Maria Chikina
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Explore the profile of Maria Chikina including associated specialties, affiliations and a list of published articles.
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Recent Articles
1.
Balci A, Chikina M
Bioinform Adv
. 2025 Mar;
5(1):vbaf013.
PMID: 40078573
Motivation: Epigenetic assays using next-generation sequencing have furthered our understanding of the functional genomic regions and the mechanisms of gene regulation. However, a single assay produces billions of data points,...
2.
Little J, Meyer G, Grover A, Francette A, Partha R, Arndt K, et al.
bioRxiv
. 2025 Mar;
PMID: 40060623
Evolutionary Rate Covariation (ERC) is an established comparative genomics method that identifies sets of genes sharing patterns of sequence evolution, which suggests shared function. Whereas many functional predictions of ERC...
3.
Crawford J, Chikina M, Greene C
Patterns (N Y)
. 2025 Jan;
5(12):101115.
PMID: 39776849
Guidelines in statistical modeling for genomics hold that simpler models have advantages over more complex ones. Potential advantages include cost, interpretability, and improved generalization across datasets or biological contexts. We...
4.
Nandi S, Zhu Y, Gillenwater L, Subirana-Granes M, Zhang H, Janani N, et al.
Pac Symp Biocomput
. 2024 Dec;
30:412-425.
PMID: 39670386
Down syndrome (DS), caused by the triplication of chromosome 21 (T21), is a prevalent genetic disorder with a higher incidence of obesity. Traditional approaches have struggled to differentiate T21-specific molecular...
5.
Selberg A, Chikina M, Sackton T, Muse S, Lucaci A, Weaver S, et al.
bioRxiv
. 2024 Nov;
PMID: 39605407
Errors in multiple sequence alignments (MSAs) are known to bias many comparative evolutionary methods. In the context of natural selection analyses, specifically codon evolutionary models, excessive rates of false positives...
6.
Redlich R, Kowalczyk A, Tene M, Sestili H, Foley K, Saputra E, et al.
Mol Biol Evol
. 2024 Oct;
41(11).
PMID: 39404101
Comparative genomics approaches seek to associate molecular evolution with the evolution of phenotypes across a phylogeny. Many of these methods lack the ability to analyze non-ordinal categorical traits with more...
7.
Sasse A, Chikina M, Mostafavi S
iScience
. 2024 Sep;
27(9):110807.
PMID: 39286491
To understand the decision process of genomic sequence-to-function models, explainable AI algorithms determine the importance of each nucleotide in a given input sequence to the model's predictions and enable discovery...
8.
Zhu Y, Benos P, Chikina M
Bioinformatics
. 2024 Sep;
40(Suppl 2):ii87-ii97.
PMID: 39230691
Motivation: Understanding causal effects is a fundamental goal of science and underpins our ability to make accurate predictions in unseen settings and conditions. While direct experimentation is the gold standard...
9.
Sasse A, Chikina M, Mostafavi S
Nat Methods
. 2024 Aug;
21(8):1374-1377.
PMID: 39122947
No abstract available.
10.
Hu M, Chikina M
Bioinformatics
. 2024 Jul;
40(7).
PMID: 38970377
Summary: Computational cell-type deconvolution is an important analytic technique for modeling the compositional heterogeneity of bulk gene expression data. A conceptually new Bayesian approach to this problem, BayesPrism, has recently...