Molekulyarnaya Meditsina (Molecular medicine)
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Years of issue
2024-2025
journal names
Molekulyarnaya Meditsina (Molecular medicine)
Top-3 citing journals

Bulletin of Experimental Biology and Medicine
(9 citations)

Meditsinskiy sovet = Medical Council
(6 citations)
Top-3 organizations

Saint-Petersburg Research Institute of Phthisiopulmonology
(21 publications)

Lomonosov Moscow State University
(19 publications)

Pavlov Institute of Physiology of the Russian Academy of Sciences
(18 publications)

Saint-Petersburg Research Institute of Phthisiopulmonology
(21 publications)

Lomonosov Moscow State University
(19 publications)

Pavlov Institute of Physiology of the Russian Academy of Sciences
(18 publications)
Most cited in 5 years
Found
Publications found: 2850

Analysis of The Role of Deep Learning Models in Image Classification Applications
Li X.
Image classification is a fundamental task in computer science, underpinning various applications such as object detection, face recognition, and object interaction analysis. The concept holds significant value due to its wide-ranging applications across multiple fields. Traditional methods for image classification, however, have been limited by their slow processing speed, rigidity, and high costs. The integration of deep learning models, particularly Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs), has revolutionized this process, enabling the development of automated, fast, and practical systems. These advanced models are now employed in diverse areas, including biomedical science, remote sensing, and business management, thanks to their ability to achieve high accuracy across a broad spectrum of scenarios. Training these models involves the use of well-known datasets like Canadian Institute for Advanced Research (CIFAR) and Modified National Institute of Standards and Technology (MNIST), which provide the necessary data for optimization and validation. The paper examines the structure, functionality, advantages, and limitations of CNNs and SVMs in the context of image classification, demonstrating that deep learning-driven classification is now a mainstream research focus. This study highlights the transformative impact of these models and provides insights into their future potential.

Comparative Analysis of YOLO Variants Based on Performance Evaluation for Object Detection
Chen A.
This study focuses on analysing and exploring the You Only Look Once (YOLO) algorithm. Specifically, this article analyses the evolution and performance of three versions (YOLOv1, YOLOv5, and YOLOv8) in object detection. The research begins by detailing the fundamental concepts of object detection and the datasets commonly used in this field. It then delves into the specific architectures and experimental outcomes associated with each YOLO version. The analysis reveals that while YOLOv8 introduces advanced features and improvements, earlier versions like YOLOv5 may offer superior stability and performance under certain conditions, particularly in specific tasks such as car detection. The discussion emphasizes the significant impact of factors such as batch size on model performance, suggesting that fine-tuning these parameters can optimize the algorithm for particular applications. The study concludes that the future of YOLO development lies in exploring and refining different variants, particularly those of YOLOv8, to better meet diverse requirements. By focusing on five distinct YOLOv8 variants, the research aims to enhance the adaptability and effectiveness of the YOLO framework across a wide range of object detection challenges, thereby contributing valuable insights into the ongoing advancement of this technology.

Comparison of Fully Convolutional Networks and U-Net for Optic Disc and Optic Cup Segmentation
Jin Z.
Glaucoma, the leading cause of irreversible blindness, must be diagnosed early and thus treated in time. However, it has no noticeable symptoms in its early stages and may not be detected easily. This paper aims to compare two well-known convolutional neural network (CNN) structures, namely Fully Convolutional Networks (FCNs) and U-Net for the segmentation of the optic disc (OD) and optic cup (OC) from retinal fundus images which play an important role in glaucoma diagnosis. The performance of both models is assessed using qualitative parameters such as the Dice coefficient, Jaccard index, and cup-to-disc ratio (CDR) error. In our experiment, the U-Net model yields more accurate segmentation results with 0.9601 average pixel accuracy and 0.9255 dice score for OD segmentation, outperforming the FCNs model with 0.9560 average pixel accuracy and 0.9132 dice score for OD segmentation. However, FCNs have a shorter inference time of 0. 0043 seconds against U-net’s 0. 0062 seconds making FCNs more suitable for real-time applications. The restrictions related to this study include biases from using only one dataset acquired from particular imaging devices, dependency on mask-based cropping techniques, and comparison being restricted to two fundamental architectures. This work presents the contribution of the deep learning models in improving glaucoma screening and therefore helping in avoiding blindness.

Sentiment Analysis of Mobile Phone Reviews Using XGBoost and Word Vectors
Wang Z.
Consumer reviews are an important source of data used to judge and examine consumer sentiment, and data mining for reviews of electronic products is an important way to help improve the design of electronic products. The research is based on the consumer reviews of online cell phone e-commerce, The paper constructs a sentiment dictionary in this field based on the Sentiment Oriented Point Mutual Information (SO-PMI) algorithm, and the sentiment weight of the review word vectors. An extreme Gradient Boosting Tree (XGBoost) is used to integrate word vectors and a Large Language Model (LLM) to construct a sentiment recognition model, and finally, a review sentiment index is derived, which unfolds from multiple dimensions to analyze the sentiment tendency in consumer reviews. The empirical analysis shows that the accuracy, recall, area under the curve (AUC), and other validation indexes of the constructed sentiment recognition model are further improved compared with the LLM model, which has a certain application value. When applying the weighted word vector method, the model has been significantly improved compared with the LLM model, the accuracy is increased by 5%, the accuracy is increased by 10%, and the comprehensive accuracy is increased by 2% after the comprehensive application of the two.

A Hybrid Machine Learning Framework for Soccer Match Outcome Prediction: Incorporating Bivariate Poisson Distribution
Chen Z.A.
The 2022 FIFA World Cup final attracted 1.5 billion viewers, while billions of dollars are wagered on soccer matches every year. The increasing demand for accurate predictions, both for academic research and betting purposes, has driven the development of advanced forecasting models. This study explores the application of mathematical and machine learning models to predict results of soccer matches, with the dual aim of academic advancement and profitable betting. The author utilizes a comprehensive dataset from top European leagues (2014-2022) and employ models including Bivariate Poisson Distribution, Naive Bayes, Neural Networks, Support Vector Machines, Random Forests, and Gradient Boosting. The paper’s feature engineering combines historical match statistics, FIFA ratings, and betting odds. While Random Forests achieved the highest accuracy (56.25%), predicting draws remains challenging. The study highlights the potential for improved prediction systems and suggests future research in advanced draw prediction techniques and profitability analysis, the paper provides research directions for researchers in related fields.

The Applications and Prospects of Large Language Models in Traffic Flow Prediction
Liu Y.
Predicting traffic flow is crucial for the functionality of intelligent transportation systems. It is of critical importance to relieve traffic pressure, reduce accident rates, and alleviate environmental pollution. It is an important part of the construction of modern intelligent road networks. With advancements in deep learning (DL), DL models have made notable strides in prediction. However, due to the complexity and non-transparency of DL models themselves, there are still problems of low accuracy and interpretability in traffic flow prediction (TFP). Leveraging large language models (LLM) helps to improve the negative conditions caused by other DL models in prediction. This paper first briefly summarizes the basic characteristics of LLM and their advantages in TFP; then conducts relevant research and analysis in the order of experimental design steps comparison and results and conclusions comparison; then analyzes and discusses the current problems and challenges faced by LLM; finally, it looks forward to future research directions and development trends, and summarizes this paper.

Hierarchical Learning: A Hybrid of Federated Learning and Personalization Fine-Tuning
Li S., Zhang B.
Hierarchical Federated Learning (FL) presents a novel approach that combines global model training with localized personalization fine-tuning to enhance the predictive accuracy of decentralized machine learning systems. Traditional FL methods, which allow multiple clients to collaboratively train a global model without sharing raw data, are hindered by issues such as non-independent and identically distributed (non-IID) data, communication overhead, and limited generalization across diverse client datasets. This study proposes a hierarchical model that mitigates these challenges by incorporating a global model, trained using the Federated Averaging (FedAvg) algorithm, and applying client-specific fine-tuning to improve local model performance. The experiment conducted on a movie recommendation system demonstrates that this hierarchical approach significantly reduces the global model’s error while offering personalized improvements on client-specific datasets. Results show an average Root Mean Squared Error (RMSE) reduction of 0.0460 following local personalization. This hybrid approach not only enhances model accuracy but also preserves data privacy and increases scalability, making it a promising solution for decentralized recommendation systems.

Research and Application of Heart Disease Prediction Model Based on Machine Learning
Bao Y.
As heart disease has become the leading cause of death worldwide, early and accurate prediction is crucial to help doctors make initial judgments about patients and improve their survival rates. This study aims to improve the accuracy and efficiency of heart disease prediction through Machine learning (ML) methods to help medical diagnosis. A heart disease dataset was used in the study, and multiple ML models were used to analyze multiple key health features, and the model performance was verified through a test set. This paper concludes that Logistic regression and random forests perform well in this task and have high practical value. Future research can stack models and optimize data sources to improve the practical performance of the model. This study provides a basic framework for building an intelligent medical auxiliary diagnosis system, which helps to achieve early prevention and timely judgment of heart disease, thereby improving the overall efficiency of medical services.

Advances in Image Generation Technology: Exploring GANs and MirrorGANs
Shi L.
This paper is an in-depth study by delving into the latest in image generation technology, where thesis is focusing on the Generative Adversarial Networks (GANs) and MirrorGANs possibilities. Image Generation is the backbone of visual computing, mostly utilized in intelligent designs. It is for this reason that this research aims at unravelling the theoretical basis and consolidated practices of GANs when it conies to generating both high-quality and semantically consistent imagery. The study will investigate the whole of the image generation process, starting from data preprocessing to the use of GANs to generate images from textual descriptions. The work discussed the relevance as well as the limitations of these technologies from the artistic point of view, medical imaging, and virtual reality. Tire article concludes that the paper sketches the data and experiments that show that the realism and richness hi picture quality are accentuated when GANs and MirrorGANs are incorporated. This suggests the scope of image-generation technology to enhance human-machine collaboration and allow for innovating hi smart tech. Further studies will be geared to enhancing these methods and consequently drawing humanity and machines closer, which hi nun will fuel the ongoing progress in this fast-paced sphere.

Effectiveness Evaluation of Random Forest, Naive Bayes, and Support Vector Machine Models for KDDCUP99 Anomaly Detection Based on K-means Clustering
Zhang M.
Security in the World Wide Web has recently seen an enormous upgrade in almost every aspect. Identifying malicious activities hi a network such as network attacks and malicious users plays a significant role hi these upgraded security directions. This research utilizes the KDDCUP99 dataset to incorporate K-means clustering with three classifiers: Random Forest (RF). Naïve Bayes (NB). and Support Vector Machine (SVM) with the goal to boost the accuracy of predicting network intrusions. In tins paper. K-means clustering technique is applied as a preprocessing step to enhance the overall quality of network intrusion detection and maximize the accuracy of the network security measures. The goal is to identify anomalies with high accuracy. Experimental results hidicate that the optimal combination is K-means + RF. which outperformed the others hi precision, recall, and Fl-score. Although K-means + NB demonstrated superior recall for certahi smaller anomalies, it underperformed compared to the RF model. The paper concludes by highlighting the value of ensemble approaches, in particular Random Forest, for tackling anomaly detection and network security issues, particularly hi light of the expanding significance of social networks and the internet.

Time Series Analysis: Application of LSTM model in predicting PM 2.5 concentration in Beijing
Yang R.
Air pollution forecasting for public health and policy-making has a critical importance, this paper employs a Long Short-Term Memory (LSTM) model to perform in-depth prediction of PM2.5 concentrations measured at the U.S. Embassy in Beijing, outperforming regular forecasting approaches. In the LSTM model, the research examines a very detailed hourly dataset and beats regular forecasting approaches. A key finding is the model’s ability to effectively generalize from historical data to predict future air quality trends, with its adeptness at handling time-dependent relationships. This research emphasizes the importance of LSTM in air pollution prediction and management in environmental science as it provides an effective means for planning and making decisions on air quality management. This research is of great importance in providing a groundwork for further enhancement of prediction modeling. By offering a more reliable and sophisticated picture of air quality variations, this study addresses the current problem about how urban air pollution control could be improved in the city.

Research on Analyzing the Emotional Polarity of Malicious Swipe Comments on E-commerce Platforms Based on NPL
Ren C.
In the era of rapid advancements in natural language processing (NLP) models, these technologies have immense potential to detect and address societal issues, enhancing the functioning of the digital society. Online shopping platforms rely heavily on user reviews to influence buyer decisions, yet malicious reviews can significantly degrade user experience. This study focuses on analyzing the emotional polarity of malicious brushorder (falsely generated) reviews in e-commerce product comments, utilizing the Jingdong product review dataset. The methodology involves utilizing the Word2Vec model to vectorize the text data, followed by principal component analysis (PCA) for outlier detection to identify potential malicious reviews based on their unique characteristics. The PCA results are further leveraged for dimensionality reduction, simplifying the dataset. Subsequently, the BERT model is employed to perform semantic similarity analysis, allowing for the screening and expansion of the experimental dataset with similar malicious comments. This enriched dataset is then subjected to sentiment polarity analysis, enabling a deeper tinderstanding of the nature and impact of these malicious reviews. By facilitating buyers in making informed decisions based on genuine reviews, this research underscores the practical value of NLP hi addressing real-world challenges in e-commerce.

The Use of Natural Language Processing Model in Literary Style Analysis of Chinese Text
Ye J.
In recent years, research on Natural Language Processing (NLP) has made consistent progress and has become a popular topic. As a promising branch of Machine Learning, NLP focuses on the understanding, generating and analysing of human languages. The applications of NLP include chatbots and language translation. This paper represents a HanLP based NLP model. The model is capable of analysing the literary style of given Chinese text by quantifying the literary style of the text on the basis of five fundamental elements, namely literary grace, sentiments, momentum, climate and lingering charm. This paper presents the input and output data of the research and conducts analyses on these data. Moreover, this paper draws a conclusion on the deviation rate and robustness of the model. It is reckoned that this model initially possesses the function of literary style analysis of Chinese text. The research, per se, along with its data, is capable of being reference for research in NLP and related fields.

Sql injection detection using Naïve Bayes classifier: A probabilistic approach for web application security
Lu Z.
A pervasive security issue in web applications is database injection, enabling attackers to alter SQL queries in order to get unauthorized access to confidential information. Using the Naive Bayes classifier, a probabilistic model specifically developed for text classification tasks, this work introduces a novel method for detecting SQL injection vulnerabilities.The process begins by collecting and organizing a comprehensive dataset, which includes both harmful and non-malicious SQL queries. Feature extraction is later employed to identify patterns and characteristics commonly associated with SQL injection, such as certain SQL clauses and logical operators. This collection of attributes is employed to generate a feature vector that serves as the input for the Naive Bayes classification algorithms. The classifier is trained using a labeled dataset and then learns to distinguish between benign and malicious requests by assessing their computed probabilities. Conventional measures such as accuracy, precision, recall, and F1-score are employed to assess the model’s ability in correctly identifying SQL while reducing false positive classifications.The present study demonstrates the potential of Naive Bayes in enhancing online application security by providing a methodical and scalable strategy for identifying SQL injection attacks.

Image Inpainting of Portraits Artwork Design and Implementation
Zhang H.
In modern society, the restoration of artwork has become increasingly important. Generative models can provide reference images for the damaged or blurred core areas of these artworks. This paper simulates artificial damage to classic portrait paintings in the Art Portraits dataset by adding center masks during data preprocessing and then implements the image inpainting task. During the training phase, the Denoising Diffusion Probabilistic Model (DDPM) is fine-tuned by progressively adding noise to the center-masked images in the noising stage, followed by denoising in the denoising stage to generate images. The generated images are compared with the original undamaged images through loss calculations to optimize the model. Additionally, a Generative Adversarial Network (GAN), which has shown promising results on other datasets, is used as a baseline for comparison. The damaged images are used as inputs, and the generated images are compared to the ground truth to evaluate the performance of both models. In the testing phase, two widely used metrics in image evaluation, Mean Squared Error (MSE) and Fréchet Inception Distance (FID), are introduced to assess the performance. The fine-tuned DDPM achieves an MSE of 0.2622 and an FID of 16.85, while the GAN scores 0.2835 and 22.78, respectively. Since lower values indicate higher fidelity in reproducing the original image, which is crucial for art restoration, the conclusion drawn from this paper is that the fine-tuned DDPM demonstrates higher accuracy and is more suitable for restoration projects related to Art Portraits.
Top-100
Citing journals
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Bulletin of Experimental Biology and Medicine
9 citations, 5.66%
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Meditsinskiy sovet = Medical Council
6 citations, 3.77%
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Russian Ophthalmological Journal (Rossiiskii Oftal'mologicheskii Zhurnal)
6 citations, 3.77%
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Molekulyarnaya Meditsina (Molecular medicine)
4 citations, 2.52%
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Journal of Obstetrics and Women's Diseases
4 citations, 2.52%
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Medical alphabet
4 citations, 2.52%
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Bulletin of the Medical Institute REAVIZ (REHABILITATION DOCTOR AND HEALTH)
4 citations, 2.52%
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Russian Journal of Infection and Immunity
3 citations, 1.89%
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Siberian Journal of Oncology
3 citations, 1.89%
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Klinicheskaya Dermatologiya i Venerologiya
2 citations, 1.26%
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Biochemistry (Moscow)
2 citations, 1.26%
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Mathematical Biology and Bioinformatics
2 citations, 1.26%
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Russian Neurological Journal
2 citations, 1.26%
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Zhurnal Nevrologii i Psikhiatrii imeni S.S. Korsakova
2 citations, 1.26%
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Advances in Gerontology
2 citations, 1.26%
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International Journal of Molecular Sciences
2 citations, 1.26%
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Rossiyskiy Vestnik Perinatologii i Pediatrii
2 citations, 1.26%
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Cardiovascular Therapy and Prevention (Russian Federation)
2 citations, 1.26%
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Russian Journal of Cardiology
2 citations, 1.26%
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Medical Immunology (Russia)
2 citations, 1.26%
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Bulletin of Russian State Medical University
2 citations, 1.26%
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Medical Radiology and Radiation Safety
2 citations, 1.26%
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Life
2 citations, 1.26%
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Diabetes Mellitus
2 citations, 1.26%
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Биохимия
2 citations, 1.26%
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Advances in molecular oncology
2 citations, 1.26%
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Translational Medicine
2 citations, 1.26%
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Consilium Medicum
2 citations, 1.26%
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Experimental and Clinical Gastroenterology
2 citations, 1.26%
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Obstetrics Gynecology and Reproduction
2 citations, 1.26%
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Journal of oncology: diagnostic radiology and radiotherapy
2 citations, 1.26%
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Food Industry
2 citations, 1.26%
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Ulyanovsk Medico-biological Journal
2 citations, 1.26%
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Materials Science Forum
1 citation, 0.63%
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Molekulyarnaya Biologiya
1 citation, 0.63%
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Pirogov Russian Journal of Surgery = Khirurgiya. Zurnal im. N.I. Pirogova
1 citation, 0.63%
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Food Bioscience
1 citation, 0.63%
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Ecological Genetics
1 citation, 0.63%
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Gigiena i sanitariia
1 citation, 0.63%
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Farmatsiya i Farmakologiya
1 citation, 0.63%
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Genes and Cells
1 citation, 0.63%
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Vavilovskii Zhurnal Genetiki i Selektsii (Vavilov Journal of Genetics and Breeding)
1 citation, 0.63%
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LWT - Food Science and Technology
1 citation, 0.63%
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Acta Naturae
1 citation, 0.63%
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Nanomaterials
1 citation, 0.63%
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Uspekhi Fiziologicheskikh Nauk
1 citation, 0.63%
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Profilakticheskaya Meditsina
1 citation, 0.63%
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Oftalmologiya
1 citation, 0.63%
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HIV Infection and Immunosuppressive Disorders
1 citation, 0.63%
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Physical Biology
1 citation, 0.63%
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Kardiologiya
1 citation, 0.63%
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Bulletin of Siberian Medicine
1 citation, 0.63%
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Rational Pharmacotherapy in Cardiology
1 citation, 0.63%
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Voprosy kurortologii, fizioterapii, i lechebnoi fizicheskoi kultury
1 citation, 0.63%
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Antibiotiki i Khimioterapiya
1 citation, 0.63%
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Arkhiv Patologii
1 citation, 0.63%
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Russian Open Medical Journal
1 citation, 0.63%
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Biology
1 citation, 0.63%
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Biomedicines
1 citation, 0.63%
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Vestnik Rossiiskoi Akademii Meditsinskikh Nauk
1 citation, 0.63%
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Cells
1 citation, 0.63%
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Neurochemical Journal
1 citation, 0.63%
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Biology Bulletin Reviews
1 citation, 0.63%
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Problems of Biological Medical and Pharmaceutical Chemistry
1 citation, 0.63%
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Вестник Российской академии наук
1 citation, 0.63%
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Journal of Evolutionary Biochemistry and Physiology
1 citation, 0.63%
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Drug development & registration
1 citation, 0.63%
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Journal of microbiology epidemiology immunobiology
1 citation, 0.63%
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Pediatrician (St Petersburg)
1 citation, 0.63%
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Journal of clinical practice
1 citation, 0.63%
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Almanac of Clinical Medicine
1 citation, 0.63%
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Epidemiology and Infectious Diseases
1 citation, 0.63%
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Fundamental and Clinical Medicine
1 citation, 0.63%
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Psikhiatriya
1 citation, 0.63%
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Terapevt (General Physician)
1 citation, 0.63%
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The Bulletin of the Scientific Centre for Expert Evaluation of Medicinal Products Regulatory Research and Medicine Evaluation
1 citation, 0.63%
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Safety and Risk of Pharmacotherapy
1 citation, 0.63%
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Ophthalmology journal
1 citation, 0.63%
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Toxicological Review
1 citation, 0.63%
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Medico-Biological and Socio-Psychological Problems of Safety in Emergency Situations
1 citation, 0.63%
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Russian Journal of Occupational Health and Industrial Ecology (Meditsina truda i promyshlennaya ekologiya)
1 citation, 0.63%
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Farmaciya (Pharmacy)
1 citation, 0.63%
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Allergology and Immunology in Pediatrics
1 citation, 0.63%
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Biological Products Prevention Diagnosis Treatment
1 citation, 0.63%
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Journal of Anatomy and Histopathology
1 citation, 0.63%
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Russian Journal for Personalized Medicine
1 citation, 0.63%
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Russian Bulletin of Obstetrician-Gynecologist / Rossiyskii Vestnik Akushera-Ginekologa
1 citation, 0.63%
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Innovative medicine of Kuban
1 citation, 0.63%
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Scientific Notes of V I Vernadsky Crimean Federal University Biology Chemistry
1 citation, 0.63%
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South Russian Journal of Therapeutic Practice
1 citation, 0.63%
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Meditsinskaya sestra
1 citation, 0.63%
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Pediatric dentistry and dental prophylaxis
1 citation, 0.63%
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Medical Journal of the Russian Federation
1 citation, 0.63%
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Endodontics Today
1 citation, 0.63%
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Успехи современной биологии
1 citation, 0.63%
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Журнал эволюционной биохимии и физиологии
1 citation, 0.63%
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Health and Ecology Issues
1 citation, 0.63%
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Sanitarnyj vrač (Sanitary Doctor)
1 citation, 0.63%
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Fizioterapevt (Physiotherapist)
1 citation, 0.63%
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MORPHOLOGICAL NEWSLETTER
1 citation, 0.63%
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Citing publishers
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Eco-Vector LLC
10 citations, 6.29%
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Springer Nature
9 citations, 5.66%
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Pleiades Publishing
9 citations, 5.66%
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Media Sphere Publishing House
9 citations, 5.66%
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MDPI
8 citations, 5.03%
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Russian Vrach, Publishing House Ltd.
7 citations, 4.4%
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Remedium, Ltd.
6 citations, 3.77%
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Real Time, Ltd.
6 citations, 3.77%
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Silicea - Poligraf, LLC
5 citations, 3.14%
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Reaviz Medical University
5 citations, 3.14%
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The Russian Academy of Sciences
4 citations, 2.52%
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Alfmed LLC
4 citations, 2.52%
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Saint Petersburg Pasteur Institute
3 citations, 1.89%
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Tomsk Cancer Research Institute
3 citations, 1.89%
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Arterialnaya Gipertenziya
3 citations, 1.89%
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Medical Informational Agency Publishers
3 citations, 1.89%
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PANORAMA Publishing House
3 citations, 1.89%
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SCEEMP
3 citations, 1.89%
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Elsevier
2 citations, 1.26%
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Institute of Mathematical Problems of Biology of RAS (IMPB RAS)
2 citations, 1.26%
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National Academy of Pediatric Science and Innovation
2 citations, 1.26%
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Consilium Medicum
2 citations, 1.26%
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SPb RAACI
2 citations, 1.26%
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Pirogov Russian National Research Medical University
2 citations, 1.26%
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Publishing House ABV Press
2 citations, 1.26%
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Endocrinology Research Centre
2 citations, 1.26%
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IRBIS
2 citations, 1.26%
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Burnasyan Federal Medical Biophysical Center Of Federal Medical Biological Agency
2 citations, 1.26%
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Federal Scientific Center for Hygiene F.F.Erisman
2 citations, 1.26%
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LLC Global Media Technology
2 citations, 1.26%
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Non-profit partnership Society of Interventional Oncoradiologists
2 citations, 1.26%
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Ural State University of Economics
2 citations, 1.26%
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Ulyanovsk State University
2 citations, 1.26%
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Trans Tech Publications
1 citation, 0.63%
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IOP Publishing
1 citation, 0.63%
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PJSC Human Stem Cells Institute
1 citation, 0.63%
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PE Polunina Elizareta Gennadievna
1 citation, 0.63%
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Baltic Medical Education Center
1 citation, 0.63%
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Siberian State Medical University
1 citation, 0.63%
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LLC Science and Innovations
1 citation, 0.63%
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Akademizdatcenter Nauka
1 citation, 0.63%
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Center of Pharmaceutical Analytics Ltd
1 citation, 0.63%
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Central Research Institute for Epidemiology
1 citation, 0.63%
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Volgograd State Medical University
1 citation, 0.63%
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Moscow Regional Research and Clinical Institute (MONIKI)
1 citation, 0.63%
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Kemerovo State Medical University
1 citation, 0.63%
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Paediatrician Publishers LLC
1 citation, 0.63%
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Institute of Cytology and Genetics SB RAS
1 citation, 0.63%
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Acta Naturae Ltd
1 citation, 0.63%
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RIOR Publishing Center
1 citation, 0.63%
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Publishing House OKI
1 citation, 0.63%
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NRCERM EMERCOM of Russia
1 citation, 0.63%
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APO Society of Specialists in Heart Failure
1 citation, 0.63%
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FSBI Research Institute of Occupational Health RAMS
1 citation, 0.63%
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Association of Pediatric Allergologists and Immunologists of Russia
1 citation, 0.63%
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Rostov State Medical University
1 citation, 0.63%
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VSMU N.N. Burdenko
1 citation, 0.63%
|
|
Periodontal Association - RPA
1 citation, 0.63%
|
|
Scientific Research Institute - Ochapovsky Regional Clinical Hospital No 1
1 citation, 0.63%
|
|
ООО "Эндо Пресс"
1 citation, 0.63%
|
|
Gomel State Medical University
1 citation, 0.63%
|
|
Show all (31 more) | |
2
4
6
8
10
|
Publishing organizations
5
10
15
20
25
|
|
Saint-Petersburg Research Institute of Phthisiopulmonology
21 publications, 6.23%
|
|
Lomonosov Moscow State University
19 publications, 5.64%
|
|
Pavlov Institute of Physiology of the Russian Academy of Sciences
18 publications, 5.34%
|
|
Saint Petersburg State University
15 publications, 4.45%
|
|
Sechenov First Moscow State Medical University
9 publications, 2.67%
|
|
Saint Petersburg State Pediatric Medical University
9 publications, 2.67%
|
|
Belgorod State University
8 publications, 2.37%
|
|
Voino-Yasenetsky Krasnoyarsk State Medical University
8 publications, 2.37%
|
|
Pirogov Russian National Research Medical University
7 publications, 2.08%
|
|
Peoples' Friendship University of Russia
6 publications, 1.78%
|
|
Samara State Medical University
6 publications, 1.78%
|
|
Astrakhan State Medical University
6 publications, 1.78%
|
|
Russian University of Medicine
5 publications, 1.48%
|
|
Chita State Medical Academy
5 publications, 1.48%
|
|
Immanuel Kant Baltic Federal University
4 publications, 1.19%
|
|
First Pavlov State Medical University of St. Petersburg
4 publications, 1.19%
|
|
N.N. Blokhin National Medical Research Center of Oncology
4 publications, 1.19%
|
|
Siberian State Medical University
4 publications, 1.19%
|
|
Tomsk National Research Medical Center of the Russian Academy of Sciences
4 publications, 1.19%
|
|
Serbsky National Medical Research Center for Psychiatry and Narcology
4 publications, 1.19%
|
|
Russian Medical Academy of Continuous Professional Education
4 publications, 1.19%
|
|
Federal Scientific and Clinical Center for Specialized Types of Medical Care and Medical Technologies, FMBA of Russia
4 publications, 1.19%
|
|
National Research Nuclear University MEPhI
3 publications, 0.89%
|
|
Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences
3 publications, 0.89%
|
|
Herzen State Pedagogical University of Russia
3 publications, 0.89%
|
|
Bashkir State Medical University
3 publications, 0.89%
|
|
FSBI «Petrov Research Institute of Oncology» of the Ministry of Healthcare of the Russian Federation
3 publications, 0.89%
|
|
Federal Research Center for Innovator and Emerging Biomedical and Pharmaceutical Technologies
3 publications, 0.89%
|
|
Ryazan State Medical University named after Academician I.P. Pavlov
3 publications, 0.89%
|
|
Izhevsk State Medical Academy
3 publications, 0.89%
|
|
![]() Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry of the Russian Academy of Sciences
2 publications, 0.59%
|
|
Institute of Cytology of the Russian Academy of Sciences
2 publications, 0.59%
|
|
Institute of Chemical Biology and Fundamental Medicine of the Siberian Branch of the Russian Academy of Sciences
2 publications, 0.59%
|
|
Prokhorov General Physics Institute of the Russian Academy of Sciences
2 publications, 0.59%
|
|
Institute of Precision Mechanics and Control SarSC of the Russian Academy of Sciences
2 publications, 0.59%
|
|
Tomsk State University
2 publications, 0.59%
|
|
National Research Tomsk Polytechnic University
2 publications, 0.59%
|
|
Bach Institute of Biochemistry of the Russian Academy of Sciences
2 publications, 0.59%
|
|
![]() Federal Research Centre “Fundamentals of Biotechnology” of the Russian Academy of Sciences
2 publications, 0.59%
|
|
Volgograd State Medical University
2 publications, 0.59%
|
|
Saratov State University
2 publications, 0.59%
|
|
Saratov State Medical University named after V. I. Razumovsky
2 publications, 0.59%
|
|
N. F. Gamaleya National Research Center for Epidemiology and Microbiology of the Ministry of Health of the Russian Federation
2 publications, 0.59%
|
|
Rostov State Medical University
2 publications, 0.59%
|
|
E.A. Vagner Perm State Medical University
2 publications, 0.59%
|
|
Institute of Biochemistry and Genetics of the Ufa Federal Research Center of the Russian Academy of Sciences
2 publications, 0.59%
|
|
National Medical Research Center of Neurosurgery named after N.N. Burdenko
2 publications, 0.59%
|
|
Federal Research and Clinical Center of Physical-Chemical Medicine of Federal Medical Biological Agency
2 publications, 0.59%
|
|
I.I. Mechnikov Scientific Research Institute of Vaccines and Serums
2 publications, 0.59%
|
|
D.O. Ott Research Institute of Obstetrics, Gynecology and Reproductology
2 publications, 0.59%
|
|
Kirov Military Medical Academy
2 publications, 0.59%
|
|
A.S. Loginov Moscow Clinical Scientific Centre
2 publications, 0.59%
|
|
Uppsala University
2 publications, 0.59%
|
|
National Research University Higher School of Economics
1 publication, 0.3%
|
|
Institute of Gene Biology of the Russian Academy of Sciences
1 publication, 0.3%
|
|
Institute of Molecular Genetics of NRC «Kurchatov Institute»
1 publication, 0.3%
|
|
Kazan Federal University
1 publication, 0.3%
|
|
ITMO University
1 publication, 0.3%
|
|
Ural Federal University
1 publication, 0.3%
|
|
Far Eastern Federal University
1 publication, 0.3%
|
|
Novosibirsk State University
1 publication, 0.3%
|
|
Peter the Great St. Petersburg Polytechnic University
1 publication, 0.3%
|
|
Siberian Federal University
1 publication, 0.3%
|
|
V. M. Gorbatov Federal Research Center for Food Systems of Russian Academy of Sciences
1 publication, 0.3%
|
|
A.P. Avtsyn Research Institute of Human Morphology
1 publication, 0.3%
|
|
National Research Centre "Kurchatov Institute"
1 publication, 0.3%
|
|
Almazov National Medical Research Centre
1 publication, 0.3%
|
|
Kazan State Medical University
1 publication, 0.3%
|
|
Ogarev Mordovia State University
1 publication, 0.3%
|
|
![]() Institute of Higher Nervous Activity and Neurophysiology of the Russian Academy of Sciences
1 publication, 0.3%
|
|
Novosibirsk State Medical University
1 publication, 0.3%
|
|
Kemerovo Cardiology Center
1 publication, 0.3%
|
|
Tambov State University named after G.R. Derzhavin
1 publication, 0.3%
|
|
Endocrinology Research Centre
1 publication, 0.3%
|
|
Institute of Bioorganic Chemistry of the National Academy of Sciences of Belarus
1 publication, 0.3%
|
|
Institute of Genetics and Cytology of the National Academy of Sciences of Belarus
1 publication, 0.3%
|
|
Belarusian State Medical University
1 publication, 0.3%
|
|
Saint-Petersburg State Chemical and Pharmaceutical University
1 publication, 0.3%
|
|
Institute of General Pathology and Pathophysiology
1 publication, 0.3%
|
|
Dmitry Rogachev National Research Center of Pediatric Hematology, Oncology and Immunology
1 publication, 0.3%
|
|
Meshalkin National Medical Research Center
1 publication, 0.3%
|
|
National Medical Research Center of Cardiology
1 publication, 0.3%
|
|
Federal Research Center of Fundamental and Translational Medicine
1 publication, 0.3%
|
|
National Research Center Institute of Immunology of the Federal Medical Biological Agency of Russia
1 publication, 0.3%
|
|
Institute of Cytochemistry and Molecular Pharmacology
1 publication, 0.3%
|
|
Federal Medical Biophysical Center named after A.I. Burnazyan
1 publication, 0.3%
|
|
Center for Theoretical Problems of Physicochemical Pharmacology of the Russian Academy of Sciences
1 publication, 0.3%
|
|
Ivanovo State Agricultural Academy named after D.K. Belyaev
1 publication, 0.3%
|
|
Research Institute of Clinical and Experimental Lymphology ICG of the Siberian Branch of the Russian Academy of Sciences
1 publication, 0.3%
|
|
The Nikiforov Russian Center of Emergency and Radiation Medicine
1 publication, 0.3%
|
|
Altai State Medical University
1 publication, 0.3%
|
|
Southern Scientific Center of the Russian Academy of Sciences
1 publication, 0.3%
|
|
Sclifosovsky Research Institute for Emergency Medicine
1 publication, 0.3%
|
|
Udmurt federal research center of the Ural Branch of the Russian Academy of Sciences
1 publication, 0.3%
|
|
Ufa Federal Research Center of the Russian Academy of Sciences
1 publication, 0.3%
|
|
Perm Federal Research Center of the Ural Branch of the Russian Academy of Sciences
1 publication, 0.3%
|
|
Russian Biotechnological University
1 publication, 0.3%
|
|
North-Western State Medical University named after I.I. Mechnikov
1 publication, 0.3%
|
|
Scientific Institution Research Institute of Medical Primatology of NRC «Kurchatov Institute»
1 publication, 0.3%
|
|
Yaroslavl State Medical University
1 publication, 0.3%
|
|
Show all (70 more) | |
5
10
15
20
25
|
Publishing organizations in 5 years
5
10
15
20
25
|
|
Saint-Petersburg Research Institute of Phthisiopulmonology
21 publications, 9.77%
|
|
Lomonosov Moscow State University
19 publications, 8.84%
|
|
Pavlov Institute of Physiology of the Russian Academy of Sciences
18 publications, 8.37%
|
|
Saint Petersburg State University
15 publications, 6.98%
|
|
Sechenov First Moscow State Medical University
9 publications, 4.19%
|
|
Saint Petersburg State Pediatric Medical University
9 publications, 4.19%
|
|
Belgorod State University
8 publications, 3.72%
|
|
Voino-Yasenetsky Krasnoyarsk State Medical University
8 publications, 3.72%
|
|
Pirogov Russian National Research Medical University
7 publications, 3.26%
|
|
Peoples' Friendship University of Russia
6 publications, 2.79%
|
|
Samara State Medical University
6 publications, 2.79%
|
|
Astrakhan State Medical University
6 publications, 2.79%
|
|
Russian University of Medicine
5 publications, 2.33%
|
|
Chita State Medical Academy
5 publications, 2.33%
|
|
Immanuel Kant Baltic Federal University
4 publications, 1.86%
|
|
First Pavlov State Medical University of St. Petersburg
4 publications, 1.86%
|
|
N.N. Blokhin National Medical Research Center of Oncology
4 publications, 1.86%
|
|
Siberian State Medical University
4 publications, 1.86%
|
|
Tomsk National Research Medical Center of the Russian Academy of Sciences
4 publications, 1.86%
|
|
Serbsky National Medical Research Center for Psychiatry and Narcology
4 publications, 1.86%
|
|
Russian Medical Academy of Continuous Professional Education
4 publications, 1.86%
|
|
Federal Scientific and Clinical Center for Specialized Types of Medical Care and Medical Technologies, FMBA of Russia
4 publications, 1.86%
|
|
National Research Nuclear University MEPhI
3 publications, 1.4%
|
|
Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences
3 publications, 1.4%
|
|
Herzen State Pedagogical University of Russia
3 publications, 1.4%
|
|
Bashkir State Medical University
3 publications, 1.4%
|
|
FSBI «Petrov Research Institute of Oncology» of the Ministry of Healthcare of the Russian Federation
3 publications, 1.4%
|
|
Federal Research Center for Innovator and Emerging Biomedical and Pharmaceutical Technologies
3 publications, 1.4%
|
|
Ryazan State Medical University named after Academician I.P. Pavlov
3 publications, 1.4%
|
|
Izhevsk State Medical Academy
3 publications, 1.4%
|
|
![]() Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry of the Russian Academy of Sciences
2 publications, 0.93%
|
|
Institute of Cytology of the Russian Academy of Sciences
2 publications, 0.93%
|
|
Institute of Chemical Biology and Fundamental Medicine of the Siberian Branch of the Russian Academy of Sciences
2 publications, 0.93%
|
|
Prokhorov General Physics Institute of the Russian Academy of Sciences
2 publications, 0.93%
|
|
Institute of Precision Mechanics and Control SarSC of the Russian Academy of Sciences
2 publications, 0.93%
|
|
Tomsk State University
2 publications, 0.93%
|
|
National Research Tomsk Polytechnic University
2 publications, 0.93%
|
|
Bach Institute of Biochemistry of the Russian Academy of Sciences
2 publications, 0.93%
|
|
![]() Federal Research Centre “Fundamentals of Biotechnology” of the Russian Academy of Sciences
2 publications, 0.93%
|
|
Volgograd State Medical University
2 publications, 0.93%
|
|
Saratov State University
2 publications, 0.93%
|
|
Saratov State Medical University named after V. I. Razumovsky
2 publications, 0.93%
|
|
N. F. Gamaleya National Research Center for Epidemiology and Microbiology of the Ministry of Health of the Russian Federation
2 publications, 0.93%
|
|
Rostov State Medical University
2 publications, 0.93%
|
|
E.A. Vagner Perm State Medical University
2 publications, 0.93%
|
|
Institute of Biochemistry and Genetics of the Ufa Federal Research Center of the Russian Academy of Sciences
2 publications, 0.93%
|
|
National Medical Research Center of Neurosurgery named after N.N. Burdenko
2 publications, 0.93%
|
|
Federal Research and Clinical Center of Physical-Chemical Medicine of Federal Medical Biological Agency
2 publications, 0.93%
|
|
I.I. Mechnikov Scientific Research Institute of Vaccines and Serums
2 publications, 0.93%
|
|
D.O. Ott Research Institute of Obstetrics, Gynecology and Reproductology
2 publications, 0.93%
|
|
Kirov Military Medical Academy
2 publications, 0.93%
|
|
A.S. Loginov Moscow Clinical Scientific Centre
2 publications, 0.93%
|
|
Uppsala University
2 publications, 0.93%
|
|
National Research University Higher School of Economics
1 publication, 0.47%
|
|
Institute of Gene Biology of the Russian Academy of Sciences
1 publication, 0.47%
|
|
Institute of Molecular Genetics of NRC «Kurchatov Institute»
1 publication, 0.47%
|
|
Kazan Federal University
1 publication, 0.47%
|
|
ITMO University
1 publication, 0.47%
|
|
Ural Federal University
1 publication, 0.47%
|
|
Far Eastern Federal University
1 publication, 0.47%
|
|
Novosibirsk State University
1 publication, 0.47%
|
|
Peter the Great St. Petersburg Polytechnic University
1 publication, 0.47%
|
|
Siberian Federal University
1 publication, 0.47%
|
|
V. M. Gorbatov Federal Research Center for Food Systems of Russian Academy of Sciences
1 publication, 0.47%
|
|
A.P. Avtsyn Research Institute of Human Morphology
1 publication, 0.47%
|
|
National Research Centre "Kurchatov Institute"
1 publication, 0.47%
|
|
Almazov National Medical Research Centre
1 publication, 0.47%
|
|
Kazan State Medical University
1 publication, 0.47%
|
|
Ogarev Mordovia State University
1 publication, 0.47%
|
|
Institute of Higher Nervous Activity and Neurophysiology of the Russian Academy of Sciences
1 publication, 0.47%
|
|
Novosibirsk State Medical University
1 publication, 0.47%
|
|
Kemerovo Cardiology Center
1 publication, 0.47%
|
|
Tambov State University named after G.R. Derzhavin
1 publication, 0.47%
|
|
Endocrinology Research Centre
1 publication, 0.47%
|
|
Institute of Bioorganic Chemistry of the National Academy of Sciences of Belarus
1 publication, 0.47%
|
|
Institute of Genetics and Cytology of the National Academy of Sciences of Belarus
1 publication, 0.47%
|
|
Belarusian State Medical University
1 publication, 0.47%
|
|
Saint-Petersburg State Chemical and Pharmaceutical University
1 publication, 0.47%
|
|
Institute of General Pathology and Pathophysiology
1 publication, 0.47%
|
|
Dmitry Rogachev National Research Center of Pediatric Hematology, Oncology and Immunology
1 publication, 0.47%
|
|
Meshalkin National Medical Research Center
1 publication, 0.47%
|
|
National Medical Research Center of Cardiology
1 publication, 0.47%
|
|
Federal Research Center of Fundamental and Translational Medicine
1 publication, 0.47%
|
|
National Research Center Institute of Immunology of the Federal Medical Biological Agency of Russia
1 publication, 0.47%
|
|
Institute of Cytochemistry and Molecular Pharmacology
1 publication, 0.47%
|
|
Federal Medical Biophysical Center named after A.I. Burnazyan
1 publication, 0.47%
|
|
Center for Theoretical Problems of Physicochemical Pharmacology of the Russian Academy of Sciences
1 publication, 0.47%
|
|
Ivanovo State Agricultural Academy named after D.K. Belyaev
1 publication, 0.47%
|
|
Research Institute of Clinical and Experimental Lymphology ICG of the Siberian Branch of the Russian Academy of Sciences
1 publication, 0.47%
|
|
The Nikiforov Russian Center of Emergency and Radiation Medicine
1 publication, 0.47%
|
|
Altai State Medical University
1 publication, 0.47%
|
|
Southern Scientific Center of the Russian Academy of Sciences
1 publication, 0.47%
|
|
Sclifosovsky Research Institute for Emergency Medicine
1 publication, 0.47%
|
|
Udmurt federal research center of the Ural Branch of the Russian Academy of Sciences
1 publication, 0.47%
|
|
Ufa Federal Research Center of the Russian Academy of Sciences
1 publication, 0.47%
|
|
Perm Federal Research Center of the Ural Branch of the Russian Academy of Sciences
1 publication, 0.47%
|
|
Russian Biotechnological University
1 publication, 0.47%
|
|
North-Western State Medical University named after I.I. Mechnikov
1 publication, 0.47%
|
|
Scientific Institution Research Institute of Medical Primatology of NRC «Kurchatov Institute»
1 publication, 0.47%
|
|
Yaroslavl State Medical University
1 publication, 0.47%
|
|
Show all (70 more) | |
5
10
15
20
25
|
Publishing countries
20
40
60
80
100
120
140
160
|
|
Russia
|
Russia, 149, 44.21%
Russia
149 publications, 44.21%
|
Belarus
|
Belarus, 2, 0.59%
Belarus
2 publications, 0.59%
|
Sweden
|
Sweden, 2, 0.59%
Sweden
2 publications, 0.59%
|
USA
|
USA, 1, 0.3%
USA
1 publication, 0.3%
|
Vietnam
|
Vietnam, 1, 0.3%
Vietnam
1 publication, 0.3%
|
Kyrgyzstan
|
Kyrgyzstan, 1, 0.3%
Kyrgyzstan
1 publication, 0.3%
|
20
40
60
80
100
120
140
160
|
Publishing countries in 5 years
20
40
60
80
100
120
140
160
|
|
Russia
|
Russia, 149, 69.3%
Russia
149 publications, 69.3%
|
Belarus
|
Belarus, 2, 0.93%
Belarus
2 publications, 0.93%
|
Sweden
|
Sweden, 2, 0.93%
Sweden
2 publications, 0.93%
|
USA
|
USA, 1, 0.47%
USA
1 publication, 0.47%
|
Vietnam
|
Vietnam, 1, 0.47%
Vietnam
1 publication, 0.47%
|
Kyrgyzstan
|
Kyrgyzstan, 1, 0.47%
Kyrgyzstan
1 publication, 0.47%
|
20
40
60
80
100
120
140
160
|
5 profile journal articles
Kleimenova Tatiana
14 publications,
17 citations
h-index: 3
3 profile journal articles
Dvornikova Kristina
🤝
Pavlov Institute of Physiology of the Russian Academy of Sciences
11 publications,
85 citations
h-index: 5
Research interests
Evolutionary biology
Immunology
Molecular biology
Molecular genetics
Molecular phylogenetics
3 profile journal articles
Bystrova Elena
PhD in Biological/biomedical sciences

Pavlov Institute of Physiology of the Russian Academy of Sciences
14 publications,
86 citations
h-index: 5
Research interests
Biophysics
Immunology
Molecular biology
Molecular genetics
2 profile journal articles
Deyev Sergey
37 publications
h-index: 0
1 profile journal article
Kovaleva Olga

N.N. Blokhin National Medical Research Center of Oncology
71 publications,
343 citations
h-index: 10
1 profile journal article
Nikolaev Cergej

Federal Research Center for Innovator and Emerging Biomedical and Pharmaceutical Technologies
44 publications,
80 citations
h-index: 4
Research interests
Biochemistry
Cell biology
Molecular biology
Pharmacology
1 profile journal article
Kuzmin Egor

Sechenov First Moscow State Medical University
11 publications,
34 citations
h-index: 3
1 profile journal article
Sokolovich Evgenii
DSc, Professor

National Medical Research Center of Phthisiopulmonology and Infectious Diseases
43 publications,
138 citations
h-index: 6
1 profile journal article
Valieva Yana
4 publications,
139 citations
h-index: 2