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".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 3005

Special Issue Editor

Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: biomedical image understanding; biological language modeling; machine learning and data mining

Special Issue Information

Dear Colleagues,

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
Guest Editor

Manuscript Submission Information

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Keywords

  • symmetry/asymmetry in bioinformatics
  • biological image understanding
  • deep learning
  • biological sequence representation
  • language modeling

Published Papers (2 papers)

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Research

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15 pages, 5336 KiB  
Article
M6A-BERT-Stacking: A Tissue-Specific Predictor for Identifying RNA N6-Methyladenosine Sites Based on BERT and Stacking Strategy
by Qianyue Li, Xin Cheng, Chen Song and Taigang Liu
Symmetry 2023, 15(3), 731; https://doi.org/10.3390/sym15030731 - 15 Mar 2023
Cited by 5 | Viewed by 2184
Abstract
As the most abundant RNA methylation modification, N6-methyladenosine (m6A) could regulate asymmetric and symmetric division of hematopoietic stem cells and play an important role in various diseases. Therefore, the precise identification of m6A sites around the genomes of different species is a critical [...] Read more.
As the most abundant RNA methylation modification, N6-methyladenosine (m6A) could regulate asymmetric and symmetric division of hematopoietic stem cells and play an important role in various diseases. Therefore, the precise identification of m6A sites around the genomes of different species is a critical step to further revealing their biological functions and influence on these diseases. However, the traditional wet-lab experimental methods for identifying m6A sites are often laborious and expensive. In this study, we proposed an ensemble deep learning model called m6A-BERT-Stacking, a powerful predictor for the detection of m6A sites in various tissues of three species. First, we utilized two encoding methods, i.e., di ribonucleotide index of RNA (DiNUCindex_RNA) and k-mer word segmentation, to extract RNA sequence features. Second, two encoding matrices together with the original sequences were respectively input into three different deep learning models in parallel to train three sub-models, namely residual networks with convolutional block attention module (Resnet-CBAM), bidirectional long short-term memory with attention (BiLSTM-Attention), and pre-trained bidirectional encoder representations from transformers model for DNA-language (DNABERT). Finally, the outputs of all sub-models were ensembled based on the stacking strategy to obtain the final prediction of m6A sites through the fully connected layer. The experimental results demonstrated that m6A-BERT-Stacking outperformed most of the existing methods based on the same independent datasets. Full article
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Review

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14 pages, 1788 KiB  
Review
Recent Deep Learning Methodology Development for RNA–RNA Interaction Prediction
by Yi Fang, Xiaoyong Pan and Hong-Bin Shen
Symmetry 2022, 14(7), 1302; https://doi.org/10.3390/sym14071302 - 23 Jun 2022
Cited by 4 | Viewed by 2537
Abstract
Genetic regulation of organisms involves complicated RNA–RNA interactions (RRIs) among messenger RNA (mRNA), microRNA (miRNA), and long non-coding RNA (lncRNA). Detecting RRIs is beneficial for discovering biological mechanisms as well as designing new drugs. In recent years, with more and more experimentally verified [...] Read more.
Genetic regulation of organisms involves complicated RNA–RNA interactions (RRIs) among messenger RNA (mRNA), microRNA (miRNA), and long non-coding RNA (lncRNA). Detecting RRIs is beneficial for discovering biological mechanisms as well as designing new drugs. In recent years, with more and more experimentally verified RNA–RNA interactions being deposited into databases, statistical machine learning, especially recent deep-learning-based automatic algorithms, have been widely applied to RRI prediction with remarkable success. This paper first gives a brief introduction to the traditional machine learning methods applied on RRI prediction and benchmark databases for training the models, and then provides a recent methodology overview of deep learning models in the prediction of microRNA (miRNA)–mRNA interactions and long non-coding RNA (lncRNA)–miRNA interactions. Full article
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