Yudong Cai
Peilin Jia
Tao Huang- 1School of Life Sciences, Shanghai University, Shanghai, China
- 2Beijing Institute of Genomics, Beijing, China
- 3Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, China
Editorial on the Research Topic
Finding new epigenomics and epigenetics biomarkers for complex diseases and significant developmental events with machine learning methods, Volume II
Next-generation sequencing (NGS) has revolutionized biomedical research, enabling genome-wide screening of genetic defects. As genomic data increases, it will be a challenge to identify genetic patterns with traditional sampling-based statistical methods. Therefore, advanced machine learning methods, such as deep learning, and Artificial Intelligence (AI), can be very beneficial.
In the first volume, we gathered insights on the difference on the multi-omics scale between lung adenocarcinoma (LUAD) and squamous cell lung carcinoma (SCLC), the underlying molecular perturbations and their phenotypic impact in patients with the broad spectrum of intellectual disability (ID), the miRNA expression profiles and clinical data of esophageal carcinoma (EC) patients, the environment of Glioblastoma (GBM) tumor revealed by single-cell sequencing, the methylation and gene expression patterns of atrial fibrillation, the latent disease-lncRNA association prediction (FRMCLDA), the Molecular Prognostic Indicators in Cirrhosis (MPIC) database, the probability matrix factorization (PMFMDA) for discovering potential disease-related miRNAs.
With this volume II Research Topic, we aim to build on the progress demonstrated in the first volume. We hope to gather application of novel interpretable classification algorithms in clinical medicine, multi-omics big data integration analysis for genetic diseases, disease gene identification based on network analysis, eQTL associations between SNPs and genes, optimization theory based on targeted therapy for cancer, development of new NGS based tests for genetic diseases, heterogeneous network construction of disease, genes, proteins, and drugs.
We believe that the machine learning methods will be more and more widely used in clinic, help mining the complex biomedical big data and reveal the big value hidden behind the big data.
Author contributions
TH wrote the editorial and all authors have approved it.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Keywords: machine learning, NGS-next generation sequencing, big data, hereditary diseases, cancer
Citation: Cai Y, Jia P and Huang T (2022) Editorial: Finding new epigenomics and epigenetics biomarkers for complex diseases and significant developmental events with machine learning methods, Volume II. Front. Genet. 13:1098821. doi: 10.3389/fgene.2022.1098821
Received: 15 November 2022; Accepted: 24 November 2022;
Published: 30 November 2022.
Edited and reviewed by:
Michael E. Symonds, University of Nottingham, United KingdomCopyright © 2022 Cai, Jia and Huang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Tao Huang, dG9odWFuZ3Rhb0AxMjYuY29t