Demo Data from VLDB 2026 / reviewers in the wild / expert
Ekaterina Khrameeva
dblp:62/11192 · also Ekaterina E. Khrameeva
· DBLP profile ↗
10ranked-venue papers
1as first author
3since 2021 · last 2025
0000-0001-6188-9139ORCID · verified
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 8 · 1 first-author · 1 since 2021Databases, data management, data science and information retrieval · 2 · 2 since 2021Artificial intelligence and machine learning · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Interdisciplinary, comprehensive, and emerging computing
2 papers |
Bioinformatics and computational biology · 100% | |
| Databases, data mining, and information retrieval
1 paper |
Information retrieval · 100% |
Topics — the 6 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology
epigenetics |
0.9 | 1 | 2025 | ComputAgeBench: Epigenetic Aging Clocks Benchmark · KDD (2) 2025 |
Bioinformatics and computational biology › epigenetics
epigenetic clock |
0.9 | 1 | 2025 | ComputAgeBench: Epigenetic Aging Clocks Benchmark · KDD (2) 2025 |
Information retrieval › evaluation
benchmark |
0.9 | 1 | 2025 | ComputAgeBench: Epigenetic Aging Clocks Benchmark · KDD (2) 2025 |
Bioinformatics and computational biology › transcriptomics
alternative splicing analysis |
0.1 | 1 | 2012 | Evidence for Widespread Association of Mammalian Splicing and Conserved Long-Range RNA Structures · RECOMB 2012 |
Bioinformatics and computational biology › RNA biology
RNA processing |
0.1 | 1 | 2012 | Evidence for Widespread Association of Mammalian Splicing and Conserved Long-Range RNA Structures · RECOMB 2012 |
Bioinformatics and computational biology › RNA biology › RNA analysis › RNA bioinformatics
RNA structure analysis |
0.1 | 1 | 2012 | Evidence for Widespread Association of Mammalian Splicing and Conserved Long-Range RNA Structures · RECOMB 2012 |
Methods — techniques the papers use, named apart from their topics
DNA methylation profiling · 1.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | ComputAgeBench: Epigenetic Aging Clocks BenchmarkabstractAge accelerating condition Healthy control p-value Age acceleration Blood data C h r o n o lo g ic a l A g e CpG site Dataset Averaging across cells ... ... ... ... ... CpG N CpG ...Figure 1: ComputAgeBench: the first comprehensive framework for benchmarking various epigenetic aging clock models.For a dataset 𝑋 , obtained by profiling DNA methylation at CpG sites in bulk blood samples, an aging clock model 𝑓 is trained to distinguish healthy individuals from those with pre-defined aging-accelerating conditions. Dmitrii Kriukov, Evgeny Efimov, Ekaterina Kuzmina, Anastasiia Dudkovskaia, Ekaterina Khrameeva, Dmitry V. Dylov |
KDD (2) | 5 |
| 2025 | A hierarchical algorithm with randomized learning for robust tissue segmentation and classification in digital pathology
Svetlana Illarionova, Rifat Hamoudi, Margarita Zapevalina, Ilya Fedin, Nadezhda Alsahanova, Alexander V. Bernstein, Evgeny Burnaev, Vera Alferova, Ekaterina Khrameeva, Dmitrii G. Shadrin, Iman Talaat, Ahmed Bouridane, Maxim Sharaev |
Inf. Sci. | 9 |
| 2023 | HiConfidence: a novel approach uncovering the biological signal in Hi-C data affected by technical biasesabstractThe chromatin interaction assays, particularly Hi-C, enable detailed studies of genome architecture in multiple organisms and model systems, resulting in a deeper understanding of gene expression regulation mechanisms mediated by epigenetics. However, the analysis and interpretation of Hi-C data remain challenging due to technical biases, limiting direct comparisons of datasets obtained in different experiments and laboratories. As a result, removing biases from Hi-C-generated chromatin contact matrices is a critical data analysis step. Our novel approach, HiConfidence, eliminates biases from the Hi-C data by weighing chromatin contacts according to their consistency between replicates so that low-quality replicates do not substantially influence the result. The algorithm is effective for the analysis of global changes in chromatin structures such as compartments and topologically associating domains. We apply the HiConfidence approach to several Hi-C datasets with significant technical biases, that could not be analyzed effectively using existing methods, and obtain meaningful biological conclusions. In particular, HiConfidence aids in the study of how changes in histone acetylation pattern affect chromatin organization in Drosophila melanogaster S2 cells. The method is freely available at GitHub: https://github.com/victorykobets/HiConfidence. Victoria A Kobets, Sergey V. Ulianov, Aleksandra A. Galitsyna, Semen A. Doronin, Elena A. Mikhaleva, Mikhail S. Gelfand, Yuri Y. Shevelyov, Sergey V. Razin, Ekaterina Khrameeva |
Briefings Bioinform. | 9 |
| 2018 | Prediction of 3D Chromatin Structure Using Recurrent Neural Networks
Michal Rozenwald, Ekaterina Khrameeva, Grigory V. Sapunov, Mikhail S. Gelfand |
BIBM | 2 |
| 2018 | Prediction of chromatin spatial structure characteristics using machine learning methods
Sergei Starikov, Ekaterina Khrameeva, Mikhail S. Gelfand |
BIBM | 2 |
| 2018 | The chromatin structure of Dictyostelium discoideum
Olga Tsoy, Aleksandra A. Galitsyna, Ekaterina Khrameeva, Sergey V. Ulianov, Mikhail S. Gelfand, Sergey V. Razin |
BIBM | 3 |
| 2018 | Nuclear lamina maintains global spatial organization of chromatin in Drosophila cultured cells
Sergey V. Ulianov, Semen A. Doronin, Ekaterina Khrameeva, Pavel Kos, Sergey S. Starikov, Aleksandra A. Galitsyna, Artem Luzhin, Mikhail S. Gelfand, Alexander Chertovich, Sergey V. Razin, Yuri Y. Shevelyov |
BIBM | 3 |
| 2018 | Single-cell Hi-C demonstrates that TADs are stable units of Drosophila genome folding that persist in individual cells
Vlada S. Zakharova, Aleksandra A. Galitsyna, Kirill E. Polovnikov, Ekaterina Khrameeva, Mariya D. Logacheva, Elena A. Mikhaleva, Egor S. Vassetzky, Aleksey A. Gavrilov, Yuri Y. Shevelev, Sergey K. Nechaev, Sergey V. Ulianov, Sergey V. Razin |
BIBM | 4 |
| 2012 | Evidence for Widespread Association of Mammalian Splicing and Conserved Long-Range RNA Structures
Dmitri D. Pervouchine, Ekaterina Khrameeva, Marina Pichugina, Olexii Nikolaienko, Mikhail S. Gelfand, Petr Rubtsov, Andrey A. Mironov |
RECOMB | 2 |
| 2012 | Biases in read coverage demonstrated by interlaboratory and interplatform comparison of 117 mRNA and genome sequencing experimentsabstractHigh-throughput sequencing of whole genomes and transcriptomes allows one to generate large amounts of sequence data very rapidly and at a low cost. The goal of most mRNA sequencing studies is to perform the comparison of the expression level between different samples. However, given a broad variety of modern sequencing protocols, platforms and versions thereof, it is not clear to what extent the obtained results are consistent across platforms and laboratories. The comparison of 117 human mRNA and genome high-throughput sequencing experiments performed on the Illumina and SOLiD platforms at 26 institutions all over the world demonstrated high dependency of the gene coverage profiles on the producing laboratory. Gene coverage profiles showed laboratory-specific non-uniformity that survived the 3'-bias correction and mappability normalization, suggesting that there are other yet unknown mRNA-associated biases. Ekaterina Khrameeva, Mikhail S. Gelfand |
BMC Bioinform. | 1 |