Special Issue "Statistical Methods for the Analysis of Genomic Data"

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

Deadline for manuscript submissions: 30 November 2019.

Special Issue Editors

Guest Editor
Dr. Hui Jiang

Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109-2029, USA
Website | E-Mail
Interests: Statistical Genomics; Bioinformatics; Statistical Computing
Guest Editor
Dr. Zhi He

Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109-2029, USA
Website | E-Mail
Interests: Bioinformatics; Optimization; Statistical Genomics; Survival Analysis

Special Issue Information

Dear Colleagues,

In recent years, technology breakthroughs have greatly enhanced our ability to understand the complex world of molecular biology. Rapid developments in genomic profiling techniques, such as high-throughput sequencing, have brought new opportunities and challenges to the fields of computational biology and bioinformatics. Furthermore, by combining genomic profiling techniques with other experimental techniques, many powerful approaches (e.g., RNA-Seq, ChIP-Seq, single-cell assays, Hi-C) have been developed in order to help explore the complex biological systems. As more genomic datasets become available, both in volume and variety, the analysis of such data has become a critical challenge as well as a topic of interest. Consequently, statistical methods dealing with the problems associated with these newly developed techniques are in high demand. This Special Issue will highlight the state-of-the-art statistical methods for the analysis of genomic data, and explore potential future directions for improvement.

Dr. Hui Jiang
Dr. Zhi He
Guest Editors

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 1800 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

  • High-throughput sequencing
  • Single-cell genomics
  • Metagenomics
  • Genomic data integration
  • Computational biology
  • Bioinformatics
  • Statistical genomics
  • Gene expression
  • Gene regulation
  • Biomarker discovery
  • Gene network
  • Functional genomics
  • Precision medicine

Published Papers (1 paper)

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Research

Open AccessArticle
Integrative Analysis of Cancer Omics Data for Prognosis Modeling
Received: 13 July 2019 / Revised: 30 July 2019 / Accepted: 7 August 2019 / Published: 9 August 2019
PDF Full-text (1492 KB) | HTML Full-text | XML Full-text
Abstract
Prognosis modeling plays an important role in cancer studies. With the development of omics profiling, extensive research has been conducted to search for prognostic markers for various cancer types. However, many of the existing studies share a common limitation by only focusing on [...] Read more.
Prognosis modeling plays an important role in cancer studies. With the development of omics profiling, extensive research has been conducted to search for prognostic markers for various cancer types. However, many of the existing studies share a common limitation by only focusing on a single cancer type and suffering from a lack of sufficient information. With potential molecular similarity across cancer types, one cancer type may contain information useful for the analysis of other types. The integration of multiple cancer types may facilitate information borrowing so as to more comprehensively and more accurately describe prognosis. In this study, we conduct marginal and joint integrative analysis of multiple cancer types, effectively introducing integration in the discovery process. For accommodating high dimensionality and identifying relevant markers, we adopt the advanced penalization technique which has a solid statistical ground. Gene expression data on nine cancer types from The Cancer Genome Atlas (TCGA) are analyzed, leading to biologically sensible findings that are different from the alternatives. Overall, this study provides a novel venue for cancer prognosis modeling by integrating multiple cancer types. Full article
(This article belongs to the Special Issue Statistical Methods for the Analysis of Genomic Data)
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