Ruslan Sharipov

2.3k total citations · 1 hit paper
36 papers, 1.2k citations indexed

About

Ruslan Sharipov is a scholar working on Molecular Biology, Health, Toxicology and Mutagenesis and Pediatrics, Perinatology and Child Health. According to data from OpenAlex, Ruslan Sharipov has authored 36 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Molecular Biology, 8 papers in Health, Toxicology and Mutagenesis and 5 papers in Pediatrics, Perinatology and Child Health. Recurrent topics in Ruslan Sharipov's work include Genomics and Chromatin Dynamics (8 papers), Air Quality and Health Impacts (7 papers) and RNA modifications and cancer (6 papers). Ruslan Sharipov is often cited by papers focused on Genomics and Chromatin Dynamics (8 papers), Air Quality and Health Impacts (7 papers) and RNA modifications and cancer (6 papers). Ruslan Sharipov collaborates with scholars based in Russia, Germany and Czechia. Ruslan Sharipov's co-authors include Fedor Kolpakov, Ivan Yevshin, Vsevolod J. Makeev, Ivan V. Kulakovskiy, Alexander Kel, Yury V. Kondrakhin, Semyon Kolmykov, Ilya E. Vorontsov, Alla D. Fedorova and Vladimir B. Bajić and has published in prestigious journals such as Nucleic Acids Research, SHILAP Revista de lepidopterología and PLoS ONE.

In The Last Decade

Ruslan Sharipov

34 papers receiving 1.2k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

HOCOMOCO: towards a complete collection of transcription factor binding models for human and mouse via large-scale ChIP-Seq analysis

2017 500 citations Ivan V. Kulakovskiy, Ilya E. Vorontsov et al. Nucleic Acids Research profile →

Peers

Countries citing papers authored by Ruslan Sharipov

Since Specialization

Citations

This map shows the geographic impact of Ruslan Sharipov'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 Ruslan Sharipov with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ruslan Sharipov more than expected).

Fields of papers citing papers by Ruslan Sharipov

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ruslan Sharipov. 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 Ruslan Sharipov. The network helps show where Ruslan Sharipov may publish in the future.

Co-authorship network of co-authors of Ruslan Sharipov

This figure shows the co-authorship network connecting the top 25 collaborators of Ruslan Sharipov. A scholar is included among the top collaborators of Ruslan Sharipov 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 Ruslan Sharipov. Ruslan Sharipov is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

#	Work	Indexed citations
1	Mathematical modelling of the influence of ACE I/D polymorphism on blood pressure and antihypertensive therapy 2024 ·Heliyon ·(unknown), (unknown), Ruslan Sharipov, (unknown), Fedor Kolpakov	3
2	Mathematical modeling of antihypertensive therapy 2022 ·Frontiers in Physiology ·(unknown), Ilya Kiselev, Ruslan Sharipov, (unknown), Fedor Kolpakov	8
3	Modular Representation of Physiologically Based Pharmacokinetic Models: Nanoparticle Delivery to Solid Tumors in Mice as an Example 2022 ·Mathematics ·(unknown), Ilya R. Akberdin, Ilya Kiselev, Ruslan Sharipov, Fedor Kolpakov	4
4	A Modular Mathematical Model of Exercise-Induced Changes in Metabolism, Signaling, and Gene Expression in Human Skeletal Muscle 2021 ·International Journal of Molecular Sciences ·Ilya R. Akberdin, Ilya Kiselev, (unknown), Ruslan Sharipov, (unknown), О. Л. Виноградова, Daniil V. Popov, Fedor Kolpakov	10
5	Thoroughly Calibrated Modular Agent-Based Model of the Human Cardiovascular and Renal Systems for Blood Pressure Regulation in Health and Disease 2021 ·Frontiers in Physiology ·(unknown), Ilya Kiselev, Ruslan Sharipov, (unknown), Fedor Kolpakov	8
6	Meta-analysis of ChIP-seq Datasets Through the Rank Aggregation Approach 2020 ·Semyon Kolmykov, Yury V. Kondrakhin, Ruslan Sharipov, (unknown), Anna S. Ryabova, Fedor Kolpakov	1
7	Population size estimation for quality control of ChIP-Seq datasets 2019 ·PLoS ONE ·Semyon Kolmykov, Yury V. Kondrakhin, Ivan Yevshin, Ruslan Sharipov, Anna S. Ryabova, Fedor Kolpakov	3
8	THE ROLE OF AIR POLLUTION BY THE PARTICULATE MATTER IN PATHOGENESIS OF THE CHRONIC NONINFECTIOUS DISEASES 2018 ·(unknown), Ruslan Sharipov, Fedor Kolpakov	2
9	Genome-wide map of human and mouse transcription factor binding sites aggregated from ChIP-Seq data 2018 ·BMC Research Notes ·Ilya E. Vorontsov, Alla D. Fedorova, Ivan Yevshin, Ruslan Sharipov, Fedor Kolpakov, Vsevolod J. Makeev, Ivan V. Kulakovskiy	19
10	Computational master-regulator search reveals mTOR and PI3K pathways responsible for low sensitivity of NCI-H292 and A427 lung cancer cell lines to cytotoxic action of p53 activator Nutlin-3 2018 ·BMC Medical Genomics ·U. A. Boyarskikh, (unknown), (unknown), (unknown), Ilya Kiselev, (unknown), Ruslan Sharipov, Philip Stegmaier, Fedor Kolpakov, М. Л. Филипенко, Alexander Kel	12
11	About a role of particle pollution for pathogenesis of chronic lung diseases 2017 ·PULMONOLOGIYA ·(unknown), Ruslan Sharipov, Fedor Kolpakov	4
12	HOCOMOCO: towards a complete collection of transcription factor binding models for human and mouse via large-scale ChIP-Seq analysis breakdown → 2017 ·Nucleic Acids Research ·Ivan V. Kulakovskiy, Ilya E. Vorontsov, Ivan Yevshin, Ruslan Sharipov, Alla D. Fedorova, (unknown), Yulia A. Medvedeva, Arturo Magana-Mora, Vladimir B. Bajić, (unknown), Fedor Kolpakov, Vsevolod J. Makeev	500
13	GTRD: a database of transcription factor binding sites identified by ChIP-seq experiments 2016 ·Nucleic Acids Research ·Ivan Yevshin, Ruslan Sharipov, (unknown), Alexander Kel, Fedor Kolpakov	158
14	Prediction of protein-DNA interactions of transcription factors linking proteomics and transcriptomics data 2016 ·PubMed ·(unknown), (unknown), Ruslan Sharipov, Ivan Yevshin, Fedor Kolpakov, Alexander Kel	2
15	Assessment of translational importance of mammalian mRNA sequence features based on Ribo-Seq and mRNA-Seq data 2016 ·Journal of Bioinformatics and Computational Biology ·(unknown), Yury V. Kondrakhin, Ivan Yevshin, (unknown), Ruslan Sharipov	2
16	RiboSeqDB – a repository of selected human and mouse ribosome footprint and RNA-seq data 2014 ·Ruslan Sharipov, Ivan Yevshin, Yury V. Kondrakhin, (unknown)	5
17	Model composition through model reduction: a combined model of CD95 and NF-κB signaling pathways 2013 ·BMC Systems Biology ·(unknown), Andreï Zinovyev, Ruslan Sharipov, Fedor Kolpakov	17
18	A Modular Model of the Apoptosis Machinery 2011 ·Advances in experimental medicine and biology ·(unknown), Ilya Kiselev, Ruslan Sharipov, Inna N. Lavrik, Fedor Kolpakov	12
19	Identification of Differentially Expressed Genes by Meta-Analysis of Microarray Data on Breast Cancer 2008 ·In Silico Biology ·Yury V. Kondrakhin, Ruslan Sharipov, Alexander Kel, Fedor Kolpakov	6
20	CYCLONET--an integrated database on cell cycle regulation and carcinogenesis 2007 ·Nucleic Acids Research ·Fedor Kolpakov, Vladimir Poroikov, Ruslan Sharipov, (unknown), Alexey Zakharov, Alexey A. Lagunin, Luciano Milanesi, Alexander Kel	9

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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