М. В. Самсонова

761 total citations
54 papers, 437 citations indexed

About

М. В. Самсонова is a scholar working on Pulmonary and Respiratory Medicine, Infectious Diseases and Molecular Biology. According to data from OpenAlex, М. В. Самсонова has authored 54 papers receiving a total of 437 indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Pulmonary and Respiratory Medicine, 9 papers in Infectious Diseases and 8 papers in Molecular Biology. Recurrent topics in М. В. Самсонова's work include Interstitial Lung Diseases and Idiopathic Pulmonary Fibrosis (13 papers), COVID-19 Clinical Research Studies (9 papers) and Chronic Obstructive Pulmonary Disease (COPD) Research (9 papers). М. В. Самсонова is often cited by papers focused on Interstitial Lung Diseases and Idiopathic Pulmonary Fibrosis (13 papers), COVID-19 Clinical Research Studies (9 papers) and Chronic Obstructive Pulmonary Disease (COPD) Research (9 papers). М. В. Самсонова collaborates with scholars based in Russia, United States and Taiwan. М. В. Самсонова's co-authors include John Reinitz, Ivan A. Pisarev, Konstantin Kozlov, Ekaterina Myasnikova, Svetlana Surkova, Roland Schafleitner, Л.М. Михалева, Eric von Wettberg, Sergey V. Nuzhdin and Cheng‐Ruei Lee and has published in prestigious journals such as Nucleic Acids Research, SHILAP Revista de lepidopterología and Bioinformatics.

In The Last Decade

М. В. Самсонова

44 papers receiving 424 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
М. В. Самсонова Russia 12 172 95 80 59 55 54 437
Maia Dorsett United States 9 170 1.0× 75 0.8× 34 0.4× 12 0.2× 22 0.4× 18 456
Tracey Williams United States 9 61 0.4× 39 0.4× 31 0.4× 24 0.4× 18 0.3× 18 354
Yifei Shen China 11 117 0.7× 35 0.4× 111 1.4× 9 0.2× 23 0.4× 25 397
Lauren Reoma United States 7 87 0.5× 23 0.2× 62 0.8× 52 0.9× 92 1.7× 14 482
E.Chung-Chin Cheng United States 11 209 1.2× 40 0.4× 62 0.8× 8 0.1× 145 2.6× 12 464
Mikhail Pomaznoy United States 8 243 1.4× 13 0.1× 54 0.7× 11 0.2× 36 0.7× 11 408
Lanyn P. Taliaferro United States 11 104 0.6× 88 0.9× 13 0.2× 10 0.2× 23 0.4× 23 293
Jez L. Marston United States 10 135 0.8× 15 0.2× 96 1.2× 10 0.2× 33 0.6× 15 242
Siyuan Hao United States 11 158 0.9× 88 0.9× 7 0.1× 13 0.2× 100 1.8× 24 372

Countries citing papers authored by М. В. Самсонова

Since Specialization
Citations

This map shows the geographic impact of М. В. Самсонова's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by М. В. Самсонова with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites М. В. Самсонова more than expected).

Fields of papers citing papers by М. В. Самсонова

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by М. В. Самсонова. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by М. В. Самсонова. The network helps show where М. В. Самсонова may publish in the future.

Co-authorship network of co-authors of М. В. Самсонова

This figure shows the co-authorship network connecting the top 25 collaborators of М. В. Самсонова. A scholar is included among the top collaborators of М. В. Самсонова based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with М. В. Самсонова. М. В. Самсонова is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Трушенко, Н. В., Olga Suvorova, S. Yu. Chikina, et al.. (2025). Retrospective study of predictors of pulmonary fibrosis progression in hypersensitivity pneumonitis. Meditsinskiy sovet = Medical Council. 67–76.
2.
Авдеев, С. Н., З. Р. Айсанов, А. A. Vizel, et al.. (2025). Federal clinical guidelines on diagnosis and treatment of hypersensitivity pneumonitis. PULMONOLOGIYA. 35(1). 16–41.
3.
Самсонова, М. В., et al.. (2024). Modeling Chickpea Productivity with Artificial Image Objects and Convolutional Neural Network. Plants. 13(17). 2444–2444. 1 indexed citations
4.
Самсонова, М. В., et al.. (2024). Clinical and morphological features of lung injury long-term after SARS-CoV-2 recovery. Terapevticheskii arkhiv. 96(3). 218–227. 1 indexed citations
5.
Трушенко, Н. В., Olga Suvorova, S. Yu. Chikina, et al.. (2023). Predictors of Progression and Mortality in Patients with Chronic Hypersensitivity Pneumonitis: Retrospective Analysis of Registry of Fibrosing Interstitial Lung Diseases. Life. 13(2). 467–467. 11 indexed citations
6.
Lee, Cheng‐Ruei, Roland Schafleitner, Eric von Wettberg, et al.. (2022). Modeling of Flowering Time in Vigna radiata with Artificial Image Objects, Convolutional Neural Network and Random Forest. Plants. 11(23). 3327–3327. 3 indexed citations
7.
Самсонова, М. В., et al.. (2022). Comparative pathomorphological characteristics of idiopathic pulmonary fibrosis and fibrotic hypersensitivity pneumonitis. Russian Journal of Archive of Pathology. 84(1). 59–59.
8.
Авдеев, С. Н., З. Р. Айсанов, А. S. Belevskiy, et al.. (2022). Federal clinical guidelines on diagnosis and treatment of idiopathic pulmonary fibrosis. PULMONOLOGIYA. 32(3). 473–495. 7 indexed citations
9.
Lee, Cheng‐Ruei, Chau‐Ti Ting, Roland Schafleitner, et al.. (2021). Modeling of Flowering Time in Vigna radiata with Approximate Bayesian Computation. Agronomy. 11(11). 2317–2317. 2 indexed citations
10.
Самсонова, М. В., et al.. (2021). Anatomic pathology and computed tomography of diffuse cystic lung diseases. 10(S4). 23–33.
11.
Самсонова, М. В., et al.. (2021). Solution of Mixed-Integer Optimization Problems in Bioinformatics with Differential Evolution Method. Mathematics. 9(24). 3329–3329. 1 indexed citations
12.
Самсонова, М. В., et al.. (2021). Long-term pathological changes in lungs after COVID-19. PULMONOLOGIYA. 31(5). 571–579. 9 indexed citations
13.
Zayrаtyants, O. V., et al.. (2020). COVID-19 pathology: experience of 2000 autopsies. Russian Journal of Forensic Medicine. 6(4). 10–23. 7 indexed citations
14.
Ковригина, А. М., et al.. (2020). Pathomorphological and immunohistochemical features of lymph nodes in COVID-19 patients (autopsy study). 9(4). 12–23. 6 indexed citations
15.
Самсонова, М. В., et al.. (2020). Features of pathological anatomy of lungs at COVID-19. PULMONOLOGIYA. 30(5). 519–532. 17 indexed citations
16.
Burlyaeva, М. О., M. A. Vishnyаkova, Cheng‐Ruei Lee, et al.. (2019). Collections of Mungbean [Vigna radiata) (L.) R. Wilczek] and urdbean [V. mungo (L.) Hepper] in Vavilov Institute (VIR): traits diversity and trends in the breeding process over the last 100 years. Genetic Resources and Crop Evolution. 66(4). 767–781. 11 indexed citations
17.
Чучалин, А. Г., et al.. (2018). The first lung transplantation at the Research Institute for Emergency named after N.V. Sklifosovsky. SHILAP Revista de lepidopterología. 1 indexed citations
18.
Самсонова, М. В., et al.. (2014). About interstitial pneumonias: answers to Prof. M.M.Il'kovich's questions and discussion. PULMONOLOGIYA. 111–113.
19.
Самсонова, М. В., et al.. (2013). Different types of chronic obstructive pulmonary disease in term of pathologist's view. PULMONOLOGIYA. 93–96. 2 indexed citations
20.
Chikina, S. Yu., et al.. (2012). Lipoid pneumonia: case reports. PULMONOLOGIYA. 116–121. 1 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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