Published since May 1, 2002

Statistical Applications in Genetics and Molecular Biology

ISSN: 1544-6115

Editor-in-chief: Guido Sanguinetti

Impact Factor: 0.419

About this journal

Objective
Statistical Applications in Genetics and Molecular Biology seeks to publish significant research on the application of statistical ideas to problems arising from computational biology. The focus of the papers should be on the relevant statistical issues but should contain a succinct description of the relevant biological problem being considered. The range of topics is wide and will include topics such as linkage mapping, association studies, gene finding and sequence alignment, protein structure prediction, design and analysis of microarray data, molecular evolution and phylogenetic trees, DNA topology, and data base search strategies. Both original research and review articles will be warmly received.

What scholars are saying about Statistical Applications in Genetics and Molecular Biology

“This journal will keep statisticians up to date on the thinking behind the development and validation of molecular biology based classifiers for diagnostic testing for use in such areas as early detection of disease or recurrence, risk stratification, prognosis, prediction of treatment response, monitoring, and drug dosing.”

Gene Pennello, Ph.D., Center for Devices and Radiological Health, FDA

“Statistical applications in Genetics and Genomics are very important areas of research. This journal is one of the high-quality journals that I use, and that my students use as well.”

Carl Lee, Professor of Mathematics, Central Michigan University

Topics

Linkage mapping
Association studies
Gene regulatory networks
Protein structure prediction
High-throughput data analysis
Molecular evolution and phylogenetic trees
Multi-omics data integration
Genetic and epigenetic data modelling

Article formats
Original Research Articles, Review Articles, Software Application Notes.

Your Benefits

Your benefits

Theoretical and computational innovations in the statistical analysis of biological data and systems
Practical applications of statistical ideas to computational biology, genomics, and systems biology and medicine
Specialized expertise in genomics, molecular, cellular and systems biology
Internationally recognized authors and editorial board
Rapid online publication of contributions as “ahead-of-print” versions
High quality peer review
Rapid “ahead-of-print” online publication of accepted papers
Open Access publication option available

History

Content available since 2002 (Volume 1, Issue 1)

Previously published by Berkeley Electronic Press

Other publications in the field

Previous Titles

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Supplementary Materials

Topics

External Links

Online Submission of Manuscripts

Volume 20 Issue 2

April 2021

Accessible July 23, 2021

Frontmatter

Page range: i-ii

Download PDF

Research Articles

July 9, 2021

Collocation based training of neural ordinary differential equations

Elisabeth Roesch, Christopher Rackauckas, Michael P. H. Stumpf

Page range: 37-49

Abstract

The predictive power of machine learning models often exceeds that of mechanistic modeling approaches. However, the interpretability of purely data-driven models, without any mechanistic basis is often complicated, and predictive power by itself can be a poor metric by which we might want to judge different methods. In this work, we focus on the relatively new modeling techniques of neural ordinary differential equations. We discuss how they relate to machine learning and mechanistic models, with the potential to narrow the gulf between these two frameworks: they constitute a class of hybrid model that integrates ideas from data-driven and dynamical systems approaches. Training neural ODEs as representations of dynamical systems data has its own specific demands, and we here propose a collocation scheme as a fast and efficient training strategy. This alleviates the need for costly ODE solvers. We illustrate the advantages that collocation approaches offer, as well as their robustness to qualitative features of a dynamical system, and the quantity and quality of observational data. We focus on systems that exemplify some of the hallmarks of complex dynamical systems encountered in systems biology, and we map out how these methods can be used in the analysis of mathematical models of cellular and physiological processes.

July 13, 2021

AdaReg: data adaptive robust estimation in linear regression with application in GTEx gene expressions

Meng Wang, Lihua Jiang, Michael P. Snyder

Page range: 51-71

Abstract

The Genotype-Tissue Expression (GTEx) project provides a valuable resource of large-scale gene expressions across multiple tissue types. Under various technical noise and unknown or unmeasured factors, how to robustly estimate the major tissue effect becomes challenging. Moreover, different genes exhibit heterogeneous expressions across different tissue types. Therefore, we need a robust method which adapts to the heterogeneities of gene expressions to improve the estimation for the tissue effect. We followed the approach of the robust estimation based on γ -density-power-weight in the works of Fujisawa, H. and Eguchi, S. (2008). Robust parameter estimation with a small bias against heavy contamination. J. Multivariate Anal . 99: 2053–2081 and Windham, M.P. (1995). Robustifying model fitting. J. Roy. Stat. Soc. B : 599–609, where γ is the exponent of density weight which controls the balance between bias and variance. As far as we know, our work is the first to propose a procedure to tune the parameter γ to balance the bias-variance trade-off under the mixture models. We constructed a robust likelihood criterion based on weighted densities in the mixture model of Gaussian population distribution mixed with unknown outlier distribution, and developed a data-adaptive γ -selection procedure embedded into the robust estimation. We provided a heuristic analysis on the selection criterion and found that our practical selection trend under various γ ’s in average performance has similar capability to capture minimizer γ as the inestimable mean squared error (MSE) trend from our simulation studies under a series of settings. Our data-adaptive robustifying procedure in the linear regression problem (AdaReg) showed a significant advantage in both simulation studies and real data application in estimating tissue effect of heart samples from the GTEx project, compared to the fixed γ procedure and other robust methods. At the end, the paper discussed some limitations on this method and future work.

Ahead of Print / Just Accepted

Ahead of Print / Just Accepted

Volume 20 (2021)

Volume 19 (2020)

Volume 18 (2019)

Volume 17 (2018)

Volume 16 (2017)

Volume 15 (2016)

Volume 14 (2015)

Volume 13 (2014)

Volume 12 (2013)

Volume 11 (2012)

Volume 7 (2008)

Volume 6 (2007)

Issue 1

Volume 5 (2006)

Issue 1

Volume 4 (2005)

Issue 1

Volume 3 (2004)

Issue 1

Volume 2 (2003)

Issue 1

Volume 1 (2002)

Issue 1

5-year Impact Factor	0.783
Cite Score	1.2
Impact Factor	0.419
Journal Citation Indicator	0.17
Mathematical Citation Quotient	0.06
SCImago Journal Rank	0.239
Source Normalized Impact per Paper	0.353

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Hybrid Open Access

In this journal, authors have the option to publish their article under an open access license. Open Access allows you as an author to retain copyright and share your findings with colleagues and interested parties worldwide without any restraints.
Please note that authors from institutions with which we have a transformative agreement can publish open access without paying an article processing charge (APC). More information on the eligible institutions and articles can be found under the "Funding and Support" tab here.

Editorial

Editor-in-Chief
Guido Sanguinetti, International School of Advanced Studies Trieste

Founding Editors
Nicholas P. Jewell, University of California Berkeley
Gary Churchill, The Jackson Laboratory
Elizabeth Thompson, University of Washington

Associate Editors
Mark Beaumont, University of Bristol
Tim Beißbarth, University of Göttingen
Harald Binder, Universitty of Freiburg
Giulio Caravagna, University of Trieste
Frank Dondelinger, Lancaster University
Mayetri Gupta, University of Glasgow
Antti Honkela, University of Helsinki
Alan Hubbard, University of California
Dirk Husmeier, University of Glasgow
Hongkai Ji, Johns Hopkins University
Sunduz Keles, University of Wisconsin
Shili Lin, Ohio State University
Ping Ma, University of Illinois at Urbana-Champaign
Vincent Macaulay, University of Glasgow
Paul Marjoram, University of Southern California
Bart Mertens, Leiden University Medical Centre
Sach Mukherjee, German Center for Neurodegenerative Diseases Bonn
Olle Nerman, Chalmers University of Technology
Enrico G. Petretto, Imperial College London
John Pinney, Imperial College London
Magnus Rattray, University of Manchester
Andrey Rzhetsky, University of Chicago
Nicolas Salamin, University of Lausanne
Michael Stumpf, University of Melbourne
Catalina Vallejos, University of Edinburgh
Mark van der Laan, University of California Berkeley
Arndt von Haeseler, Center for Integrative Bioinformatics Vienna
Ernst Wit, University of Groningen
Christopher Yau, University of Birmingham

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(Deutsch)

Online ISSN: 1544-6115
Type: Journal
Language: English
Publisher: De Gruyter
First published: May 1, 2002
Publication Frequency: 6 Issues per Year
Audience: Researchers and practitioners interested in statistical applications in genetics and molecular biology