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Genes
Special Issues
Machine Learning Applications in Genetics
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Machine Learning Applications in Genetics

A special issue of Genes (ISSN 2073-4425). This special issue belongs to the section "Bioinformatics".

Deadline for manuscript submissions: closed (25 July 2023) | Viewed by 2347

Special Issue Editors

Dr. Shibiao Wan

E-Mail Website
Guest Editor
Center for Applied Bioinformatics, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA
Interests: bioinformatics; machine learning; computational biology; single cell analysis; genomics
Special Issues, Collections and Topics in MDPI journals
Dr. Wenan Chen

E-Mail Website
Guest Editor
St. Jude Children's Research Hospital, Memphis, TN, USA
Interests: statistical genetics; statistical genomics; bioinformatics; machine learning; biomedical image analysis
Dr. Yichao Li

E-Mail Website
Guest Editor
St. Jude Children's Research Hospital, Memphis, TN, USA
Interests: functional genomics; machine learning; genome editing

Special Issue Information

Dear Colleagues,

With the advent of next-generation sequencing (NGS) techniques and, recently, the first complete sequence of a human genome, massive quantities of heterogenous and diverse data in biology and medicine have been generated, which makes conventional analysis methods time-consuming and inefficient. To unravel the mechanisms of molecular biological systems in which enormous amounts of genomics data are usually involved, machine learning has become one of the essential tools. Machine learning has been widely used in various domains of genetics, including but not limited to cancer genetics, epigenetics, single-cell genomics, genome editing, functional genomics, pharmacogenetics, genetic risk prediction, etc. To facilitate the dissemination of progress in the application of machine learning in genetics, we are launching this Special Issue.

For this Special Issue, we particularly encourage the submission of manuscripts which deal with any aspect of machine learning applications in genetics, including but not limited to the domains listed above. We welcome manuscripts in the form of original research articles, reviews, short communications, perspectives, and commentaries of the aforementioned topics and domains.

Dr. Shibiao Wan
Dr. Wenan Chen
Dr. Yichao Li
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 2600 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

  • supervised learning
  • unsupervised learning
  • cancer genetics
  • single cell genomics
  • gene regulation
  • genome editing
  • functional genomics
  • multiomics
  • pharmacogenetics
  • genetic risk prediction

Published Papers (1 paper)

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Research

20 pages, 1068 KiB  
Article
Attention-Based Graph Neural Network for Label Propagation in Single-Cell Omics
by Rahul Bhadani, Zhuo Chen and Lingling An
Genes 2023, 14(2), 506; https://doi.org/10.3390/genes14020506 - 16 Feb 2023
Cited by 1 | Viewed by 1714
Abstract
Single-cell data analysis has been at forefront of development in biology and medicine since sequencing data have been made available. An important challenge in single-cell data analysis is the identification of cell types. Several methods have been proposed for cell-type identification. However, these [...] Read more.
Single-cell data analysis has been at forefront of development in biology and medicine since sequencing data have been made available. An important challenge in single-cell data analysis is the identification of cell types. Several methods have been proposed for cell-type identification. However, these methods do not capture the higher-order topological relationship between different samples. In this work, we propose an attention-based graph neural network that captures the higher-order topological relationship between different samples and performs transductive learning for predicting cell types. The evaluation of our method on both simulation and publicly available datasets demonstrates the superiority of our method, scAGN, in terms of prediction accuracy. In addition, our method works best for highly sparse datasets in terms of F1 score, precision score, recall score, and Matthew’s correlation coefficients as well. Further, our method’s runtime complexity is consistently faster compared to other methods. Full article
(This article belongs to the Special Issue Machine Learning Applications in Genetics)
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