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.
SPAdes: A New Genome Assembly Algorithm and Its Applications to Single-Cell Sequencing
201218.0k citationsAnton Bankevich, Sergey Nurk et al.Journal of Computational Biologyprofile →
druGAN: An Advanced Generative Adversarial Autoencoder Model for de Novo Generation of New Molecules with Desired Molecular Properties in Silico
2017391 citationsArtur Kadurin, Sergey Nikolenko et al.profile →
FDR-controlled metabolite annotation for high-resolution imaging mass spectrometry
2016334 citationsAndrew Palmer, Prasad Phapale et al.Nature Methodsprofile →
Tooth detection and numbering in panoramic radiographs using convolutional neural networks
Countries citing papers authored by Sergey Nikolenko
Since
Specialization
Citations
This map shows the geographic impact of Sergey Nikolenko'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 Sergey Nikolenko with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sergey Nikolenko more than expected).
Fields of papers citing papers by Sergey Nikolenko
This network shows the impact of papers produced by Sergey Nikolenko. 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 Sergey Nikolenko. The network helps show where Sergey Nikolenko may publish in the future.
Co-authorship network of co-authors of Sergey Nikolenko
This figure shows the co-authorship network connecting the top 25 collaborators of Sergey Nikolenko.
A scholar is included among the top collaborators of Sergey Nikolenko 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 Sergey Nikolenko. Sergey Nikolenko is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Tutubalina, Elena, et al.. (2019). AspeRa: Aspect-Based Rating Prediction Based on User Reviews. Meeting of the Association for Computational Linguistics. 11–13.1 indexed citations
Polykovskiy, Daniil, et al.. (2018). Concorde: Morphological Agreement in Conversational Models. Asian Conference on Machine Learning. 407–421.
12.
Alexeeva, Svetlana, et al.. (2017). Measuring Prejudice and Ethnic Tensions in User-Generated Content.4 indexed citations
13.
Bodrunova, Svetlana S., Olessia Koltsova, Sergei Koltcov, & Sergey Nikolenko. (2017). Who’s Bad? Attitudes Toward Resettlers From the Post-Soviet South Versus Other Nations in the Russian Blogosphere. International journal of communication. 11. 23.8 indexed citations
14.
Alekseev, Anton, et al.. (2016). Improving neural network models for natural language processing in russian with synonyms.7 indexed citations
15.
Palmer, Andrew, Prasad Phapale, Régis Lavigne, et al.. (2016). FDR-controlled metabolite annotation for high-resolution imaging mass spectrometry. Nature Methods. 14(1). 57–60.334 indexed citations breakdown →
16.
Nikolenko, Sergey. (2016). ARTM vs. LDA: an SVD Extension Case Study. 276–282.1 indexed citations
17.
Alekseev, Anton & Sergey Nikolenko. (2016). Predicting the age of social network users from user-generated texts with word embeddings.3 indexed citations
Bankevich, Anton, Sergey Nurk, Dmitry Antipov, et al.. (2012). SPAdes: A New Genome Assembly Algorithm and Its Applications to Single-Cell Sequencing. Journal of Computational Biology. 19(5). 455–477.17957 indexed citations breakdown →
20.
Nikolenko, Sergey & Alexander Sirotkin. (2011). A New Bayesian Rating System for Team Competitions. International Conference on Machine Learning. 601–608.6 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.