Special Issue "Novel Computational Methods for the Analysis of Gene-Gene Interactions"

A special issue of Genes (ISSN 2073-4425). This special issue belongs to the section "Technologies and Resources for Genetics".

Deadline for manuscript submissions: 1 September 2018

Special Issue Editor

Guest Editor
Dr. Ting Hu

Department of Computer Science Memorial University St. John’s, NL Canada
Website | E-Mail
Interests: Evolutionary Computing; Bioinformatics; Computational Biology; Artificial Evolution; Machine Learning; Complex Networks

Special Issue Information

Dear Colleagues,

With the rapid development of genotyping technologies and exponential increase in computational power, we are now able to leverage the wealth of genetic data to test millions of genetic variations for their associations with complex traits and diseases. In the past decade, we have identified hundreds of genetic variations associated with a variety of human traits and diseases, however, with very limited increments in disease risks. Such a problem has led researchers to search for the explanations for the ``missing heritability''.  Many agree that the limited heritability found by initial studies is very likely a result of the overly simplified assumption on the genetic architecture of complex human traits and diseases and the constraints of most commonly used one-gene-at-a-time methodology.

Most genetic association analyses adopt univariate methods, where individual genetic factors are evaluated on the trait/disease association separately. However, many human traits and diseases, such as cancers and diabetes, are more plausibly due to the interactions among multiple genetic factors, i.e., epistasis. Such an interaction effect has not yet been fully taken into account in current research.

The research field calls for innovative and sophisticated computational methodologies that embrace the complexity of the genetic architecture of complex traits and diseases rather than ignoring it. These new methodologies should disrupt the common and simple assumptions on complex traits and diseases, and use intelligent heuristic search or modeling strategies to address the high dimensional gene-gene interactions.

In this special issue, we would like to feature a series of novel computational methods, especially in machine learning and complex network modeling, that are capable of detecting and quantifying the multi-variant gene-gene interactions associated with complex traits and diseases. We welcome any original articles relating to, but not limited to, the topics described herein.


Ting Hu, PhD
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Genes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.


  • Gene-gene interaction
  • Epistasis
  • Machine learning
  • Network modeling
  • Genetic association studies

Published Papers

This special issue is now open for submission.
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