ArticlePLoS computational biology2023
BioSeq-Diabolo: Biological sequence similarity analysis using Diabolo.
Article in PLoS computational biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers.
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Who cites it
53 citing papers in PubMed.
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- TF-loop: deciphering the transcription factor regulatory language for CTCF-mediated chromatin loop based on BERT.Briefings in bioinformatics · 2026Article
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- Bioinformatics insights into plant genomic imprinting: approaches, challenges, and future perspectives.Briefings in functional genomics · 2026Review
- BiGvCL: bipartite graph-based cross-domain contrastive learning model for the predicting drug-gene interactions.Briefings in bioinformatics · 2026Article
- Biological sequence analysis: Advances, medical applications, and challenges.Fundamental research · 2026Review
- ProjFusNet: deep neural network for peptide precursor prediction using projection-fused protein language model and structural features.Journal of cheminformatics · 2025Article
- PCBert-Kla: an efficient prediction method for lysine lactylation sites based on ProtBert and fusion of physicochemical features.Briefings in bioinformatics · 2025Article
- iPro-MP: a BERT-based model to predict multiple prokaryotic promoters.Genome biology · 2025Article
- HyperACP: A cutting-edge hybrid framework for anticancer peptide classification via scalable feature extraction and adaptive neighbor-based synthesis.PLoS computational biology · 2025Article
- Integration of pre-trained protein language models with equivariant graph neural networks for peptide toxicity prediction.BMC biology · 2025Article
- An artificial intelligence-based approach for identifying the proteins regulating liquid-liquid phase separation.Briefings in bioinformatics · 2025Article
- Classification of Acid and Alkaline Enzymes Based on Normalized Van der Waals Volume Features.Proteomics. Clinical applications · 2025Article
- AOPxSVM: A Support Vector Machine for Identifying Antioxidant Peptides Using a Block Substitution Matrix and Amino Acid Composition, Transformation, and Distribution Embeddings.Foods (Basel, Switzerland) · 2025Article
- DGCLCMI: a deep graph collaboration learning method to predict circRNA-miRNA interactions.BMC biology · 2025Article
- Multiscale graph equivariant diffusion model for 3D molecule design.Science advances · 2025Article
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2 authors.
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Abstract
As the key for biological sequence structure and function prediction, disease diagnosis and treatment, biological sequence similarity analysis has attracted more and more attentions. However, the exiting computational methods failed to accurately analyse the biological sequence similarities because of the various data types (DNA, RNA, protein, disease, etc) and their low sequence similarities (remote homology). Therefore, new concepts and techniques are desired to solve this challenging problem. Biological sequences (DNA, RNA and protein sequences) can be considered as the sentences of "the book of life", and their similarities can be considered as the biological language semantics (BLS). In this study, we are seeking the semantics analysis techniques derived from the natural language processing (NLP) to comprehensively and accurately analyse the biological sequence similarities. 27 semantics analysis methods derived from NLP were introduced to analyse biological sequence similarities, bringing new concepts and techniques to biological sequence similarity analysis. Experimental results show that these semantics analysis methods are able to facilitate the development of protein remote homology detection, circRNA-disease associations identification and protein function annotation, achieving better performance than the other state-of-the-art predictors in the related fields. Based on these semantics analysis methods, a platform called BioSeq-Diabolo has been constructed, which is named after a popular traditional sport in China. The users only need to input the embeddings of the biological sequence data. BioSeq-Diabolo will intelligently identify the task, and then accurately analyse the biological sequence similarities based on biological language semantics. BioSeq-Diabolo will integrate different biological sequence similarities in a supervised manner by using Learning to Rank (LTR), and the performance of the constructed methods will be evaluated and analysed so as to recommend the best methods for the users. The web server and stand-alone package of BioSeq-Diabolo can be accessed at http://bliulab.net/BioSeq-Diabolo/server/.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.