Statistical Applications in Genetics and Molecular Biology
Overview
Statistical Applications in Genetics and Molecular Biology is a peer-reviewed journal that focuses on the application of statistical methods in genetics and molecular biology research. It covers a wide range of topics including genetic association studies, gene expression analysis, genomic data analysis, and population genetics. The journal provides a platform for researchers to share innovative statistical approaches and their practical applications in understanding the genetic basis of diseases and biological processes.
Details
Details
Abbr.
Stat Appl Genet Mol Biol
Start
2002
End
Continuing
Frequency
Bimonthly
p-ISSN
2194-6302
e-ISSN
1544-6115
Country
Germany
Language
English
Specialties
Public Health
Molecular Biology
Genetics
Molecular Biology
Genetics
Metrics
Metrics
h-index / Ranks: 7207
51
SJR / Ranks: 13524
201
CiteScore / Ranks: 11984
1.40
JIF / Ranks: 6961
0.9
Recent Articles
1.
Jayanetti W, Sikdar S
Stat Appl Genet Mol Biol
. 2024 Sep;
23(1).
PMID: 39340124
In recent years, meta-analyzing summary results from multiple studies has become a common practice in genomic research, leading to a significant improvement in the power of statistical detection compared to...
2.
Sharma L, Deepak A, Ranjan A, Krishnasamy G
Stat Appl Genet Mol Biol
. 2024 Jun;
23(1).
PMID: 38943434
Understanding a protein's function based solely on its amino acid sequence is a crucial but intricate task in bioinformatics. Traditionally, this challenge has proven difficult. However, recent years have witnessed...
3.
Krutto A, Nost T, Thoresen M
Stat Appl Genet Mol Biol
. 2024 May;
23(1).
PMID: 38810893
This article addresses the limitations of existing statistical models in analyzing and interpreting highly skewed miRNA-seq raw read count data that can range from zero to millions. A heavy-tailed model...
4.
Laursen R, Maretty L, Hobolth A
Stat Appl Genet Mol Biol
. 2024 May;
23(1).
PMID: 38753402
Somatic mutations in cancer can be viewed as a mixture distribution of several mutational signatures, which can be inferred using non-negative matrix factorization (NMF). Mutational signatures have previously been parametrized...
5.
Hari A, Jinto E, Dennis D, Krishna K, George P, Roshni S, et al.
Stat Appl Genet Mol Biol
. 2024 May;
23(1).
PMID: 38736398
Longitudinal time-to-event analysis is a statistical method to analyze data where covariates are measured repeatedly. In survival studies, the risk for an event is estimated using Cox-proportional hazard model or...
6.
Minotto T, Robert P, Haff I, Sandve G
Stat Appl Genet Mol Biol
. 2024 Apr;
23(1).
PMID: 38563699
Simulation frameworks are useful to stress-test predictive models when data is scarce, or to assert model sensitivity to specific data distributions. Such frameworks often need to recapitulate several layers of...
7.
Haque M, Kubatko L
Stat Appl Genet Mol Biol
. 2024 Feb;
23(1).
PMID: 38366619
Methods based on the multi-species coalescent have been widely used in phylogenetic tree estimation using genome-scale DNA sequence data to understand the underlying evolutionary relationship between the sampled species. Evolutionary...
8.
Linder H, Zhang Y, Wang Y, Ouyang Z
Stat Appl Genet Mol Biol
. 2024 Feb;
23(1).
PMID: 38363177
Developments in biotechnologies enable multi-platform data collection for functional genomic units apart from the gene. Profiling of non-coding microRNAs (miRNAs) is a valuable tool for understanding the molecular profile of...
9.
Liu Z, Turkmen A, Lin S
Stat Appl Genet Mol Biol
. 2024 Jan;
23(1).
PMID: 38235525
Population stratification (PS) is one major source of confounding in both single nucleotide polymorphism (SNP) and haplotype association studies. To address PS, principal component regression (PCR) and linear mixed model...
10.
Liu X, Ahsan Z, Martheswaran T, Rosenberg N
Stat Appl Genet Mol Biol
. 2023 Dec;
22(1).
PMID: 38073574
Allele-sharing statistics for a genetic locus measure the dissimilarity between two populations as a mean of the dissimilarity between random pairs of individuals, one from each population. Owing to within-population...