Special Issue "Feature Papers in Technologies and Resources for Genetics 2023"

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

Deadline for manuscript submissions: 20 October 2023 | Viewed by 806

Special Issue Editors

Department of Molecular Medicine, University of Padova, Padua, Italy
Interests: cancer genomics and transciptomics; bioinformatics; systems biology; microRNAs; circular RNAs
Special Issues, Collections and Topics in MDPI journals
Department of Biochemistry and Molecular Medicine, George Washington University, Washington, DC 20052, USA
Interests: genomics; transcriptomics; cancer genomics; computational biology; bioinformatics; RNA seq; bioinformatic tools
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue, “Feature Papers in Technologies and Resources for Genetics”, aims to collect high-quality review articles or research articles on all aspects of novel advances in technological methods, protocols, and software for the generation and interpretation of genome-derived data. It is dedicated to recent advances in the research area of genomics and genetics and comprises a selection of exclusive papers from the Editorial Board Members (EBMs) of the Technologies and Resources for Genetics Section, as well as invited papers from relevant experts. We also welcome senior experts in the field to make contributions to this Special Issue. We aim to represent our Section as an attractive open access publishing platform for genomics and genetic research.

The topics of this issue will describe novel advances in technological methods, protocols, and software for the generation and interpretation of genome-derived data. The topics covered will include but are not limited to:

  1. Genome sequencing, genomic technologies, and novel sequencing strategies;
  2. Functional genomics and genome annotation;
  3. Computational biology, bioinformatics, and biostatistics;
  4. Bioinformatics analysis of proteomics and genomics data, including new online data resources and tools;
  5. New approaches for phylogenomic analyses;
  6. Genome editing;
  7. Genetic reprogramming;
  8. Single-molecule, real-time (SMRT) sequencing;
  9. Comparative genomics;
  10. Conservation genetics and genomics;
  11. Metagenomics;
  12. Noncoding genomics;
  13. Circular RNA;
  14. Machine learning applications to genetics and genomics.

Prof. Dr. Stefania Bortoluzzi
Prof. Dr. Anelia D. Horvath
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 submissions that pass pre-check are 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 2400 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.

Published Papers (1 paper)

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Research

Article
Deep-Learning-Based Hepatic Ploidy Quantification Using H&E Histopathology Images
Genes 2023, 14(4), 921; https://doi.org/10.3390/genes14040921 - 16 Apr 2023
Viewed by 663
Abstract
Polyploidy, the duplication of the entire genome within a single cell, is a significant characteristic of cells in many tissues, including the liver. The quantification of hepatic ploidy typically relies on flow cytometry and immunofluorescence (IF) imaging, which are not widely available in [...] Read more.
Polyploidy, the duplication of the entire genome within a single cell, is a significant characteristic of cells in many tissues, including the liver. The quantification of hepatic ploidy typically relies on flow cytometry and immunofluorescence (IF) imaging, which are not widely available in clinical settings due to high financial and time costs. To improve accessibility for clinical samples, we developed a computational algorithm to quantify hepatic ploidy using hematoxylin-eosin (H&E) histopathology images, which are commonly obtained during routine clinical practice. Our algorithm uses a deep learning model to first segment and classify different types of cell nuclei in H&E images. It then determines cellular ploidy based on the relative distance between identified hepatocyte nuclei and determines nuclear ploidy using a fitted Gaussian mixture model. The algorithm can establish the total number of hepatocytes and their detailed ploidy information in a region of interest (ROI) on H&E images. This is the first successful attempt to automate ploidy analysis on H&E images. Our algorithm is expected to serve as an important tool for studying the role of polyploidy in human liver disease. Full article
(This article belongs to the Special Issue Feature Papers in Technologies and Resources for Genetics 2023)
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