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Special Issue "Symmetry/Asymmetry in Bioinformatics: Image Understanding and Language Modeling"
A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer Science and Symmetry/Asymmetry".
Deadline for manuscript submissions: 30 June 2023 | Viewed by 1668
Special Issue Editor
Interests: biomedical image understanding; biological language modeling; machine learning and data mining
Special Issue Information
Over the past decade, bioinformatics has been a fast-growing research field due to the rapid development of various high-throughput experimental data, among which sequencing data and microscopic images are two major types. Benefiting from the recent advances in computer vision (CV) and natural language processing (NLP), microscopic image understanding using CNN-based deep learning models and biological sequence representation using language modeling models have emerged in recent years, especially symmetry and asymmetry properties in complex biological systems, having always attracted researchers’ attention, involved in the development of organisms, regulatory processes, molecular interactions, etc. The symmetry/asymmetry property also inspired new models in machine learning and bioinformatics, especially the self-supervised learning for addressing the lack of labels in biological data.
In this Special Issue, we would like to see studies using computational methods to reveal the symmetry/asymmetry properties in biological data, as well as new computational models motivated by symmetry/asymmetry properties. The list of possible topics includes, but is not limited to:
- Machine learning algorithms exploring symmetry/asymmetry in bioinformatics;
- Reviews or surveys in image understanding and language modeling in bioinformatics;
- Deep learning techniques with applications in biological image understanding and DNA/protein sequence representation;
- Latest computational models realizing the symmetry/asymmetry properties, e.g., the Siamese network architecture and contrastive learning framework.
Dr. Yang Yang
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 2000 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.
- symmetry/asymmetry in bioinformatics
- biological image understanding
- deep learning
- biological sequence representation
- language modeling
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: IDRnet: A novel model for modifying and enriching protein subcellular location features based on interactive pairwise pixel-by-pixel
Authors: Kai Zou, Zhihai Zhang, Ziqian Wang, Fan Yang
Affiliation: School of Communications and Electronics, Jiangxi Science and Technology Normal University, Nanchang, China
Title: A review on biological sequence pre-training techniques using deep learning
Authors: Yang Yang，Fu Wang
Affiliation: Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.