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Symmetry
Special Issues
Research on Symmetry in Chemometrics
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Research on Symmetry in Chemometrics

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Chemistry: Symmetry/Asymmetry".

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 3216

Special Issue Editors

Prof. Dr. Zhining Wen

E-Mail Website
Guest Editor
College of Chemistry, Sichuan Unversity, Chengdu, China
Interests: development of new algorithms in chemometrics; genomics data analysis and modeling; drug safety evaluation and database construction
Prof. Dr. Long Jiao

E-Mail Website
Guest Editor
College of Chemistry and Chemical Engineering, Xi'an Shiyou University, Xi'an, China
Interests: analytical chemistry; chemometrics and chemoinformatics
Dr. Jiesi Luo

E-Mail Website
Guest Editor
College of Pharmacy, Southwest Medical University, Luzhou, Sichuan, China
Interests: bioinformatics; chemometrics

Special Issue Information

Dear Colleagues,

In the past few decades, chemometrics has played an important role in analytical chemistry. Combined with a various techniques of instrumental analysis, chemometrics can effectively tackle the qualitative and quantitative problems in complex systems. With the continuous improvement of information technology, it provides a broader space for the development and application of chemometrics. In addition, the rapid development of artificial intelligence (AI) in the fields of chemistry and materials has shown great advantages in the past two years. Under this background, we would like to invite domestic and foreign experts to contribute their researches by employing the symmetry or asymmetry concept in the methods and methodologies, including new algorithms proposed in chemometrics, new software development, and the latest applications of chemometrics methods in chemistry and related disciplines, such as materials, agriculture, pharmacology and precision medicine. Topics related to this issue may include but are not limited to:

  • Development of new algorithms in chemometrics;
  • Best understanding and visualization of deep learning;
  • Multivariate curve resolution modeling in chemistry;
  • Pattern recognition and AI-based methods in chemistry;
  • QSAR modeling in pharmacology and the related fields;
  • AI-based methods for drug design;
  • Rapid qualitative and quantitative analysis for agriculture and food safety;
  • Application of the chemometrics in precision medicine.

Prof. Dr. Zhining Wen
Prof. Dr. Long Jiao
Dr. Jiesi Luo
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. Symmetry 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.

Keywords

  • chemometrics
  • multivariate curve resolution
  • pattern recognition
  • artificial intelligence
  • QSAR
  • drug design
  • agriculture and food safety
  • precision medicine

Published Papers (1 paper)

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Research

20 pages, 46923 KiB  
Article
A Multiple Comprehensive Analysis of scATAC-seq Based on Auto-Encoder and Matrix Decomposition
by Yuyao Huang, Yizhou Li, Yuan Liu, Runyu Jing and Menglong Li
Symmetry 2021, 13(8), 1467; https://0-doi-org.brum.beds.ac.uk/10.3390/sym13081467 - 11 Aug 2021
Cited by 2 | Viewed by 2720
Abstract
Single-cell ATAC-seq (scATAC-seq), as the updating of ATAC-seq, provides a novel method for probing open chromatin sites. Currently, research of scATAC-seq is faced with the problem of high dimensionality and the inherent sparsity of the generated data. Recently, several works proposed the use [...] Read more.
Single-cell ATAC-seq (scATAC-seq), as the updating of ATAC-seq, provides a novel method for probing open chromatin sites. Currently, research of scATAC-seq is faced with the problem of high dimensionality and the inherent sparsity of the generated data. Recently, several works proposed the use of an autoencoder–decoder, a symmetry neural network architecture, and non-negative matrix factorization methods to characterize the high-dimensional data. To evaluate the performance of multiple methods, in this work, we performed a multiple comparison for characterizing scATAC-seq based on four kinds of auto-encoders known as a symmetry neural network, and two kinds of matrix factorization methods. Different sizes of latent features were used to generate the UMAP plots and for further K-means clustering. Using a gold-standard data set, we practically explored the performance among the methods and the number of latent features in a comprehensive way. Finally, we briefly discuss the underlying difficulties and future directions for scATAC-seq characterizing. As a result, the method designed for handling the sparsity outperforms other tools in the generated dataset. Full article
(This article belongs to the Special Issue Research on Symmetry in Chemometrics)
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