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: closed (1 September 2018)

Special Issue Editor

Guest Editor
Dr. Ting Hu

Department of Computer Science, Memorial University of Newfoundland St. John's, NL A1B 3X5, Canada
Website | E-Mail
Interests: designing robust meta-heuristic evolutionary algorithms; mining large-scale biomedical data using complex networks, information theory, and machine learning techniques; using simulated computational evolution to study core mechanisms of natural evolution

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.

Keywords

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

Published Papers (1 paper)

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Research

Open AccessArticle An Improved Method for Prediction of Cancer Prognosis by Network Learning
Genes 2018, 9(10), 478; https://doi.org/10.3390/genes9100478
Received: 28 August 2018 / Revised: 21 September 2018 / Accepted: 27 September 2018 / Published: 2 October 2018
PDF Full-text (2787 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Accurate identification of prognostic biomarkers is an important yet challenging goal in bioinformatics. Many bioinformatics approaches have been proposed for this purpose, but there is still room for improvement. In this paper, we propose a novel machine learning-based method for more accurate identification
[...] Read more.
Accurate identification of prognostic biomarkers is an important yet challenging goal in bioinformatics. Many bioinformatics approaches have been proposed for this purpose, but there is still room for improvement. In this paper, we propose a novel machine learning-based method for more accurate identification of prognostic biomarker genes and use them for prediction of cancer prognosis. The proposed method specifies the candidate prognostic gene module by graph learning using the generative adversarial networks (GANs) model, and scores genes using a PageRank algorithm. We applied the proposed method to multiple-omics data that included copy number, gene expression, DNA methylation, and somatic mutation data for five cancer types. The proposed method showed better prediction accuracy than did existing methods. We identified many prognostic genes and their roles in their biological pathways. We also showed that the genes identified from different omics data were complementary, which led to improved accuracy in prediction using multi-omics data. Full article
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