Demo Data from VLDB 2026 / reviewers in the wild / expert
Alexander Artyomenko
dblp:140/3301
· DBLP profile ↗
4ranked-venue papers
2as first author
0since 2021 · last 2017
0000-0002-0360-8981ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 4 · 2 first-author
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% |
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
genomics |
0.2 | 1 | 2016 | Long Single-Molecule Reads Can Resolve the Complexity of the Influenza Virus Composed of Rare, Closely Related Mutant Variants · RECOMB 2016 |
Bioinformatics and computational biology
sequence analysis |
0.2 | 1 | 2016 | Long Single-Molecule Reads Can Resolve the Complexity of the Influenza Virus Composed of Rare, Closely Related Mutant Variants · RECOMB 2016 |
Bioinformatics and computational biology › genomics
viral genomics |
0.2 | 1 | 2016 | Long Single-Molecule Reads Can Resolve the Complexity of the Influenza Virus Composed of Rare, Closely Related Mutant Variants · RECOMB 2016 |
Bioinformatics and computational biology
combinatorial pooling |
0.2 | 1 | 2015 | Computational framework for next-generation sequencing of heterogeneous viral populations using combinatorial pooling · Bioinform. 2015 |
Bioinformatics and computational biology
deconvolution |
0.2 | 1 | 2015 | Computational framework for next-generation sequencing of heterogeneous viral populations using combinatorial pooling · Bioinform. 2015 |
Bioinformatics and computational biology
next-generation sequencing |
0.2 | 1 | 2015 | Computational framework for next-generation sequencing of heterogeneous viral populations using combinatorial pooling · Bioinform. 2015 |
Methods — techniques the papers use, named apart from their topics
barcoding · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2017 | Agent-Based in Silico Evolution of HCV Quasispecies
Alexander Artyomenko, Pelin B. Icer, Pavel Skums, Sumathi Ramachandran, Yuri Khudyakov, Alex Zelikovsky |
ISBRA | 1 |
| 2016 | Long Single-Molecule Reads Can Resolve the Complexity of the Influenza Virus Composed of Rare, Closely Related Mutant Variants
Alexander Artyomenko, Nicholas C. Wu, Serghei Mangul, Eleazar Eskin, Ren Sun, Alex Zelikovsky |
RECOMB | 1 |
| 2015 | Computational framework for next-generation sequencing of heterogeneous viral populations using combinatorial poolingabstractMOTIVATION: Next-generation sequencing (NGS) allows for analyzing a large number of viral sequences from infected patients, providing an opportunity to implement large-scale molecular surveillance of viral diseases. However, despite improvements in technology, traditional protocols for NGS of large numbers of samples are still highly cost and labor intensive. One of the possible cost-effective alternatives is combinatorial pooling. Although a number of pooling strategies for consensus sequencing of DNA samples and detection of SNPs have been proposed, these strategies cannot be applied to sequencing of highly heterogeneous viral populations. RESULTS: We developed a cost-effective and reliable protocol for sequencing of viral samples, that combines NGS using barcoding and combinatorial pooling and a computational framework including algorithms for optimal virus-specific pools design and deconvolution of individual samples from sequenced pools. Evaluation of the framework on experimental and simulated data for hepatitis C virus showed that it substantially reduces the sequencing costs and allows deconvolution of viral populations with a high accuracy. AVAILABILITY AND IMPLEMENTATION: The source code and experimental data sets are available at http://alan.cs.gsu.edu/NGS/?q=content/pooling. Pavel Skums, Alexander Artyomenko, Olga Glebova, Sumathi Ramachandran, Ion I. Mandoiu, David S. Campo, Zoya Dimitrova, Alex Zelikovsky, Yuri Khudyakov |
Bioinform. | 2 |
| 2013 | Reconstruction of viral population structure from next-generation sequencing data using multicommodity flowsabstractBACKGROUND: Highly mutable RNA viruses exist in infected hosts as heterogeneous populations of genetically close variants known as quasispecies. Next-generation sequencing (NGS) allows for analysing a large number of viral sequences from infected patients, presenting a novel opportunity for studying the structure of a viral population and understanding virus evolution, drug resistance and immune escape. Accurate reconstruction of genetic composition of intra-host viral populations involves assembling the NGS short reads into whole-genome sequences and estimating frequencies of individual viral variants. Although a few approaches were developed for this task, accurate reconstruction of quasispecies populations remains greatly unresolved. RESULTS: Two new methods, AmpMCF and ShotMCF, for reconstruction of the whole-genome intra-host viral variants and estimation of their frequencies were developed, based on Multicommodity Flows (MCFs). AmpMCF was designed for NGS reads obtained from individual PCR amplicons and ShotMCF for NGS shotgun reads. While AmpMCF, based on covering formulation, identifies a minimal set of quasispecies explaining all observed reads, ShotMCS, based on packing formulation, engages the maximal number of reads to generate the most probable set of quasispecies. Both methods were evaluated on simulated data in comparison to Maximum Bandwidth and ViSpA, previously developed state-of-the-art algorithms for estimating quasispecies spectra from the NGS amplicon and shotgun reads, respectively. Both algorithms were accurate in estimation of quasispecies frequencies, especially from large datasets. CONCLUSIONS: The problem of viral population reconstruction from amplicon or shotgun NGS reads was solved using the MCF formulation. The two methods, ShotMCF and AmpMCF, developed here afford accurate reconstruction of the structure of intra-host viral population from NGS reads. The implementations of the algorithms are available at http://alan.cs.gsu.edu/vira.html (AmpMCF) and http://alan.cs.gsu.edu/NGS/?q=content/shotmcf (ShotMCF). Pavel Skums, Nicholas Mancuso, Alexander Artyomenko, Bassam Tork, Ion I. Mandoiu, Yuri Khudyakov, Alex Zelikovsky |
BMC Bioinform. | 3 |