68 citations · 120 across the 6 of their papers we have counts for
7 papers
Reconstructing antibody repertoires from error-prone immunosequencing datasets
Alexander Shlemov, Sergey Bankevich, Andrey Bzikadze +3
Transforming error-prone immunosequencing datasets into antibody repertoires is a fundamental problem in immunogenomics, and a prerequisite for studies of immune responses. Althoug…
Two-exponential models of gene expression patterns for noisy experimental data
Theodore Alexandrov, Nina Golyandina, David Holloway +2
Motivation: Spatial pattern formation of the primary anterior-posterior morphogenetic gradient of the transcription factor Bicoid (Bcd) has been studied experimentally and computat…
New algorithmic challenges of adaptive immune repertoire construction
Alexander Shlemov, Sergey Bankevich, Andrey Bzikadze +1
Motivation: The analysis of antibodies and T-cell receptors (TCRs) concentrations in serum is a fundamental problem in immunoinformatics. Repertoire construction is a preliminary s…
Semi-nonparametric singular spectrum analysis with projection
Nina Golyandina, Alex Shlemov
Singular spectrum analysis (SSA) is considered for decomposition of time series into identifiable components. The Basic SSA method is nonparametric and constructs an adaptive expan…
Shaped extensions of singular spectrum analysis
Alex Shlemov, Nina Golyandina
Extensions of singular spectrum analysis (SSA) for processing of non-rectangular images and time series with gaps are considered. A circular version is suggested, which allows appl…
Multivariate and 2D Extensions of Singular Spectrum Analysis with the Rssa Package
Nina Golyandina, Anton Korobeynikov, Alex Shlemov +1
Implementation of multivariate and 2D extensions of Singular Spectrum Analysis (SSA) by means of the R-package Rssa is considered. The extensions include MSSA for simultaneous anal…
Variations of singular spectrum analysis for separability improvement: non-orthogonal decompositions of time series
Nina Golyandina, Alex Shlemov
Singular spectrum analysis (SSA) as a nonparametric tool for decomposition of an observed time series into sum of interpretable components such as trend, oscillations and noise is…