach was validated on blood plasma samples. The findings suggest the possibility of expanding the practical application of the developed models for processing and annotating two-dimensional electrophoresis maps from various sources. Looking forward, the potential for further development and improvement of the proposed algorithms is considerable. One direction involves expanding the data set for training and testing the models, which would improve their accuracy and universality. Another direction is integrating the developed models with other bioinformatics tools and databases to create a comprehensive solution in the field of bioinformatics.